Hospital Medicine

A duodenocaval fistula (DCF) is seen when a connection exists between the duodenum and the inferior vena cava. It is a rare entity that is commonly missed and presents a diagnostic challenge due to its nonspecific presenting symptoms.^1,2 Patients commonly present with gastrointestinal (GI) bleeding or sepsis. Here we present a case of a 37-year-old man who presented to the hospital for a workup related to melena but went into cardiac arrest prior to an esophagogastroduodenoscopy. Unfortunately, on autopsy, the patient was found to have a DCF. We highlight the diagnostic challenge associated with DCF and how in this case the presentation was confused by a diagnosis of possible transfusion-related acute lung injury (TRALI). To the best of our knowledge, this is also the first description of a case of DCF associated with food embolism to the lungs causing respiratory failure.

Case Presentation

A 37-year-old man with a history significant for bulimia presented to the hospital with a 3-day history of melena and reports of dizziness. The patient did not report being on any prescribed medications but noted that he took 4 aspirin daily to “calm his nerves.” The rest of the patient’s history was unremarkable aside from a reported history of induced emesis 3 to 4 times per week for an extended period up until 2 weeks before admission.

On admission, his vital signs demonstrated tachycardia and orthostatic hypotension. Pertinent findings on physical examination were skin pallor, a normal lung examination, mild epigastric tenderness, and guaiac-positive stools. He was alert and oriented to person, place, and time with no focal deficits. His admission laboratory tests were notable for a hemoglobin (Hb) level of 4.6 g/dL (reference range, 14-17.9), a white blood cell count of 13.5 K/cm (reference range, 4.5-11), an international normalized ratio of 1.21, a blood urea nitrogen of 61 mg/dL (reference range, 10-20), and a creatinine of 2.3 mg/dL (reference range, 0.8-1.4). The patient was placed on 2 L of oxygen via nasal cannula for comfort rather than true hypoxia. A chest X-ray on admission was negative with no signs of infiltrate, edema, or widened mediastinum. An abdominal X-ray was significant for a dilated stomach consistent with bulimia with no abdominal free air or signs of obstruction. The case was discussed with the gastroenterology service who felt that the patient needed to be more hemodynamically stable before pursuing endoscopic evaluation.

He was admitted to the intensive care unit and give a transfusion of 4 units of fresh frozen plasma and 2 packed red blood cells (PRBCs) without any issues. During the infusion of a third PRBC, he developed chills, tachycardia, and hypertension with accompanying respiratory distress characterized by wheezing, decreased breath sounds bilaterally, and a decrease in oxygen saturation to 70% on 2 L supplemental oxygen. He responded to treatment with meperidine, methylprednisolone sodium succinate, albuterol nebulizer, and acetaminophen. A new chest X-ray was read as “development of pulmonary edema vs bilateral pneumonitis.” A transfusion reaction was reported to the blood bank and a diagnosis of TRALI was considered. That evening, he completed a dose of platelets and another PRBC without difficulty after he was premedicated with meperidine, methylprednisolone sodium succinate, and acetaminophen. During the night, the patient spiked a temperature of 40.3 °C that was successfully treated with a cooling blanket and acetaminophen.

The following morning the patient was found to be tachypneic and tachycardic with his face mask off. His symptoms were corrected by replacing his face mask. He claimed he felt anxious about getting more transfusions and that he had breathing problems like this at home in the recent past. The patient requested an aspirin to calm his nerves. Over the course of the day, his Hb level dropped from 6.6 g/dL to 5.9 g/dL, and 2 washed leukopoor PRBCs were ordered.

The first unit was infused uneventfully, but after 125 cc of the second unit, the patient developed respiratory distress, rigors, and hypotension to 70/58 mm Hg despite premedication. He again was treated successfully with increased face mask support. A few rales were noted, but his fluid balance was even. A second transfusion reaction was filed with the blood bank and based on the 2 transfusion-associated events with no other clear explanation for his symptoms, the clinical team favored the TRALI diagnosis. However, the blood bank was suspicious this might not be TRALI as the previous night the patient had 2 episodes of respiratory distress with drops in oxygen saturation unassociated with transfusions. The patient was clinically stable for the remainder of the night.

Early the following morning the patient was scheduled for an esophagogastroduodenoscopy to evaluate for a source of his bleeding. At the beginning of the procedure, a unit of washed leukoreduced PRBCs was hung for a Hb level of 6.9 g/dL. No bleeding source was noted in the stomach, but as the endoscope was passed into the duodenum, and after an infusion of only 25 cc of RBCs, the patient became cyanotic and went into cardiac arrest. Despite advanced resuscitation efforts over 90 minutes, the patient could not be successfully resuscitated and died while in the endoscopy suite. A transfusion reaction workup was initiated but was unremarkable. The transfusion medicine staff was suspicious that something other than TRALI was the cause of the patient’s respiratory distress as he had respiratory distress remote to the transfusions and the unit was prepared correctly before administration. The patient’s family agreed to an autopsy.

Pathology

A full autopsy was performed 22 hours after the patient died. The lungs were congested and of increased weight: The right lung was 800 g, and the left was 750 g. The right lower lobe had a wedge-shaped infarction measuring 6 cm × 5 cm fed by a thrombosed vessel. Multiple small hemorrhagic wedge-shaped areas were noted in the left lung. An ulcer measuring 6 cm × 5 cm was noted just distal to the pylorus. At the base of this ulcer was a 1.5 cm × 0.5 cm tract that communicated with the inferior vena cava (Figure 1).

A postmortem blood culture was positive for Clostridium perfringens (C perfringens) and Candida albicans (C Albicans). Interestingly, one of the collected blood culture vials exploded en route to the laboratory, presumably due to the presence of many gas-forming C perfringens bacteria.

On microscopic examination of the autopsy samples, gram-positive rods were observed in the tissue of multiple organs, including the heart, lungs, liver, and kidneys (Figure 2).

Serology

Fourteen days after the patient’s death, both PRBC units infused during transfusion reactions were positive for granulocyte antibodies by immunofluorescence and agglutination techniques. Human leukocyte antigen antibody testing was also sent but was not found in either the donor or patient.

Discussion

Our case illustrates the unique and challenging diagnosis of DCF given the rarity of presentation and how quickly patients may clinically decompensate. After an extensive search of the medical literature, we were only able to identify about 40 previous cases of DCF, of which 37 were described in one review.¹ DCF, although rare, should be considered at risk for forming in the following settings: migrating inferior vena cava filter, right nephrectomy and radiotherapy, duodenal peptic ulcer, abdominal trauma, and oncologic settings involving metastatic malignancy requiring radiation and/or surgical grafting of the inferior vena cava.^1-4 When the diagnosis is considered, computed tomography (CT) is the best initial imaging modality as it allows for noninvasive evaluation of both the inferior vena cava and nonadjacent structures. A commonality of our case and those described in the literature is the diagnostic mystery and nonspecific symptoms patients present with, thus making CT an appropriate diagnostic modality. Endoscopy is useful for the further workup of GI bleeding and the diagnosis of peptic ulcer disease.⁵ In our case, given the patient’s autopsy findings and history of extensive nonsteroidal anti-inflammatory drug use, the duodenal peptic ulcer was likely the precipitating factor for his DCF.

The most challenging aspect in diagnosing DCF is that many times patients present with nonspecific symptoms, and given its rarity it is not something that is usually at the forefront of most differentials.² This diagnostic difficulty may elucidate why there is such a relatively high mortality rate—nearly 40%—associated with DCF and why many times accurate diagnosis is not made until autopsy.^1,3 The most common presenting manifestations are sepsis and/or GI bleeding; in less than half the cases described in the literature patients had both sepsis and GI bleeding. In our case, the patient had signs of melena but was not felt to be septic as his presenting signs were felt to be in the setting of blood loss and dehydration (given his history of bulimia), not an acute infectious source.

In retrospect, one of the more confounding aspects of this case is the clinical picture concerning for TRALI. The patient required supplemental oxygen throughout his hospitalization and decompensated while or after receiving a transfusion, thus having TRALI on the differential was not felt inappropriate at that time. However, this case also illustrates the power of an anchoring bias, and perhaps the clinical team anchored on the diagnosis of TRALI too quickly before considering other possible etiologies for the patient’s respiratory distress. TRALI can be one of the most challenging diagnoses to make in the field of transfusion medicine as there are no definitive diagnostic criteria.⁶ It is felt to be a clinical diagnosis of exclusion as there is no pathognomonic sign or diagnostic test to confirm it as the cause of the patient’s respiratory distress, though anti–human leukocyte antigen antibodies commonly are present.^6,7 Considering how quickly the patient decompensated on day 2 of hospitalization and the presence of C perfringens bacteremia, which carries a mortality rate of 27% to 44%, it is likely that further diagnostic workup would not have changed the clinical outcome.⁸

Conclusions

Our investigation reports a case of a DCF in the setting of significant duodenal peptic ulcer disease. We highlight the diagnostic challenge that this commonly lethal etiology presents. We believe ours is the first case in which it was confused for TRALI and associated with food embolism to the lungs causing hypoxic respiratory failure. We want to highlight that DCF, though rare, should be considered for patients who present with GI bleeding and hypoxic respiratory failure.

A duodenocaval fistula (DCF) is seen when a connection exists between the duodenum and the inferior vena cava. It is a rare entity that is commonly missed and presents a diagnostic challenge due to its nonspecific presenting symptoms.^1,2 Patients commonly present with gastrointestinal (GI) bleeding or sepsis. Here we present a case of a 37-year-old man who presented to the hospital for a workup related to melena but went into cardiac arrest prior to an esophagogastroduodenoscopy. Unfortunately, on autopsy, the patient was found to have a DCF. We highlight the diagnostic challenge associated with DCF and how in this case the presentation was confused by a diagnosis of possible transfusion-related acute lung injury (TRALI). To the best of our knowledge, this is also the first description of a case of DCF associated with food embolism to the lungs causing respiratory failure.

Case Presentation

A 37-year-old man with a history significant for bulimia presented to the hospital with a 3-day history of melena and reports of dizziness. The patient did not report being on any prescribed medications but noted that he took 4 aspirin daily to “calm his nerves.” The rest of the patient’s history was unremarkable aside from a reported history of induced emesis 3 to 4 times per week for an extended period up until 2 weeks before admission.

On admission, his vital signs demonstrated tachycardia and orthostatic hypotension. Pertinent findings on physical examination were skin pallor, a normal lung examination, mild epigastric tenderness, and guaiac-positive stools. He was alert and oriented to person, place, and time with no focal deficits. His admission laboratory tests were notable for a hemoglobin (Hb) level of 4.6 g/dL (reference range, 14-17.9), a white blood cell count of 13.5 K/cm (reference range, 4.5-11), an international normalized ratio of 1.21, a blood urea nitrogen of 61 mg/dL (reference range, 10-20), and a creatinine of 2.3 mg/dL (reference range, 0.8-1.4). The patient was placed on 2 L of oxygen via nasal cannula for comfort rather than true hypoxia. A chest X-ray on admission was negative with no signs of infiltrate, edema, or widened mediastinum. An abdominal X-ray was significant for a dilated stomach consistent with bulimia with no abdominal free air or signs of obstruction. The case was discussed with the gastroenterology service who felt that the patient needed to be more hemodynamically stable before pursuing endoscopic evaluation.

He was admitted to the intensive care unit and give a transfusion of 4 units of fresh frozen plasma and 2 packed red blood cells (PRBCs) without any issues. During the infusion of a third PRBC, he developed chills, tachycardia, and hypertension with accompanying respiratory distress characterized by wheezing, decreased breath sounds bilaterally, and a decrease in oxygen saturation to 70% on 2 L supplemental oxygen. He responded to treatment with meperidine, methylprednisolone sodium succinate, albuterol nebulizer, and acetaminophen. A new chest X-ray was read as “development of pulmonary edema vs bilateral pneumonitis.” A transfusion reaction was reported to the blood bank and a diagnosis of TRALI was considered. That evening, he completed a dose of platelets and another PRBC without difficulty after he was premedicated with meperidine, methylprednisolone sodium succinate, and acetaminophen. During the night, the patient spiked a temperature of 40.3 °C that was successfully treated with a cooling blanket and acetaminophen.

The following morning the patient was found to be tachypneic and tachycardic with his face mask off. His symptoms were corrected by replacing his face mask. He claimed he felt anxious about getting more transfusions and that he had breathing problems like this at home in the recent past. The patient requested an aspirin to calm his nerves. Over the course of the day, his Hb level dropped from 6.6 g/dL to 5.9 g/dL, and 2 washed leukopoor PRBCs were ordered.

The first unit was infused uneventfully, but after 125 cc of the second unit, the patient developed respiratory distress, rigors, and hypotension to 70/58 mm Hg despite premedication. He again was treated successfully with increased face mask support. A few rales were noted, but his fluid balance was even. A second transfusion reaction was filed with the blood bank and based on the 2 transfusion-associated events with no other clear explanation for his symptoms, the clinical team favored the TRALI diagnosis. However, the blood bank was suspicious this might not be TRALI as the previous night the patient had 2 episodes of respiratory distress with drops in oxygen saturation unassociated with transfusions. The patient was clinically stable for the remainder of the night.

Early the following morning the patient was scheduled for an esophagogastroduodenoscopy to evaluate for a source of his bleeding. At the beginning of the procedure, a unit of washed leukoreduced PRBCs was hung for a Hb level of 6.9 g/dL. No bleeding source was noted in the stomach, but as the endoscope was passed into the duodenum, and after an infusion of only 25 cc of RBCs, the patient became cyanotic and went into cardiac arrest. Despite advanced resuscitation efforts over 90 minutes, the patient could not be successfully resuscitated and died while in the endoscopy suite. A transfusion reaction workup was initiated but was unremarkable. The transfusion medicine staff was suspicious that something other than TRALI was the cause of the patient’s respiratory distress as he had respiratory distress remote to the transfusions and the unit was prepared correctly before administration. The patient’s family agreed to an autopsy.

Pathology

A full autopsy was performed 22 hours after the patient died. The lungs were congested and of increased weight: The right lung was 800 g, and the left was 750 g. The right lower lobe had a wedge-shaped infarction measuring 6 cm × 5 cm fed by a thrombosed vessel. Multiple small hemorrhagic wedge-shaped areas were noted in the left lung. An ulcer measuring 6 cm × 5 cm was noted just distal to the pylorus. At the base of this ulcer was a 1.5 cm × 0.5 cm tract that communicated with the inferior vena cava (Figure 1).

A postmortem blood culture was positive for Clostridium perfringens (C perfringens) and Candida albicans (C Albicans). Interestingly, one of the collected blood culture vials exploded en route to the laboratory, presumably due to the presence of many gas-forming C perfringens bacteria.

On microscopic examination of the autopsy samples, gram-positive rods were observed in the tissue of multiple organs, including the heart, lungs, liver, and kidneys (Figure 2).

Serology

Fourteen days after the patient’s death, both PRBC units infused during transfusion reactions were positive for granulocyte antibodies by immunofluorescence and agglutination techniques. Human leukocyte antigen antibody testing was also sent but was not found in either the donor or patient.

Discussion

Our case illustrates the unique and challenging diagnosis of DCF given the rarity of presentation and how quickly patients may clinically decompensate. After an extensive search of the medical literature, we were only able to identify about 40 previous cases of DCF, of which 37 were described in one review.¹ DCF, although rare, should be considered at risk for forming in the following settings: migrating inferior vena cava filter, right nephrectomy and radiotherapy, duodenal peptic ulcer, abdominal trauma, and oncologic settings involving metastatic malignancy requiring radiation and/or surgical grafting of the inferior vena cava.^1-4 When the diagnosis is considered, computed tomography (CT) is the best initial imaging modality as it allows for noninvasive evaluation of both the inferior vena cava and nonadjacent structures. A commonality of our case and those described in the literature is the diagnostic mystery and nonspecific symptoms patients present with, thus making CT an appropriate diagnostic modality. Endoscopy is useful for the further workup of GI bleeding and the diagnosis of peptic ulcer disease.⁵ In our case, given the patient’s autopsy findings and history of extensive nonsteroidal anti-inflammatory drug use, the duodenal peptic ulcer was likely the precipitating factor for his DCF.

The most challenging aspect in diagnosing DCF is that many times patients present with nonspecific symptoms, and given its rarity it is not something that is usually at the forefront of most differentials.² This diagnostic difficulty may elucidate why there is such a relatively high mortality rate—nearly 40%—associated with DCF and why many times accurate diagnosis is not made until autopsy.^1,3 The most common presenting manifestations are sepsis and/or GI bleeding; in less than half the cases described in the literature patients had both sepsis and GI bleeding. In our case, the patient had signs of melena but was not felt to be septic as his presenting signs were felt to be in the setting of blood loss and dehydration (given his history of bulimia), not an acute infectious source.

In retrospect, one of the more confounding aspects of this case is the clinical picture concerning for TRALI. The patient required supplemental oxygen throughout his hospitalization and decompensated while or after receiving a transfusion, thus having TRALI on the differential was not felt inappropriate at that time. However, this case also illustrates the power of an anchoring bias, and perhaps the clinical team anchored on the diagnosis of TRALI too quickly before considering other possible etiologies for the patient’s respiratory distress. TRALI can be one of the most challenging diagnoses to make in the field of transfusion medicine as there are no definitive diagnostic criteria.⁶ It is felt to be a clinical diagnosis of exclusion as there is no pathognomonic sign or diagnostic test to confirm it as the cause of the patient’s respiratory distress, though anti–human leukocyte antigen antibodies commonly are present.^6,7 Considering how quickly the patient decompensated on day 2 of hospitalization and the presence of C perfringens bacteremia, which carries a mortality rate of 27% to 44%, it is likely that further diagnostic workup would not have changed the clinical outcome.⁸

Conclusions

Our investigation reports a case of a DCF in the setting of significant duodenal peptic ulcer disease. We highlight the diagnostic challenge that this commonly lethal etiology presents. We believe ours is the first case in which it was confused for TRALI and associated with food embolism to the lungs causing hypoxic respiratory failure. We want to highlight that DCF, though rare, should be considered for patients who present with GI bleeding and hypoxic respiratory failure.

A duodenocaval fistula (DCF) is seen when a connection exists between the duodenum and the inferior vena cava. It is a rare entity that is commonly missed and presents a diagnostic challenge due to its nonspecific presenting symptoms.^1,2 Patients commonly present with gastrointestinal (GI) bleeding or sepsis. Here we present a case of a 37-year-old man who presented to the hospital for a workup related to melena but went into cardiac arrest prior to an esophagogastroduodenoscopy. Unfortunately, on autopsy, the patient was found to have a DCF. We highlight the diagnostic challenge associated with DCF and how in this case the presentation was confused by a diagnosis of possible transfusion-related acute lung injury (TRALI). To the best of our knowledge, this is also the first description of a case of DCF associated with food embolism to the lungs causing respiratory failure.

Case Presentation

A 37-year-old man with a history significant for bulimia presented to the hospital with a 3-day history of melena and reports of dizziness. The patient did not report being on any prescribed medications but noted that he took 4 aspirin daily to “calm his nerves.” The rest of the patient’s history was unremarkable aside from a reported history of induced emesis 3 to 4 times per week for an extended period up until 2 weeks before admission.

On admission, his vital signs demonstrated tachycardia and orthostatic hypotension. Pertinent findings on physical examination were skin pallor, a normal lung examination, mild epigastric tenderness, and guaiac-positive stools. He was alert and oriented to person, place, and time with no focal deficits. His admission laboratory tests were notable for a hemoglobin (Hb) level of 4.6 g/dL (reference range, 14-17.9), a white blood cell count of 13.5 K/cm (reference range, 4.5-11), an international normalized ratio of 1.21, a blood urea nitrogen of 61 mg/dL (reference range, 10-20), and a creatinine of 2.3 mg/dL (reference range, 0.8-1.4). The patient was placed on 2 L of oxygen via nasal cannula for comfort rather than true hypoxia. A chest X-ray on admission was negative with no signs of infiltrate, edema, or widened mediastinum. An abdominal X-ray was significant for a dilated stomach consistent with bulimia with no abdominal free air or signs of obstruction. The case was discussed with the gastroenterology service who felt that the patient needed to be more hemodynamically stable before pursuing endoscopic evaluation.

He was admitted to the intensive care unit and give a transfusion of 4 units of fresh frozen plasma and 2 packed red blood cells (PRBCs) without any issues. During the infusion of a third PRBC, he developed chills, tachycardia, and hypertension with accompanying respiratory distress characterized by wheezing, decreased breath sounds bilaterally, and a decrease in oxygen saturation to 70% on 2 L supplemental oxygen. He responded to treatment with meperidine, methylprednisolone sodium succinate, albuterol nebulizer, and acetaminophen. A new chest X-ray was read as “development of pulmonary edema vs bilateral pneumonitis.” A transfusion reaction was reported to the blood bank and a diagnosis of TRALI was considered. That evening, he completed a dose of platelets and another PRBC without difficulty after he was premedicated with meperidine, methylprednisolone sodium succinate, and acetaminophen. During the night, the patient spiked a temperature of 40.3 °C that was successfully treated with a cooling blanket and acetaminophen.

The following morning the patient was found to be tachypneic and tachycardic with his face mask off. His symptoms were corrected by replacing his face mask. He claimed he felt anxious about getting more transfusions and that he had breathing problems like this at home in the recent past. The patient requested an aspirin to calm his nerves. Over the course of the day, his Hb level dropped from 6.6 g/dL to 5.9 g/dL, and 2 washed leukopoor PRBCs were ordered.

The first unit was infused uneventfully, but after 125 cc of the second unit, the patient developed respiratory distress, rigors, and hypotension to 70/58 mm Hg despite premedication. He again was treated successfully with increased face mask support. A few rales were noted, but his fluid balance was even. A second transfusion reaction was filed with the blood bank and based on the 2 transfusion-associated events with no other clear explanation for his symptoms, the clinical team favored the TRALI diagnosis. However, the blood bank was suspicious this might not be TRALI as the previous night the patient had 2 episodes of respiratory distress with drops in oxygen saturation unassociated with transfusions. The patient was clinically stable for the remainder of the night.

Early the following morning the patient was scheduled for an esophagogastroduodenoscopy to evaluate for a source of his bleeding. At the beginning of the procedure, a unit of washed leukoreduced PRBCs was hung for a Hb level of 6.9 g/dL. No bleeding source was noted in the stomach, but as the endoscope was passed into the duodenum, and after an infusion of only 25 cc of RBCs, the patient became cyanotic and went into cardiac arrest. Despite advanced resuscitation efforts over 90 minutes, the patient could not be successfully resuscitated and died while in the endoscopy suite. A transfusion reaction workup was initiated but was unremarkable. The transfusion medicine staff was suspicious that something other than TRALI was the cause of the patient’s respiratory distress as he had respiratory distress remote to the transfusions and the unit was prepared correctly before administration. The patient’s family agreed to an autopsy.

Pathology

A full autopsy was performed 22 hours after the patient died. The lungs were congested and of increased weight: The right lung was 800 g, and the left was 750 g. The right lower lobe had a wedge-shaped infarction measuring 6 cm × 5 cm fed by a thrombosed vessel. Multiple small hemorrhagic wedge-shaped areas were noted in the left lung. An ulcer measuring 6 cm × 5 cm was noted just distal to the pylorus. At the base of this ulcer was a 1.5 cm × 0.5 cm tract that communicated with the inferior vena cava (Figure 1).

A postmortem blood culture was positive for Clostridium perfringens (C perfringens) and Candida albicans (C Albicans). Interestingly, one of the collected blood culture vials exploded en route to the laboratory, presumably due to the presence of many gas-forming C perfringens bacteria.

On microscopic examination of the autopsy samples, gram-positive rods were observed in the tissue of multiple organs, including the heart, lungs, liver, and kidneys (Figure 2).

Serology

Fourteen days after the patient’s death, both PRBC units infused during transfusion reactions were positive for granulocyte antibodies by immunofluorescence and agglutination techniques. Human leukocyte antigen antibody testing was also sent but was not found in either the donor or patient.

Discussion

Our case illustrates the unique and challenging diagnosis of DCF given the rarity of presentation and how quickly patients may clinically decompensate. After an extensive search of the medical literature, we were only able to identify about 40 previous cases of DCF, of which 37 were described in one review.¹ DCF, although rare, should be considered at risk for forming in the following settings: migrating inferior vena cava filter, right nephrectomy and radiotherapy, duodenal peptic ulcer, abdominal trauma, and oncologic settings involving metastatic malignancy requiring radiation and/or surgical grafting of the inferior vena cava.^1-4 When the diagnosis is considered, computed tomography (CT) is the best initial imaging modality as it allows for noninvasive evaluation of both the inferior vena cava and nonadjacent structures. A commonality of our case and those described in the literature is the diagnostic mystery and nonspecific symptoms patients present with, thus making CT an appropriate diagnostic modality. Endoscopy is useful for the further workup of GI bleeding and the diagnosis of peptic ulcer disease.⁵ In our case, given the patient’s autopsy findings and history of extensive nonsteroidal anti-inflammatory drug use, the duodenal peptic ulcer was likely the precipitating factor for his DCF.

The most challenging aspect in diagnosing DCF is that many times patients present with nonspecific symptoms, and given its rarity it is not something that is usually at the forefront of most differentials.² This diagnostic difficulty may elucidate why there is such a relatively high mortality rate—nearly 40%—associated with DCF and why many times accurate diagnosis is not made until autopsy.^1,3 The most common presenting manifestations are sepsis and/or GI bleeding; in less than half the cases described in the literature patients had both sepsis and GI bleeding. In our case, the patient had signs of melena but was not felt to be septic as his presenting signs were felt to be in the setting of blood loss and dehydration (given his history of bulimia), not an acute infectious source.

In retrospect, one of the more confounding aspects of this case is the clinical picture concerning for TRALI. The patient required supplemental oxygen throughout his hospitalization and decompensated while or after receiving a transfusion, thus having TRALI on the differential was not felt inappropriate at that time. However, this case also illustrates the power of an anchoring bias, and perhaps the clinical team anchored on the diagnosis of TRALI too quickly before considering other possible etiologies for the patient’s respiratory distress. TRALI can be one of the most challenging diagnoses to make in the field of transfusion medicine as there are no definitive diagnostic criteria.⁶ It is felt to be a clinical diagnosis of exclusion as there is no pathognomonic sign or diagnostic test to confirm it as the cause of the patient’s respiratory distress, though anti–human leukocyte antigen antibodies commonly are present.^6,7 Considering how quickly the patient decompensated on day 2 of hospitalization and the presence of C perfringens bacteremia, which carries a mortality rate of 27% to 44%, it is likely that further diagnostic workup would not have changed the clinical outcome.⁸

Conclusions

Our investigation reports a case of a DCF in the setting of significant duodenal peptic ulcer disease. We highlight the diagnostic challenge that this commonly lethal etiology presents. We believe ours is the first case in which it was confused for TRALI and associated with food embolism to the lungs causing hypoxic respiratory failure. We want to highlight that DCF, though rare, should be considered for patients who present with GI bleeding and hypoxic respiratory failure.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)^1-3 and total inpatient cost,¹ as well as increased inpatient mortality, higher left-without-being-seen rates,⁴ delays in clinically meaningful care,^5,6 and poor patient and clinician satisfaction.^6,7 While various solutions have been proposed to alleviate ED crowding,⁸ excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”⁹ Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.⁹ Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.¹⁰ No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.¹¹ Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.⁹ A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al¹² reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY