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Division of Hospital Medicine, Department of Medicine, Duke University Medical Center
Given name(s)
Noppon
Family name
Setji
Degrees
MD

Use of the dual-antiplatelet therapy score to guide treatment duration after percutaneous coronary intervention

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Clinical question: Can the dual-antiplatelet therapy scoring system be used to determine which patients undergoing percutaneous coronary intervention (PCI) would benefit from prolonged (24 months) DAPT?

Background: Prolonged DAPT therapy has been estimated to prevent 8 myocardial infarctions per 1,000 persons treated for 1 year but at the cost of 6 major bleeding events with no clear mortality benefit. Given these trade-offs, the DAPT score could be used to identify patients who would benefit or would be harmed from prolonged DAPT. The safety and efficacy of DAPT duration as guided by the DAPT score has not been assessed outside the derivation cohort. This study applied the DAPT score to the PRODIGY trial patients to evaluate safety and outcomes of DAPT for 24 months versus a less than 6-month regimen.

Study design: Retrospective use of the DAPT score in PRODIGY patients.

Setting: PCI patients in PRODIGY trial.

Synopsis: In the original derivation cohort, a low DAPT score of less than 2 identified patients whose bleeding risks outweigh ischemic benefits and a high score above 2 identifies patients for whom ischemic benefits outweigh bleeding risks. When the DAPT score was applied to the 1,970 patients enrolled in PRODIGY, 55% had a low score and 45% had a high score. The primary efficacy outcomes of death, MI, and stroke were evaluated as well as primary safety outcomes of bleeding according to the Bleeding Academic Research Consortium definition. The reduction in the primary efficacy outcomes with 24-month vs. 6-month DAPT was greater in patients with a high DAPT score but only in the older paclitaxel-eluting stents. Since these stents have mostly fallen out of favor, there are some limitations to the applicability of the study findings. The study also provides support for 6 months of DAPT for patients with a DAPT score of less than 2.

Bottom line: For patients who underwent PCI with a DAPT score of less than 2, the risk for bleeding appears to be higher than are the ischemic benefits, while patients who had a high DAPT score of greater than 2 with a first-generation stent, the ischemic benefits of prolonged DAPT seemed to outweigh the bleeding risks.

Citation: Piccolo R et al. Use of the dual-antiplatelet therapy score to guide treatment duration after percutaneous coronary intervention. Ann Intern Med. 2017 Jul 4;167(1):17-25

Dr. Setji is a hospitalist and medical director, Duke University Hospital.

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Clinical question: Can the dual-antiplatelet therapy scoring system be used to determine which patients undergoing percutaneous coronary intervention (PCI) would benefit from prolonged (24 months) DAPT?

Background: Prolonged DAPT therapy has been estimated to prevent 8 myocardial infarctions per 1,000 persons treated for 1 year but at the cost of 6 major bleeding events with no clear mortality benefit. Given these trade-offs, the DAPT score could be used to identify patients who would benefit or would be harmed from prolonged DAPT. The safety and efficacy of DAPT duration as guided by the DAPT score has not been assessed outside the derivation cohort. This study applied the DAPT score to the PRODIGY trial patients to evaluate safety and outcomes of DAPT for 24 months versus a less than 6-month regimen.

Study design: Retrospective use of the DAPT score in PRODIGY patients.

Setting: PCI patients in PRODIGY trial.

Synopsis: In the original derivation cohort, a low DAPT score of less than 2 identified patients whose bleeding risks outweigh ischemic benefits and a high score above 2 identifies patients for whom ischemic benefits outweigh bleeding risks. When the DAPT score was applied to the 1,970 patients enrolled in PRODIGY, 55% had a low score and 45% had a high score. The primary efficacy outcomes of death, MI, and stroke were evaluated as well as primary safety outcomes of bleeding according to the Bleeding Academic Research Consortium definition. The reduction in the primary efficacy outcomes with 24-month vs. 6-month DAPT was greater in patients with a high DAPT score but only in the older paclitaxel-eluting stents. Since these stents have mostly fallen out of favor, there are some limitations to the applicability of the study findings. The study also provides support for 6 months of DAPT for patients with a DAPT score of less than 2.

Bottom line: For patients who underwent PCI with a DAPT score of less than 2, the risk for bleeding appears to be higher than are the ischemic benefits, while patients who had a high DAPT score of greater than 2 with a first-generation stent, the ischemic benefits of prolonged DAPT seemed to outweigh the bleeding risks.

Citation: Piccolo R et al. Use of the dual-antiplatelet therapy score to guide treatment duration after percutaneous coronary intervention. Ann Intern Med. 2017 Jul 4;167(1):17-25

Dr. Setji is a hospitalist and medical director, Duke University Hospital.

 

Clinical question: Can the dual-antiplatelet therapy scoring system be used to determine which patients undergoing percutaneous coronary intervention (PCI) would benefit from prolonged (24 months) DAPT?

Background: Prolonged DAPT therapy has been estimated to prevent 8 myocardial infarctions per 1,000 persons treated for 1 year but at the cost of 6 major bleeding events with no clear mortality benefit. Given these trade-offs, the DAPT score could be used to identify patients who would benefit or would be harmed from prolonged DAPT. The safety and efficacy of DAPT duration as guided by the DAPT score has not been assessed outside the derivation cohort. This study applied the DAPT score to the PRODIGY trial patients to evaluate safety and outcomes of DAPT for 24 months versus a less than 6-month regimen.

Study design: Retrospective use of the DAPT score in PRODIGY patients.

Setting: PCI patients in PRODIGY trial.

Synopsis: In the original derivation cohort, a low DAPT score of less than 2 identified patients whose bleeding risks outweigh ischemic benefits and a high score above 2 identifies patients for whom ischemic benefits outweigh bleeding risks. When the DAPT score was applied to the 1,970 patients enrolled in PRODIGY, 55% had a low score and 45% had a high score. The primary efficacy outcomes of death, MI, and stroke were evaluated as well as primary safety outcomes of bleeding according to the Bleeding Academic Research Consortium definition. The reduction in the primary efficacy outcomes with 24-month vs. 6-month DAPT was greater in patients with a high DAPT score but only in the older paclitaxel-eluting stents. Since these stents have mostly fallen out of favor, there are some limitations to the applicability of the study findings. The study also provides support for 6 months of DAPT for patients with a DAPT score of less than 2.

Bottom line: For patients who underwent PCI with a DAPT score of less than 2, the risk for bleeding appears to be higher than are the ischemic benefits, while patients who had a high DAPT score of greater than 2 with a first-generation stent, the ischemic benefits of prolonged DAPT seemed to outweigh the bleeding risks.

Citation: Piccolo R et al. Use of the dual-antiplatelet therapy score to guide treatment duration after percutaneous coronary intervention. Ann Intern Med. 2017 Jul 4;167(1):17-25

Dr. Setji is a hospitalist and medical director, Duke University Hospital.

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Outcomes of alcohol septal ablation in younger patients with obstructive hypertrophic cardiomyopathy

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Clinical question: Is alcohol septal ablation (ASA) safe in younger patients with obstructive hypertrophic cardiomyopathy (HCM)?

Background: ASA is a class III recommendation for younger patients when myectomy is a viable option. This recommendation was based on the lack of evidence for younger patients whereas myectomy already was proven to be safe and effective.

Dr. Noppon Setji
Study design: International multicenter observational cohort design.

Setting: 7 tertiary centers from 4 European countries during 1996-2015.

Synopsis: With 1,200 patients, this was the largest ASA cohort to date. The cohort was divided into three groups: young (less than 50 years), middle age (51-65 years), and old (greater than 65 years). During the periprocedural period, young patients had better outcomes than did older patients in regards to complete heart block, cardiac tamponade, and mortality. After 5.4 years of follow-up, young patients had favorable outcomes for long-term survival after ASA and comparable rates of adverse antiarrhythmic events; 95% of these young patients were functioning in NYHA class I or II at follow-up. These young patients also had half the risk of permanent pacemaker implantation, compared with older patients. In an analysis of very young patients (younger than 35 years), ASA appeared to be safe and effective as well. Additionally, young patients who were treated with more than 2.5 mL alcohol had higher all-cause mortality, compared with patients who were treated with less than 2.5 mL.

Bottom line: For patients aged 50 years or less with HCM, ASA and surgical myectomy are both safe and effective for relief of symptoms.

Citation: Liebregts M et al. Outcomes of alcohol septal ablation in younger patients with obstructive hypertrophic cardiomyopathy. JACC: Cardiovascular Interventions. Jun 2017:1134-43.

Dr. Setji is a hospitalist and medical director, Duke University Hospital.

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Clinical question: Is alcohol septal ablation (ASA) safe in younger patients with obstructive hypertrophic cardiomyopathy (HCM)?

Background: ASA is a class III recommendation for younger patients when myectomy is a viable option. This recommendation was based on the lack of evidence for younger patients whereas myectomy already was proven to be safe and effective.

Dr. Noppon Setji
Study design: International multicenter observational cohort design.

Setting: 7 tertiary centers from 4 European countries during 1996-2015.

Synopsis: With 1,200 patients, this was the largest ASA cohort to date. The cohort was divided into three groups: young (less than 50 years), middle age (51-65 years), and old (greater than 65 years). During the periprocedural period, young patients had better outcomes than did older patients in regards to complete heart block, cardiac tamponade, and mortality. After 5.4 years of follow-up, young patients had favorable outcomes for long-term survival after ASA and comparable rates of adverse antiarrhythmic events; 95% of these young patients were functioning in NYHA class I or II at follow-up. These young patients also had half the risk of permanent pacemaker implantation, compared with older patients. In an analysis of very young patients (younger than 35 years), ASA appeared to be safe and effective as well. Additionally, young patients who were treated with more than 2.5 mL alcohol had higher all-cause mortality, compared with patients who were treated with less than 2.5 mL.

Bottom line: For patients aged 50 years or less with HCM, ASA and surgical myectomy are both safe and effective for relief of symptoms.

Citation: Liebregts M et al. Outcomes of alcohol septal ablation in younger patients with obstructive hypertrophic cardiomyopathy. JACC: Cardiovascular Interventions. Jun 2017:1134-43.

Dr. Setji is a hospitalist and medical director, Duke University Hospital.

 

Clinical question: Is alcohol septal ablation (ASA) safe in younger patients with obstructive hypertrophic cardiomyopathy (HCM)?

Background: ASA is a class III recommendation for younger patients when myectomy is a viable option. This recommendation was based on the lack of evidence for younger patients whereas myectomy already was proven to be safe and effective.

Dr. Noppon Setji
Study design: International multicenter observational cohort design.

Setting: 7 tertiary centers from 4 European countries during 1996-2015.

Synopsis: With 1,200 patients, this was the largest ASA cohort to date. The cohort was divided into three groups: young (less than 50 years), middle age (51-65 years), and old (greater than 65 years). During the periprocedural period, young patients had better outcomes than did older patients in regards to complete heart block, cardiac tamponade, and mortality. After 5.4 years of follow-up, young patients had favorable outcomes for long-term survival after ASA and comparable rates of adverse antiarrhythmic events; 95% of these young patients were functioning in NYHA class I or II at follow-up. These young patients also had half the risk of permanent pacemaker implantation, compared with older patients. In an analysis of very young patients (younger than 35 years), ASA appeared to be safe and effective as well. Additionally, young patients who were treated with more than 2.5 mL alcohol had higher all-cause mortality, compared with patients who were treated with less than 2.5 mL.

Bottom line: For patients aged 50 years or less with HCM, ASA and surgical myectomy are both safe and effective for relief of symptoms.

Citation: Liebregts M et al. Outcomes of alcohol septal ablation in younger patients with obstructive hypertrophic cardiomyopathy. JACC: Cardiovascular Interventions. Jun 2017:1134-43.

Dr. Setji is a hospitalist and medical director, Duke University Hospital.

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Individualized Care Plans

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The highest utilizers of care: Individualized care plans to coordinate care, improve healthcare service utilization, and reduce costs at an academic tertiary care center

High utilizers of hospital services are medically complex, psychosocially vulnerable, and at risk for adverse health outcomes.[1, 2] They make up a fraction of the patient population but use a disproportionate amount of resources, with high rates of emergency department (ED) visits and hospital admissions.[1, 3, 4] Less than 1% of patients account for 21% of national healthcare spending, and hospital costs are the largest category of national healthcare expenditures.[2, 5] Many patients who disproportionately contribute to high healthcare costs also have high hospital admission rates.[6]

Interventions targeting high utilizers have typically focused on the outpatient setting.[7, 8, 9, 10] Interventions using individualized care plans in the ED reduced ED visits from 33% to 70%, but all have required an additional case management program or partnership with an outside nonprofit case management organization.[11, 12, 13] One study by a hospitalist group using individualized care plans reduced ED visits and admissions by 70%, 2 months after care‐plan implementation; however, all of their care plans were focused explicitly on restricting intravenous opiate use for patients with chronic pain.[14]

Given the current focus on cost‐conscious, high‐quality care in the American healthcare system, we designed a quality‐improvement (QI) intervention using individualized care plans to reduce unnecessary healthcare service utilization and hospital costs for the highest utilizers of ED and inpatient care. Our approach focuses on integrating care plans within our electronic medical record (EMR) and implementing them using the existing healthcare workforce. We analyzed pre‐ and postintervention data to determine its effect on service utilization and hospital costs across a regional health system.

METHODS

QI Intervention

We retrospectively analyzed data collected as part of an ongoing QI project at Duke University Hospital, a 924‐bed academic tertiary care center with approximately 36,000 inpatient discharges per year. The Complex Care Plan Committee (CCPC) aims to improve the effectiveness, efficiency, and equity of care for medically, socially, and behaviorally complex adult patients who are the highest utilizers of care in the ED and inpatient medicine service. The CCPC is a volunteer, QI committee comprised of a multidisciplinary team from hospital medicine, emergency medicine, psychiatry, ambulatory care, social work, nursing, risk management, and performance services (system analysts). Individualized care plans are developed on a rolling basis as new patients are identified based on their hospital utilization rates (ED visits and admissions). To be eligible for a care plan, patients have to have at least 3 ED visits or admissions within 6 months and have some degree of medical, social, or behavioral complexity, for example, multiple medical comorbidities with care by several subspecialists, or concomitant psychiatric illness, substance abuse, and homelessness. Strict eligibility criteria are purposefully not imposed to allow flexibility and appropriate tailoring of this intervention to both high‐utilizing and complex patients. Given their complexity, the CCPC felt that without individualized care plans these patients would be at increased risk for rehospitalization and increased morbidity or mortality. The patients included in this analysis are the 24 patients with the most ED visits and hospital admissions at Duke University Hospital, accounting for a total of 183 ED visits and 145 inpatient admissions in the 6 months before the care plans were rolled out.

Each individualized care plan summarizes the patient's medical, psychiatric, and social histories, documents any disruptive behaviors, reviews their hospital utilization patterns, and proposes a set of management strategies focused on providing high‐quality care while limiting unnecessary admissions. They are written by 1 or 2 members of the CCPC who perform a thorough chart review and obtain collateral information from the ED, inpatient, and outpatient providers who have cared for that patient. Care plans are then reviewed and approved by the CCPC as a whole during monthly meetings. Care plans contain detailed information in the following domains: demographics; outpatient care team (primary care provider, specialists, psychiatrist/counselors, social worker, case manager, and home health agency); medical, psychiatric, and behavioral health history; social history; utilization patterns (dates of ED visits and hospitalizations with succinct narratives and outcomes of each admission); and finally ED, inpatient, and outpatient strategies for managing the patient, preventing unnecessary admissions, and connecting them to appropriate services. The CCPC chairperson reviews care plans quarterly to ensure they remain appropriate and relevant.

The care plan is a document uploaded into the EMR (EpicCare; Epic, Verona, WI), where it is available to any provider across the Duke health system. Within Epic, a colored banner visible across the top of the patient's chart notifies the provider of any patient with an individualized care plan. The care plan document is housed in a tab readily visible on the navigation pane. The care plan serves as a roadmap for ED providers and hospitalists, helping them navigate each patient's complex history and guiding them in their disposition decision making. We also developed an automated notification process such that when a high utilizer registers in the ED, a secure page is sent to the admitting hospitalist, who then notifies the ED provider. An automated email is also sent to the CCPC chairperson. These alerts also provide a mechanism for internal oversight and feedback by the CCPC to providers regarding care‐plan adherence.

Outcome Variables and Data Analysis

Our analysis included the 24 patients with individualized care plans developed from August 1, 2012 to August 31, 2013. We analyzed utilization data 6 and 12 months before and 6 and 12 months after the individualized care‐plan intervention was initiated (August 1, 2011 to August 31, 2014). Primary outcomes were the number of ED visits and hospital admissions, as well as ED and inpatient variable direct costs (VDCs). Secondary outcomes included inpatient length of stay (LOS) and 30‐day readmissions. We analyzed outcome data across all 3 hospitals in the Duke University Health System. This includes the only 2 hospitals in Durham, North Carolina (population 245,475) and 1 hospital in Raleigh, North Carolina (population 431,746).

We also describe basic demographic data, payor status, and medical comorbidities for this cohort of patients. Payor status is defined as the most frequently reported payor type prior to care‐plan implementation. Variable direct costs are directly related to patient care and fluctuate with patient volume. They include medications, supplies, laboratory tests, radiology studies, and nursing salaries. They are a proportion of total costs for an ED visit or hospitalization, excluding fixed and indirect costs, such as administrator or physician salaries, utilities, facilities, and equipment.

Primary and secondary outcomes were analyzed using descriptive statistics. Continuous outcomes are summarized with mean (standard deviation) and median (range), whereas categorical outcomes are summarized with N (%). LOS is calculated as the average number of days in the hospital per hospital admission per patient. The time periods of 12 months prior, 6 months prior, 6 months after, and 12 months after care‐plan implementation were examined. Only patients with 6 or more months of postcare‐plan data are included in the 6‐month comparison, and only patients with 12 or more months of postcare‐plan data are included in the 12‐month comparison. One patient in the 6‐month comparison group died very soon after care‐plan implementation, so that patient is included in Table 1 (N=24) but excluded from outcome analyses in Tables 2 and 3 (N=23). Differences between 6 months pre and 6 months postcare plan, and 12 months pre and 12 months postcare plan were examined using the Wilcoxon signed rank test for nonparametric matched data. Mean change is calculated as ([Post‐Pre]/Pre) for each patient, and then averaged across all patients. Mean percentage change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. It was done this way to emphasize the effect on the patient level. No adjustments were made for multiple comparisons. All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC). This study was granted exempt status by the Duke University Institutional Review Board.

Patient Demographics and Comorbidities
 Patients With Care Plans, N=24Patients With 12 Months PostCare Plan Follow‐up, N=12Patients With 6 Months PostCare Plan Follow‐up, N=23*
  • NOTE: Abbreviations: SD, standard deviation. *One patient died soon after care‐plan implementation; therefore N=23. Most frequently reported insurance type precare‐plan start date. Patients can have more than 1 comorbidity; therefore, numbers do not add up to N=24.

Age, y, mean (SD)38.5 (11.7)41.6 (9.2)37.3 (10.5)
Median (range)36 (2565)41 (2858)36 (2558)
Gender, N (%)   
Male11 (46%)5 (42%)11 (48%)
Female13 (54%)7 (58%)12 (52%)
Payor, N (%)   
Medicare11 (46%)6 (50%)10 (43%)
Medicaid9 (38%)4 (33%)9 (39%)
Medicare and Medicaid0 (0%)0 (0%)0 (0%)
Private insurance2 (8%)1 (8%)2 (9%)
None1 (4%)0 (0%)1 (4%)
Other1 (4%)1 (8%)1 (4%)
Comorbidities, N (%)   
Asthma9 (38%)5 (42%)9 (39%)
Chronic obstructive pulmonary disease2 (8%)2 (17%)2 (9%)
Chronic pain20 (83%)12 (100%)20 (87%)
Coronary artery disease5 (21%)4 (33%)5 (22%)
Diabetes mellitus10 (42%)6 (50%)9 (39%)
End‐stage renal disease4 (17%)4 (33%)4 (17%)
Heart failure5 (21%)2 (17%)4 (17%)
Hypertension13 (54%)6 (50%)12 (52%)
Mental health/substance abuse23 (96%)12 (100%)22 (96%)
Sickle cell10 (42%)5 (42%)10 (43%)
Utilization Patterns Before and After Care‐Plan Implementation Across Duke University Health System*
 6 Months Pre Care Plan6 Months Post Care Plan12 Months Pre Care Plan12 Months Post Care Plan6‐Month Change6‐Month P Value12‐Month Change12‐Month P Value
  • NOTE: Abbreviations: ED, emergency department; LOS, length of stay; SD, standard deviation. *Duke University Health System includes Duke University Hospital, Duke Regional Hospital, and Duke Raleigh Hospital. Mean percent change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. Mean change is calculated as Post‐Pre for each patient, and then averaged across patients. Wilcoxon signed rank test.

Admissions     <0.001 0.003
N2323121223231212
Total145561315856.0% (41.6%) 50.5% (43.9%) 
Mean (SD)6.3 (3.8)2.4 (2.4)10.9 (6.3)4.8 (4.2)3.9 (3.76) 6.1 (6.02) 
Median (range)5 (114)2 (08)8 (320)3 (011)    
30‐day readmissions    <0.001 0.002
N2323121223231212
Total130441064566.0% (32.4%) 51.5% (32.0%) 
Mean (SD)5.7 (4.1)1.9 (2.4)8.8 (7.0)3.8 (2.7)3.7 (3.79) 5.1 (5.71) 
Median (range)4 (013)1 (08)6 (019)3 (011)    
Inpatient LOS     0.506 0.910
N2323121223231212
Total76635866531750.8% (51.4%) 37.8% (78.8%) 
Mean (SD)5.0 (3.2)4.7 (4.3)4.7 (1.5)4.4 (3.1)0.3 (4.3) 0.3 (2.27) 
Median (range)4.3 (1.515.8)4 (016)4.8 (2.26.9)3.7 (09)    
ED visits     0.836 0.941
N2323121223231212
Total183198185307+42.9% (148.4%) +48.4% (145.1%) 
Mean (SD)8.0 (11.5)8.6 (19.8)15.4 (14.7)25.6 (54.4)0.7 (11.92) 10.2 (43.19) 
Median (range)5 (050)3 (096)12 (150)7 (1196)    
Healthcare Costs Before and After Care‐Plan Implementation Across Duke University Health System*
 6 Months Pre Care Plan6 Months Post Care Plan12 Months Pre Care Plan12 Months Post Care Plan6‐Month Change6‐Month P Value12‐Month Change12‐Month P Value
  • NOTE: Abbreviations: ED, emergency department; SD, standard deviation. *Duke University Health System includes Duke University Hospital, Duke Regional Hospital, and Duke Raleigh Hospital. Mean percent change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. Mean change is calculated as Post‐Pre for each patient, and then averaged across patients. Wilcoxon signed rank test.

Inpatient costs ($)    0.001 0.052
N2323121223231212
Total686,612.43358,520.42538,579.90299,501.0347.7% (52.3%) 35.8% (76.1%) 
Mean (SD)29,852.71 (21,808.22)15,587.84 (21,141.79)44,881.66 (30,132.26)24,958.42 (27,248.41)14,264.9 (19,301.75) 19,923.2 (31,891.69) 
Median (range)30,203.43 (1,625.1880,171.87)7,041.28 (086,457.05)39,936.05 (8,237.5382,861.11)13,321.56 (082,309.19)    
ED costs ($)     0.143 0.850
N2323121223231212
Total80,105.3460,500.3882,473.8698,298.84+12.5% (147.5%) +48.0% (161.8%) 
Mean (SD)3,482.84 (4,423.57)2,630.45 (4,782.56)6,872.82 (5,633.70)8,191.57 (13,974.75)852.4 (2,780.01) 1,318.7 (10,348.89) 
Median (range)2,239.19 (019,492.03)1,163.45 (022,449.84)5,924.31 (277.3019,492.03)3,002.70 (553.7250,955.56)    
Combined costs ($)     0.002 0.129
N2323121223231212
Total766,717.77419,020.80621,053.76397,799.8745.3% (48.3%) 25.5% (76.9%) 
Mean (SD)33,335.56 (22,427.77)18,218.30 (21,398.27)51,754.48 (32,248.94)33,149.99 (31,769.40)15,117.3 (19,932.41) 18,604.5 (35,513.56) 
Median (range)32,000.42 (1,625.1880,611.70)9,088.88 (087,549.37)45,716.08 (10,874.0599,426.72)23,971.85 (553.7285,440.12)    

RESULTS

Table 1 shows the demographics and comorbidities for the 24 patients with care plans included in this analysis. The average age of patients is 38.5 years (range, 2565 years) and a nearly even split between males (11) and females (13). Chronic disease burden is high. Furthermore, 83% of patients have chronic pain and 96% have mental health problems or substance abuse.

Table 2 shows inpatient and ED utilization patterns before and after care‐plan implementation. Inpatient admissions decreased by 56% for the 6 months after care‐plan implementation (P<0.001) and by 50.5% for the 12 months after care‐plan implementation (P=0.003). This translates to a decrease in the average number of admissions per patient from 6.3 to 2.4, 6 months postcare plan, and from 10.9 to 4.8, 12 months postcare plan.

Thirty‐day readmissions also significantly decreased after care‐plan implementation. Among the 23 patients with data 6 months pre and postcare plan, there were 130 readmissions before and 44 readmissions after care‐plan implementation, a 66% reduction (P<0.001). Among the 12 patients with data 12 months pre and postcare plan, there were 106 readmissions before and 45 readmissions after care‐plan implementation, a 51.5% reduction (P=0.002). Inpatient LOS did not show a statistically significant change after care‐plan implementation.

ED visits were similar for the 6 months pre compared to 6 months postcare plan. ED visits at 12 months postcare plan increased from an average of 15.4 visits pre to 25.6 visits per patient postcare plan. This was driven by a single homeless patient with dialysis‐dependent end‐stage renal disease, who had 134 ED visits in the 12 months after careplan implementation. Analysis of the data with this outlier removed showed a reduction in ED visits from an average of 12.3 visits per patient to 10.1 visits per patient in the 12 months postcare plan; however, this was not statistically significant (P=0.66, data not shown).

Table 3 shows inpatient and ED VDCs before and after care‐plan implementation. The average VDCs per patient per admission decreased from $29,852.71 to $15,587.84, 6 months after care‐plan implementation, a 47.7% reduction (P=0.001). The average VDCs per patient per admission decreased from $44,881.66 to $24,958.42, 12 months after care‐plan implementation, a 35.8% reduction (P=0.052). ED costs did not show a statistically significant decrease. However, with the outlier removed as above, costs did decrease by 12.3%, 6 months after care‐plan implementation, approaching statistical significance (P=0.073, data not shown). Combined inpatient and ED variable direct costs decreased by an average of $15,117.30, 6 months after care‐plan implementation, a 45.3% reduction (P=0.002), and by an average of $18,604.50, 12 months after care‐plan implementation, a 25.5% reduction, although this did not reach statistical significance (P=0.129).

DISCUSSION

A multidisciplinary team at our academic medical center developed individualized care plans tailored to the specific medical and psychosocial complexities of high utilizers to reduce unnecessary service utilization and hospital costs. Postintervention analysis shows reduced inpatient admissions and 30‐day readmissions among this population by 50%. Furthermore, inpatient variable direct costs decreased by 47% for the 6 months following care‐plan implementation and by 35% for the 12 months following care‐plan implementation. This translates into a $347,696.97 cost savings for the 23 patients 6 months after care‐plan implementation, and a $223,253.89 cost savings for the 12 patients 12 months after care‐plan implementation. This reduction in utilization and cost was seen across all 3 hospitals in the Duke University Health System, including the only 2 hospitals in Durham, North Carolina. Unlike other urban areas, public transportation in our region is scarce, and the options for hospital shopping in central North Carolina are relatively limited. Although this study does not measure utilization in surrounding counties, we do not feel this occurred as we did not see a rise in requests for medical records nor attempts to contact Duke providers for questions on these patients as a result of our intervention. This, along with our regional health system outcome analysis, provides support that our intervention did not cause patients to seek care elsewhere and result in cost‐shifting to other facilities.

We hypothesize that our care plans may be responsible for decreased admissions and 30‐day readmissions through several mechanisms. By raising awareness of these patients' excessive hospital utilization patterns and making this information readily available through our EMR, providers in the ED may be more conscientious about their admission decisions. Problems that at face value seem acute, are often more chronic and can be better managed in the outpatient setting. Several care plans also explicitly recommend limiting unnecessary intravenous opiate use for chronic pain patients. Other patients who have frequent admissions actually have end‐stage disease, and care plans for these patients help facilitate referrals to hospice programs.

Care plans provide a consistent message of patient histories, utilization patterns, and management strategies, and also serve as a communication tool between hospitalists and ED providers. A systematic review of all ED‐based interventions for high utilizers revealed that most studies did show a reduction in ED visits, but all incorporated case management programs to do so.[15] We did not reduce ED visits, possibly because we lacked the resources and care coordination a community‐based case management program provides. However, care plans did serve as a platform with which hospitalists and ED providers can help coordinate care among multiple outpatient providers. This has potentially limited admissions by providing a referral destination or outpatient point of contact for ED providers. For example, as a result of our intervention, referral mechanisms to our comprehensive pain clinic and outpatient psychiatry clinic have both been strengthened and streamlined. The fact that care plans decreased admissions and readmissions, but not ED visits, suggests that our intervention may not have actually changed patient behavior, but instead changed provider practices in relation to disposition decisions in the ED.

Our QI intervention has several strengths. First, it is fully integrated within our existing healthcare workforce, without the need for an extra case management system. Second, it is seamlessly incorporated into our EMR and represents another potential use of an EMR that has not been previously touted. Third, the multidisciplinary nature of the CCPC ensures that all stakeholders involved in the care of high utilizers are represented. Fourth, the outcome analysis across all 3 hospitals in our health system provides a balancing metric against the notion that our intervention simply caused patients to seek care elsewhere in the region. Last, the QI design and lack of strict inclusion and exclusion criteria adds practicality and shows effectiveness, not just efficacy, of the intervention.

Because this was developed as a QI intervention without strict inclusion and exclusion criteria, generalizability is lacking. In the future, one could use the EMR to more systematically identify high‐utilizing, complex patients. One study showed the ability to use the EMR with a standardized framework to identify hot spotting (high utilizers) and contextual anomaly detection (ie, anomalous utilization cases where patient‐incurred levels of utilization are unexpected given their clinical characteristics).[16] The nonrandomized, retrospective pre/post‐intervention analysis without a control group diminishes the external validity of the results and does introduce the potential for bias.

One of the primary study limitations includes the small sample size of only 24 patients. Admittedly, these first 24 patients are the absolute highest utilizers of care at our hospital, possibly making their utilization patterns more amenable to our intervention. The 96% prevalence rate of mental health and substance abuse in our cohort is significantly higher than other published data among high utilizers.[4, 17, 18] We are continuing to develop care plans for additional high‐utilizing, complex patients, and expect to enroll more patients with end‐stage disease, and relatively fewer with substance abuse or psychiatric illness as time goes on. It is possible this new cohort of patients has proportionally less unnecessary utilization, thus limiting our intervention effect. One final limitation of our study is the lack of care quality and patient safety outcomes. In future studies, health outcomes, adverse events, and outpatient care utilization will be important balancing measures to include.

In conclusion, we showed that a QI intervention using individualized care plans reduces hospital admissions, 30‐day readmissions, and hospital costs across a regional health system for a group of complex, high‐utilizing patients. This intervention can, and should, be developed by a multidisciplinary team and fully integrated into the existing healthcare workforce and EMR to ensure appropriateness, effectiveness, and longevity. Going forward, it will be imperative to evaluate this intervention prospectively, at multiple sites, in coordination with outpatient providers, and including quality and safety outcomes to determine if this hospital‐based intervention impacts care coordination, utilization rates, cost, and health outcomes across the broader healthcare system.

Disclosure

Nothing to report.

Files
References
  1. Hansagi H, Olsson M, Sjoberg S, Tomson Y, Goransson S. Frequent use of the hospital emergency department is indicative of high use of other health care services. Ann Emerg Med. 2001;37:561567.
  2. Matzer F, Wisiak UV, Graninger M, et al. Biopsychosocial health care needs at the emergency room: challenge of complexity. PLoS One. 2012;7:e41775.
  3. Agency for Healthcare Research and Quality. The high concentration of U.S. health care expenditures. Research in Action. Available at: http://meps.ahrq.gov/mepsweb/data_files/publications/ra19/ra19.pdf. Published June 2006. Accessed November 18, 2013.
  4. Byrne M, Murphy AW, Plunkett PK, McGee HM, Murray A, Bury G. Frequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristics. Ann Emerg Med. 2003;41:309318.
  5. Centers for Medicare 8:665671.
  6. Katzelnick DJ, Simon GE, Pearson SD, et al. Randomized trial of a depression management program in high utilizers of medical care. Arch Fam Med. 2000;9:345351.
  7. Badger T, Gelenberg AJ, Berren M. Consultative intervention to improve outcomes of high utilizers in a public mental health system. Perspect Psychiatr Care. 2004;40:5360, 69.
  8. Law DD, Crane DR, Berge JM. The influence of individual, marital, and family therapy on high utilizers of health care. J Marital Fam Ther. 2003;29:353363.
  9. Okin RL, Boccellari A, Azocar F, et al. The effects of clinical case management on hospital service use among ED frequent users. Am J Emerg Med. 2000;18:603608.
  10. For success with frequent ED utilizers, take steps to understand patient needs, connect them with appropriate resources. ED Manag. 2013;25:5759.
  11. ED diversion: multidisciplinary approach engages high utilizers, helps them better navigate the health care system. ED Manag. 2011;23:127130.
  12. CM program keeps high utilizers out of hospital. Hosp Case Manag. 2012;20:108109.
  13. Hilger R, Quirk R, Dahms R. Use of restriction care plans to decrease medically unnecessary admissions and emergency department visits. J Hosp Med. 2012;7:S2.
  14. Althaus F, Paroz S, Hugli O, et al. Effectiveness of interventions targeting frequent users of emergency departments: a systematic review. Ann Emerg Med. 2011;58:4152.
  15. Hu J, Wang F, Sun J, Sorrentino R, Ebadollahi S. A healthcare utilization analysis framework for hot spotting and contextual anomaly detection. AMIA Annu Symp Proc. 2012;2012:360369.
  16. Pasic J, Russo J, Roy‐Byrne P. High utilizers of psychiatric emergency services. Psychiatr Serv. 2005;56:678684.
  17. Henk HJ, Katzelnick DJ, Kobak KA, Greist JH, Jefferson JW. Medical costs attributed to depression among patients with a history of high medical expenses in a health maintenance organization. Arch Gen Psychiatry. 1996;53:899904.
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High utilizers of hospital services are medically complex, psychosocially vulnerable, and at risk for adverse health outcomes.[1, 2] They make up a fraction of the patient population but use a disproportionate amount of resources, with high rates of emergency department (ED) visits and hospital admissions.[1, 3, 4] Less than 1% of patients account for 21% of national healthcare spending, and hospital costs are the largest category of national healthcare expenditures.[2, 5] Many patients who disproportionately contribute to high healthcare costs also have high hospital admission rates.[6]

Interventions targeting high utilizers have typically focused on the outpatient setting.[7, 8, 9, 10] Interventions using individualized care plans in the ED reduced ED visits from 33% to 70%, but all have required an additional case management program or partnership with an outside nonprofit case management organization.[11, 12, 13] One study by a hospitalist group using individualized care plans reduced ED visits and admissions by 70%, 2 months after care‐plan implementation; however, all of their care plans were focused explicitly on restricting intravenous opiate use for patients with chronic pain.[14]

Given the current focus on cost‐conscious, high‐quality care in the American healthcare system, we designed a quality‐improvement (QI) intervention using individualized care plans to reduce unnecessary healthcare service utilization and hospital costs for the highest utilizers of ED and inpatient care. Our approach focuses on integrating care plans within our electronic medical record (EMR) and implementing them using the existing healthcare workforce. We analyzed pre‐ and postintervention data to determine its effect on service utilization and hospital costs across a regional health system.

METHODS

QI Intervention

We retrospectively analyzed data collected as part of an ongoing QI project at Duke University Hospital, a 924‐bed academic tertiary care center with approximately 36,000 inpatient discharges per year. The Complex Care Plan Committee (CCPC) aims to improve the effectiveness, efficiency, and equity of care for medically, socially, and behaviorally complex adult patients who are the highest utilizers of care in the ED and inpatient medicine service. The CCPC is a volunteer, QI committee comprised of a multidisciplinary team from hospital medicine, emergency medicine, psychiatry, ambulatory care, social work, nursing, risk management, and performance services (system analysts). Individualized care plans are developed on a rolling basis as new patients are identified based on their hospital utilization rates (ED visits and admissions). To be eligible for a care plan, patients have to have at least 3 ED visits or admissions within 6 months and have some degree of medical, social, or behavioral complexity, for example, multiple medical comorbidities with care by several subspecialists, or concomitant psychiatric illness, substance abuse, and homelessness. Strict eligibility criteria are purposefully not imposed to allow flexibility and appropriate tailoring of this intervention to both high‐utilizing and complex patients. Given their complexity, the CCPC felt that without individualized care plans these patients would be at increased risk for rehospitalization and increased morbidity or mortality. The patients included in this analysis are the 24 patients with the most ED visits and hospital admissions at Duke University Hospital, accounting for a total of 183 ED visits and 145 inpatient admissions in the 6 months before the care plans were rolled out.

Each individualized care plan summarizes the patient's medical, psychiatric, and social histories, documents any disruptive behaviors, reviews their hospital utilization patterns, and proposes a set of management strategies focused on providing high‐quality care while limiting unnecessary admissions. They are written by 1 or 2 members of the CCPC who perform a thorough chart review and obtain collateral information from the ED, inpatient, and outpatient providers who have cared for that patient. Care plans are then reviewed and approved by the CCPC as a whole during monthly meetings. Care plans contain detailed information in the following domains: demographics; outpatient care team (primary care provider, specialists, psychiatrist/counselors, social worker, case manager, and home health agency); medical, psychiatric, and behavioral health history; social history; utilization patterns (dates of ED visits and hospitalizations with succinct narratives and outcomes of each admission); and finally ED, inpatient, and outpatient strategies for managing the patient, preventing unnecessary admissions, and connecting them to appropriate services. The CCPC chairperson reviews care plans quarterly to ensure they remain appropriate and relevant.

The care plan is a document uploaded into the EMR (EpicCare; Epic, Verona, WI), where it is available to any provider across the Duke health system. Within Epic, a colored banner visible across the top of the patient's chart notifies the provider of any patient with an individualized care plan. The care plan document is housed in a tab readily visible on the navigation pane. The care plan serves as a roadmap for ED providers and hospitalists, helping them navigate each patient's complex history and guiding them in their disposition decision making. We also developed an automated notification process such that when a high utilizer registers in the ED, a secure page is sent to the admitting hospitalist, who then notifies the ED provider. An automated email is also sent to the CCPC chairperson. These alerts also provide a mechanism for internal oversight and feedback by the CCPC to providers regarding care‐plan adherence.

Outcome Variables and Data Analysis

Our analysis included the 24 patients with individualized care plans developed from August 1, 2012 to August 31, 2013. We analyzed utilization data 6 and 12 months before and 6 and 12 months after the individualized care‐plan intervention was initiated (August 1, 2011 to August 31, 2014). Primary outcomes were the number of ED visits and hospital admissions, as well as ED and inpatient variable direct costs (VDCs). Secondary outcomes included inpatient length of stay (LOS) and 30‐day readmissions. We analyzed outcome data across all 3 hospitals in the Duke University Health System. This includes the only 2 hospitals in Durham, North Carolina (population 245,475) and 1 hospital in Raleigh, North Carolina (population 431,746).

We also describe basic demographic data, payor status, and medical comorbidities for this cohort of patients. Payor status is defined as the most frequently reported payor type prior to care‐plan implementation. Variable direct costs are directly related to patient care and fluctuate with patient volume. They include medications, supplies, laboratory tests, radiology studies, and nursing salaries. They are a proportion of total costs for an ED visit or hospitalization, excluding fixed and indirect costs, such as administrator or physician salaries, utilities, facilities, and equipment.

Primary and secondary outcomes were analyzed using descriptive statistics. Continuous outcomes are summarized with mean (standard deviation) and median (range), whereas categorical outcomes are summarized with N (%). LOS is calculated as the average number of days in the hospital per hospital admission per patient. The time periods of 12 months prior, 6 months prior, 6 months after, and 12 months after care‐plan implementation were examined. Only patients with 6 or more months of postcare‐plan data are included in the 6‐month comparison, and only patients with 12 or more months of postcare‐plan data are included in the 12‐month comparison. One patient in the 6‐month comparison group died very soon after care‐plan implementation, so that patient is included in Table 1 (N=24) but excluded from outcome analyses in Tables 2 and 3 (N=23). Differences between 6 months pre and 6 months postcare plan, and 12 months pre and 12 months postcare plan were examined using the Wilcoxon signed rank test for nonparametric matched data. Mean change is calculated as ([Post‐Pre]/Pre) for each patient, and then averaged across all patients. Mean percentage change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. It was done this way to emphasize the effect on the patient level. No adjustments were made for multiple comparisons. All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC). This study was granted exempt status by the Duke University Institutional Review Board.

Patient Demographics and Comorbidities
 Patients With Care Plans, N=24Patients With 12 Months PostCare Plan Follow‐up, N=12Patients With 6 Months PostCare Plan Follow‐up, N=23*
  • NOTE: Abbreviations: SD, standard deviation. *One patient died soon after care‐plan implementation; therefore N=23. Most frequently reported insurance type precare‐plan start date. Patients can have more than 1 comorbidity; therefore, numbers do not add up to N=24.

Age, y, mean (SD)38.5 (11.7)41.6 (9.2)37.3 (10.5)
Median (range)36 (2565)41 (2858)36 (2558)
Gender, N (%)   
Male11 (46%)5 (42%)11 (48%)
Female13 (54%)7 (58%)12 (52%)
Payor, N (%)   
Medicare11 (46%)6 (50%)10 (43%)
Medicaid9 (38%)4 (33%)9 (39%)
Medicare and Medicaid0 (0%)0 (0%)0 (0%)
Private insurance2 (8%)1 (8%)2 (9%)
None1 (4%)0 (0%)1 (4%)
Other1 (4%)1 (8%)1 (4%)
Comorbidities, N (%)   
Asthma9 (38%)5 (42%)9 (39%)
Chronic obstructive pulmonary disease2 (8%)2 (17%)2 (9%)
Chronic pain20 (83%)12 (100%)20 (87%)
Coronary artery disease5 (21%)4 (33%)5 (22%)
Diabetes mellitus10 (42%)6 (50%)9 (39%)
End‐stage renal disease4 (17%)4 (33%)4 (17%)
Heart failure5 (21%)2 (17%)4 (17%)
Hypertension13 (54%)6 (50%)12 (52%)
Mental health/substance abuse23 (96%)12 (100%)22 (96%)
Sickle cell10 (42%)5 (42%)10 (43%)
Utilization Patterns Before and After Care‐Plan Implementation Across Duke University Health System*
 6 Months Pre Care Plan6 Months Post Care Plan12 Months Pre Care Plan12 Months Post Care Plan6‐Month Change6‐Month P Value12‐Month Change12‐Month P Value
  • NOTE: Abbreviations: ED, emergency department; LOS, length of stay; SD, standard deviation. *Duke University Health System includes Duke University Hospital, Duke Regional Hospital, and Duke Raleigh Hospital. Mean percent change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. Mean change is calculated as Post‐Pre for each patient, and then averaged across patients. Wilcoxon signed rank test.

Admissions     <0.001 0.003
N2323121223231212
Total145561315856.0% (41.6%) 50.5% (43.9%) 
Mean (SD)6.3 (3.8)2.4 (2.4)10.9 (6.3)4.8 (4.2)3.9 (3.76) 6.1 (6.02) 
Median (range)5 (114)2 (08)8 (320)3 (011)    
30‐day readmissions    <0.001 0.002
N2323121223231212
Total130441064566.0% (32.4%) 51.5% (32.0%) 
Mean (SD)5.7 (4.1)1.9 (2.4)8.8 (7.0)3.8 (2.7)3.7 (3.79) 5.1 (5.71) 
Median (range)4 (013)1 (08)6 (019)3 (011)    
Inpatient LOS     0.506 0.910
N2323121223231212
Total76635866531750.8% (51.4%) 37.8% (78.8%) 
Mean (SD)5.0 (3.2)4.7 (4.3)4.7 (1.5)4.4 (3.1)0.3 (4.3) 0.3 (2.27) 
Median (range)4.3 (1.515.8)4 (016)4.8 (2.26.9)3.7 (09)    
ED visits     0.836 0.941
N2323121223231212
Total183198185307+42.9% (148.4%) +48.4% (145.1%) 
Mean (SD)8.0 (11.5)8.6 (19.8)15.4 (14.7)25.6 (54.4)0.7 (11.92) 10.2 (43.19) 
Median (range)5 (050)3 (096)12 (150)7 (1196)    
Healthcare Costs Before and After Care‐Plan Implementation Across Duke University Health System*
 6 Months Pre Care Plan6 Months Post Care Plan12 Months Pre Care Plan12 Months Post Care Plan6‐Month Change6‐Month P Value12‐Month Change12‐Month P Value
  • NOTE: Abbreviations: ED, emergency department; SD, standard deviation. *Duke University Health System includes Duke University Hospital, Duke Regional Hospital, and Duke Raleigh Hospital. Mean percent change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. Mean change is calculated as Post‐Pre for each patient, and then averaged across patients. Wilcoxon signed rank test.

Inpatient costs ($)    0.001 0.052
N2323121223231212
Total686,612.43358,520.42538,579.90299,501.0347.7% (52.3%) 35.8% (76.1%) 
Mean (SD)29,852.71 (21,808.22)15,587.84 (21,141.79)44,881.66 (30,132.26)24,958.42 (27,248.41)14,264.9 (19,301.75) 19,923.2 (31,891.69) 
Median (range)30,203.43 (1,625.1880,171.87)7,041.28 (086,457.05)39,936.05 (8,237.5382,861.11)13,321.56 (082,309.19)    
ED costs ($)     0.143 0.850
N2323121223231212
Total80,105.3460,500.3882,473.8698,298.84+12.5% (147.5%) +48.0% (161.8%) 
Mean (SD)3,482.84 (4,423.57)2,630.45 (4,782.56)6,872.82 (5,633.70)8,191.57 (13,974.75)852.4 (2,780.01) 1,318.7 (10,348.89) 
Median (range)2,239.19 (019,492.03)1,163.45 (022,449.84)5,924.31 (277.3019,492.03)3,002.70 (553.7250,955.56)    
Combined costs ($)     0.002 0.129
N2323121223231212
Total766,717.77419,020.80621,053.76397,799.8745.3% (48.3%) 25.5% (76.9%) 
Mean (SD)33,335.56 (22,427.77)18,218.30 (21,398.27)51,754.48 (32,248.94)33,149.99 (31,769.40)15,117.3 (19,932.41) 18,604.5 (35,513.56) 
Median (range)32,000.42 (1,625.1880,611.70)9,088.88 (087,549.37)45,716.08 (10,874.0599,426.72)23,971.85 (553.7285,440.12)    

RESULTS

Table 1 shows the demographics and comorbidities for the 24 patients with care plans included in this analysis. The average age of patients is 38.5 years (range, 2565 years) and a nearly even split between males (11) and females (13). Chronic disease burden is high. Furthermore, 83% of patients have chronic pain and 96% have mental health problems or substance abuse.

Table 2 shows inpatient and ED utilization patterns before and after care‐plan implementation. Inpatient admissions decreased by 56% for the 6 months after care‐plan implementation (P<0.001) and by 50.5% for the 12 months after care‐plan implementation (P=0.003). This translates to a decrease in the average number of admissions per patient from 6.3 to 2.4, 6 months postcare plan, and from 10.9 to 4.8, 12 months postcare plan.

Thirty‐day readmissions also significantly decreased after care‐plan implementation. Among the 23 patients with data 6 months pre and postcare plan, there were 130 readmissions before and 44 readmissions after care‐plan implementation, a 66% reduction (P<0.001). Among the 12 patients with data 12 months pre and postcare plan, there were 106 readmissions before and 45 readmissions after care‐plan implementation, a 51.5% reduction (P=0.002). Inpatient LOS did not show a statistically significant change after care‐plan implementation.

ED visits were similar for the 6 months pre compared to 6 months postcare plan. ED visits at 12 months postcare plan increased from an average of 15.4 visits pre to 25.6 visits per patient postcare plan. This was driven by a single homeless patient with dialysis‐dependent end‐stage renal disease, who had 134 ED visits in the 12 months after careplan implementation. Analysis of the data with this outlier removed showed a reduction in ED visits from an average of 12.3 visits per patient to 10.1 visits per patient in the 12 months postcare plan; however, this was not statistically significant (P=0.66, data not shown).

Table 3 shows inpatient and ED VDCs before and after care‐plan implementation. The average VDCs per patient per admission decreased from $29,852.71 to $15,587.84, 6 months after care‐plan implementation, a 47.7% reduction (P=0.001). The average VDCs per patient per admission decreased from $44,881.66 to $24,958.42, 12 months after care‐plan implementation, a 35.8% reduction (P=0.052). ED costs did not show a statistically significant decrease. However, with the outlier removed as above, costs did decrease by 12.3%, 6 months after care‐plan implementation, approaching statistical significance (P=0.073, data not shown). Combined inpatient and ED variable direct costs decreased by an average of $15,117.30, 6 months after care‐plan implementation, a 45.3% reduction (P=0.002), and by an average of $18,604.50, 12 months after care‐plan implementation, a 25.5% reduction, although this did not reach statistical significance (P=0.129).

DISCUSSION

A multidisciplinary team at our academic medical center developed individualized care plans tailored to the specific medical and psychosocial complexities of high utilizers to reduce unnecessary service utilization and hospital costs. Postintervention analysis shows reduced inpatient admissions and 30‐day readmissions among this population by 50%. Furthermore, inpatient variable direct costs decreased by 47% for the 6 months following care‐plan implementation and by 35% for the 12 months following care‐plan implementation. This translates into a $347,696.97 cost savings for the 23 patients 6 months after care‐plan implementation, and a $223,253.89 cost savings for the 12 patients 12 months after care‐plan implementation. This reduction in utilization and cost was seen across all 3 hospitals in the Duke University Health System, including the only 2 hospitals in Durham, North Carolina. Unlike other urban areas, public transportation in our region is scarce, and the options for hospital shopping in central North Carolina are relatively limited. Although this study does not measure utilization in surrounding counties, we do not feel this occurred as we did not see a rise in requests for medical records nor attempts to contact Duke providers for questions on these patients as a result of our intervention. This, along with our regional health system outcome analysis, provides support that our intervention did not cause patients to seek care elsewhere and result in cost‐shifting to other facilities.

We hypothesize that our care plans may be responsible for decreased admissions and 30‐day readmissions through several mechanisms. By raising awareness of these patients' excessive hospital utilization patterns and making this information readily available through our EMR, providers in the ED may be more conscientious about their admission decisions. Problems that at face value seem acute, are often more chronic and can be better managed in the outpatient setting. Several care plans also explicitly recommend limiting unnecessary intravenous opiate use for chronic pain patients. Other patients who have frequent admissions actually have end‐stage disease, and care plans for these patients help facilitate referrals to hospice programs.

Care plans provide a consistent message of patient histories, utilization patterns, and management strategies, and also serve as a communication tool between hospitalists and ED providers. A systematic review of all ED‐based interventions for high utilizers revealed that most studies did show a reduction in ED visits, but all incorporated case management programs to do so.[15] We did not reduce ED visits, possibly because we lacked the resources and care coordination a community‐based case management program provides. However, care plans did serve as a platform with which hospitalists and ED providers can help coordinate care among multiple outpatient providers. This has potentially limited admissions by providing a referral destination or outpatient point of contact for ED providers. For example, as a result of our intervention, referral mechanisms to our comprehensive pain clinic and outpatient psychiatry clinic have both been strengthened and streamlined. The fact that care plans decreased admissions and readmissions, but not ED visits, suggests that our intervention may not have actually changed patient behavior, but instead changed provider practices in relation to disposition decisions in the ED.

Our QI intervention has several strengths. First, it is fully integrated within our existing healthcare workforce, without the need for an extra case management system. Second, it is seamlessly incorporated into our EMR and represents another potential use of an EMR that has not been previously touted. Third, the multidisciplinary nature of the CCPC ensures that all stakeholders involved in the care of high utilizers are represented. Fourth, the outcome analysis across all 3 hospitals in our health system provides a balancing metric against the notion that our intervention simply caused patients to seek care elsewhere in the region. Last, the QI design and lack of strict inclusion and exclusion criteria adds practicality and shows effectiveness, not just efficacy, of the intervention.

Because this was developed as a QI intervention without strict inclusion and exclusion criteria, generalizability is lacking. In the future, one could use the EMR to more systematically identify high‐utilizing, complex patients. One study showed the ability to use the EMR with a standardized framework to identify hot spotting (high utilizers) and contextual anomaly detection (ie, anomalous utilization cases where patient‐incurred levels of utilization are unexpected given their clinical characteristics).[16] The nonrandomized, retrospective pre/post‐intervention analysis without a control group diminishes the external validity of the results and does introduce the potential for bias.

One of the primary study limitations includes the small sample size of only 24 patients. Admittedly, these first 24 patients are the absolute highest utilizers of care at our hospital, possibly making their utilization patterns more amenable to our intervention. The 96% prevalence rate of mental health and substance abuse in our cohort is significantly higher than other published data among high utilizers.[4, 17, 18] We are continuing to develop care plans for additional high‐utilizing, complex patients, and expect to enroll more patients with end‐stage disease, and relatively fewer with substance abuse or psychiatric illness as time goes on. It is possible this new cohort of patients has proportionally less unnecessary utilization, thus limiting our intervention effect. One final limitation of our study is the lack of care quality and patient safety outcomes. In future studies, health outcomes, adverse events, and outpatient care utilization will be important balancing measures to include.

In conclusion, we showed that a QI intervention using individualized care plans reduces hospital admissions, 30‐day readmissions, and hospital costs across a regional health system for a group of complex, high‐utilizing patients. This intervention can, and should, be developed by a multidisciplinary team and fully integrated into the existing healthcare workforce and EMR to ensure appropriateness, effectiveness, and longevity. Going forward, it will be imperative to evaluate this intervention prospectively, at multiple sites, in coordination with outpatient providers, and including quality and safety outcomes to determine if this hospital‐based intervention impacts care coordination, utilization rates, cost, and health outcomes across the broader healthcare system.

Disclosure

Nothing to report.

High utilizers of hospital services are medically complex, psychosocially vulnerable, and at risk for adverse health outcomes.[1, 2] They make up a fraction of the patient population but use a disproportionate amount of resources, with high rates of emergency department (ED) visits and hospital admissions.[1, 3, 4] Less than 1% of patients account for 21% of national healthcare spending, and hospital costs are the largest category of national healthcare expenditures.[2, 5] Many patients who disproportionately contribute to high healthcare costs also have high hospital admission rates.[6]

Interventions targeting high utilizers have typically focused on the outpatient setting.[7, 8, 9, 10] Interventions using individualized care plans in the ED reduced ED visits from 33% to 70%, but all have required an additional case management program or partnership with an outside nonprofit case management organization.[11, 12, 13] One study by a hospitalist group using individualized care plans reduced ED visits and admissions by 70%, 2 months after care‐plan implementation; however, all of their care plans were focused explicitly on restricting intravenous opiate use for patients with chronic pain.[14]

Given the current focus on cost‐conscious, high‐quality care in the American healthcare system, we designed a quality‐improvement (QI) intervention using individualized care plans to reduce unnecessary healthcare service utilization and hospital costs for the highest utilizers of ED and inpatient care. Our approach focuses on integrating care plans within our electronic medical record (EMR) and implementing them using the existing healthcare workforce. We analyzed pre‐ and postintervention data to determine its effect on service utilization and hospital costs across a regional health system.

METHODS

QI Intervention

We retrospectively analyzed data collected as part of an ongoing QI project at Duke University Hospital, a 924‐bed academic tertiary care center with approximately 36,000 inpatient discharges per year. The Complex Care Plan Committee (CCPC) aims to improve the effectiveness, efficiency, and equity of care for medically, socially, and behaviorally complex adult patients who are the highest utilizers of care in the ED and inpatient medicine service. The CCPC is a volunteer, QI committee comprised of a multidisciplinary team from hospital medicine, emergency medicine, psychiatry, ambulatory care, social work, nursing, risk management, and performance services (system analysts). Individualized care plans are developed on a rolling basis as new patients are identified based on their hospital utilization rates (ED visits and admissions). To be eligible for a care plan, patients have to have at least 3 ED visits or admissions within 6 months and have some degree of medical, social, or behavioral complexity, for example, multiple medical comorbidities with care by several subspecialists, or concomitant psychiatric illness, substance abuse, and homelessness. Strict eligibility criteria are purposefully not imposed to allow flexibility and appropriate tailoring of this intervention to both high‐utilizing and complex patients. Given their complexity, the CCPC felt that without individualized care plans these patients would be at increased risk for rehospitalization and increased morbidity or mortality. The patients included in this analysis are the 24 patients with the most ED visits and hospital admissions at Duke University Hospital, accounting for a total of 183 ED visits and 145 inpatient admissions in the 6 months before the care plans were rolled out.

Each individualized care plan summarizes the patient's medical, psychiatric, and social histories, documents any disruptive behaviors, reviews their hospital utilization patterns, and proposes a set of management strategies focused on providing high‐quality care while limiting unnecessary admissions. They are written by 1 or 2 members of the CCPC who perform a thorough chart review and obtain collateral information from the ED, inpatient, and outpatient providers who have cared for that patient. Care plans are then reviewed and approved by the CCPC as a whole during monthly meetings. Care plans contain detailed information in the following domains: demographics; outpatient care team (primary care provider, specialists, psychiatrist/counselors, social worker, case manager, and home health agency); medical, psychiatric, and behavioral health history; social history; utilization patterns (dates of ED visits and hospitalizations with succinct narratives and outcomes of each admission); and finally ED, inpatient, and outpatient strategies for managing the patient, preventing unnecessary admissions, and connecting them to appropriate services. The CCPC chairperson reviews care plans quarterly to ensure they remain appropriate and relevant.

The care plan is a document uploaded into the EMR (EpicCare; Epic, Verona, WI), where it is available to any provider across the Duke health system. Within Epic, a colored banner visible across the top of the patient's chart notifies the provider of any patient with an individualized care plan. The care plan document is housed in a tab readily visible on the navigation pane. The care plan serves as a roadmap for ED providers and hospitalists, helping them navigate each patient's complex history and guiding them in their disposition decision making. We also developed an automated notification process such that when a high utilizer registers in the ED, a secure page is sent to the admitting hospitalist, who then notifies the ED provider. An automated email is also sent to the CCPC chairperson. These alerts also provide a mechanism for internal oversight and feedback by the CCPC to providers regarding care‐plan adherence.

Outcome Variables and Data Analysis

Our analysis included the 24 patients with individualized care plans developed from August 1, 2012 to August 31, 2013. We analyzed utilization data 6 and 12 months before and 6 and 12 months after the individualized care‐plan intervention was initiated (August 1, 2011 to August 31, 2014). Primary outcomes were the number of ED visits and hospital admissions, as well as ED and inpatient variable direct costs (VDCs). Secondary outcomes included inpatient length of stay (LOS) and 30‐day readmissions. We analyzed outcome data across all 3 hospitals in the Duke University Health System. This includes the only 2 hospitals in Durham, North Carolina (population 245,475) and 1 hospital in Raleigh, North Carolina (population 431,746).

We also describe basic demographic data, payor status, and medical comorbidities for this cohort of patients. Payor status is defined as the most frequently reported payor type prior to care‐plan implementation. Variable direct costs are directly related to patient care and fluctuate with patient volume. They include medications, supplies, laboratory tests, radiology studies, and nursing salaries. They are a proportion of total costs for an ED visit or hospitalization, excluding fixed and indirect costs, such as administrator or physician salaries, utilities, facilities, and equipment.

Primary and secondary outcomes were analyzed using descriptive statistics. Continuous outcomes are summarized with mean (standard deviation) and median (range), whereas categorical outcomes are summarized with N (%). LOS is calculated as the average number of days in the hospital per hospital admission per patient. The time periods of 12 months prior, 6 months prior, 6 months after, and 12 months after care‐plan implementation were examined. Only patients with 6 or more months of postcare‐plan data are included in the 6‐month comparison, and only patients with 12 or more months of postcare‐plan data are included in the 12‐month comparison. One patient in the 6‐month comparison group died very soon after care‐plan implementation, so that patient is included in Table 1 (N=24) but excluded from outcome analyses in Tables 2 and 3 (N=23). Differences between 6 months pre and 6 months postcare plan, and 12 months pre and 12 months postcare plan were examined using the Wilcoxon signed rank test for nonparametric matched data. Mean change is calculated as ([Post‐Pre]/Pre) for each patient, and then averaged across all patients. Mean percentage change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. It was done this way to emphasize the effect on the patient level. No adjustments were made for multiple comparisons. All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC). This study was granted exempt status by the Duke University Institutional Review Board.

Patient Demographics and Comorbidities
 Patients With Care Plans, N=24Patients With 12 Months PostCare Plan Follow‐up, N=12Patients With 6 Months PostCare Plan Follow‐up, N=23*
  • NOTE: Abbreviations: SD, standard deviation. *One patient died soon after care‐plan implementation; therefore N=23. Most frequently reported insurance type precare‐plan start date. Patients can have more than 1 comorbidity; therefore, numbers do not add up to N=24.

Age, y, mean (SD)38.5 (11.7)41.6 (9.2)37.3 (10.5)
Median (range)36 (2565)41 (2858)36 (2558)
Gender, N (%)   
Male11 (46%)5 (42%)11 (48%)
Female13 (54%)7 (58%)12 (52%)
Payor, N (%)   
Medicare11 (46%)6 (50%)10 (43%)
Medicaid9 (38%)4 (33%)9 (39%)
Medicare and Medicaid0 (0%)0 (0%)0 (0%)
Private insurance2 (8%)1 (8%)2 (9%)
None1 (4%)0 (0%)1 (4%)
Other1 (4%)1 (8%)1 (4%)
Comorbidities, N (%)   
Asthma9 (38%)5 (42%)9 (39%)
Chronic obstructive pulmonary disease2 (8%)2 (17%)2 (9%)
Chronic pain20 (83%)12 (100%)20 (87%)
Coronary artery disease5 (21%)4 (33%)5 (22%)
Diabetes mellitus10 (42%)6 (50%)9 (39%)
End‐stage renal disease4 (17%)4 (33%)4 (17%)
Heart failure5 (21%)2 (17%)4 (17%)
Hypertension13 (54%)6 (50%)12 (52%)
Mental health/substance abuse23 (96%)12 (100%)22 (96%)
Sickle cell10 (42%)5 (42%)10 (43%)
Utilization Patterns Before and After Care‐Plan Implementation Across Duke University Health System*
 6 Months Pre Care Plan6 Months Post Care Plan12 Months Pre Care Plan12 Months Post Care Plan6‐Month Change6‐Month P Value12‐Month Change12‐Month P Value
  • NOTE: Abbreviations: ED, emergency department; LOS, length of stay; SD, standard deviation. *Duke University Health System includes Duke University Hospital, Duke Regional Hospital, and Duke Raleigh Hospital. Mean percent change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. Mean change is calculated as Post‐Pre for each patient, and then averaged across patients. Wilcoxon signed rank test.

Admissions     <0.001 0.003
N2323121223231212
Total145561315856.0% (41.6%) 50.5% (43.9%) 
Mean (SD)6.3 (3.8)2.4 (2.4)10.9 (6.3)4.8 (4.2)3.9 (3.76) 6.1 (6.02) 
Median (range)5 (114)2 (08)8 (320)3 (011)    
30‐day readmissions    <0.001 0.002
N2323121223231212
Total130441064566.0% (32.4%) 51.5% (32.0%) 
Mean (SD)5.7 (4.1)1.9 (2.4)8.8 (7.0)3.8 (2.7)3.7 (3.79) 5.1 (5.71) 
Median (range)4 (013)1 (08)6 (019)3 (011)    
Inpatient LOS     0.506 0.910
N2323121223231212
Total76635866531750.8% (51.4%) 37.8% (78.8%) 
Mean (SD)5.0 (3.2)4.7 (4.3)4.7 (1.5)4.4 (3.1)0.3 (4.3) 0.3 (2.27) 
Median (range)4.3 (1.515.8)4 (016)4.8 (2.26.9)3.7 (09)    
ED visits     0.836 0.941
N2323121223231212
Total183198185307+42.9% (148.4%) +48.4% (145.1%) 
Mean (SD)8.0 (11.5)8.6 (19.8)15.4 (14.7)25.6 (54.4)0.7 (11.92) 10.2 (43.19) 
Median (range)5 (050)3 (096)12 (150)7 (1196)    
Healthcare Costs Before and After Care‐Plan Implementation Across Duke University Health System*
 6 Months Pre Care Plan6 Months Post Care Plan12 Months Pre Care Plan12 Months Post Care Plan6‐Month Change6‐Month P Value12‐Month Change12‐Month P Value
  • NOTE: Abbreviations: ED, emergency department; SD, standard deviation. *Duke University Health System includes Duke University Hospital, Duke Regional Hospital, and Duke Raleigh Hospital. Mean percent change is calculated as ([Post‐Pre]/Pre)*100 for each patient, and then averaged across patients. Mean change is calculated as Post‐Pre for each patient, and then averaged across patients. Wilcoxon signed rank test.

Inpatient costs ($)    0.001 0.052
N2323121223231212
Total686,612.43358,520.42538,579.90299,501.0347.7% (52.3%) 35.8% (76.1%) 
Mean (SD)29,852.71 (21,808.22)15,587.84 (21,141.79)44,881.66 (30,132.26)24,958.42 (27,248.41)14,264.9 (19,301.75) 19,923.2 (31,891.69) 
Median (range)30,203.43 (1,625.1880,171.87)7,041.28 (086,457.05)39,936.05 (8,237.5382,861.11)13,321.56 (082,309.19)    
ED costs ($)     0.143 0.850
N2323121223231212
Total80,105.3460,500.3882,473.8698,298.84+12.5% (147.5%) +48.0% (161.8%) 
Mean (SD)3,482.84 (4,423.57)2,630.45 (4,782.56)6,872.82 (5,633.70)8,191.57 (13,974.75)852.4 (2,780.01) 1,318.7 (10,348.89) 
Median (range)2,239.19 (019,492.03)1,163.45 (022,449.84)5,924.31 (277.3019,492.03)3,002.70 (553.7250,955.56)    
Combined costs ($)     0.002 0.129
N2323121223231212
Total766,717.77419,020.80621,053.76397,799.8745.3% (48.3%) 25.5% (76.9%) 
Mean (SD)33,335.56 (22,427.77)18,218.30 (21,398.27)51,754.48 (32,248.94)33,149.99 (31,769.40)15,117.3 (19,932.41) 18,604.5 (35,513.56) 
Median (range)32,000.42 (1,625.1880,611.70)9,088.88 (087,549.37)45,716.08 (10,874.0599,426.72)23,971.85 (553.7285,440.12)    

RESULTS

Table 1 shows the demographics and comorbidities for the 24 patients with care plans included in this analysis. The average age of patients is 38.5 years (range, 2565 years) and a nearly even split between males (11) and females (13). Chronic disease burden is high. Furthermore, 83% of patients have chronic pain and 96% have mental health problems or substance abuse.

Table 2 shows inpatient and ED utilization patterns before and after care‐plan implementation. Inpatient admissions decreased by 56% for the 6 months after care‐plan implementation (P<0.001) and by 50.5% for the 12 months after care‐plan implementation (P=0.003). This translates to a decrease in the average number of admissions per patient from 6.3 to 2.4, 6 months postcare plan, and from 10.9 to 4.8, 12 months postcare plan.

Thirty‐day readmissions also significantly decreased after care‐plan implementation. Among the 23 patients with data 6 months pre and postcare plan, there were 130 readmissions before and 44 readmissions after care‐plan implementation, a 66% reduction (P<0.001). Among the 12 patients with data 12 months pre and postcare plan, there were 106 readmissions before and 45 readmissions after care‐plan implementation, a 51.5% reduction (P=0.002). Inpatient LOS did not show a statistically significant change after care‐plan implementation.

ED visits were similar for the 6 months pre compared to 6 months postcare plan. ED visits at 12 months postcare plan increased from an average of 15.4 visits pre to 25.6 visits per patient postcare plan. This was driven by a single homeless patient with dialysis‐dependent end‐stage renal disease, who had 134 ED visits in the 12 months after careplan implementation. Analysis of the data with this outlier removed showed a reduction in ED visits from an average of 12.3 visits per patient to 10.1 visits per patient in the 12 months postcare plan; however, this was not statistically significant (P=0.66, data not shown).

Table 3 shows inpatient and ED VDCs before and after care‐plan implementation. The average VDCs per patient per admission decreased from $29,852.71 to $15,587.84, 6 months after care‐plan implementation, a 47.7% reduction (P=0.001). The average VDCs per patient per admission decreased from $44,881.66 to $24,958.42, 12 months after care‐plan implementation, a 35.8% reduction (P=0.052). ED costs did not show a statistically significant decrease. However, with the outlier removed as above, costs did decrease by 12.3%, 6 months after care‐plan implementation, approaching statistical significance (P=0.073, data not shown). Combined inpatient and ED variable direct costs decreased by an average of $15,117.30, 6 months after care‐plan implementation, a 45.3% reduction (P=0.002), and by an average of $18,604.50, 12 months after care‐plan implementation, a 25.5% reduction, although this did not reach statistical significance (P=0.129).

DISCUSSION

A multidisciplinary team at our academic medical center developed individualized care plans tailored to the specific medical and psychosocial complexities of high utilizers to reduce unnecessary service utilization and hospital costs. Postintervention analysis shows reduced inpatient admissions and 30‐day readmissions among this population by 50%. Furthermore, inpatient variable direct costs decreased by 47% for the 6 months following care‐plan implementation and by 35% for the 12 months following care‐plan implementation. This translates into a $347,696.97 cost savings for the 23 patients 6 months after care‐plan implementation, and a $223,253.89 cost savings for the 12 patients 12 months after care‐plan implementation. This reduction in utilization and cost was seen across all 3 hospitals in the Duke University Health System, including the only 2 hospitals in Durham, North Carolina. Unlike other urban areas, public transportation in our region is scarce, and the options for hospital shopping in central North Carolina are relatively limited. Although this study does not measure utilization in surrounding counties, we do not feel this occurred as we did not see a rise in requests for medical records nor attempts to contact Duke providers for questions on these patients as a result of our intervention. This, along with our regional health system outcome analysis, provides support that our intervention did not cause patients to seek care elsewhere and result in cost‐shifting to other facilities.

We hypothesize that our care plans may be responsible for decreased admissions and 30‐day readmissions through several mechanisms. By raising awareness of these patients' excessive hospital utilization patterns and making this information readily available through our EMR, providers in the ED may be more conscientious about their admission decisions. Problems that at face value seem acute, are often more chronic and can be better managed in the outpatient setting. Several care plans also explicitly recommend limiting unnecessary intravenous opiate use for chronic pain patients. Other patients who have frequent admissions actually have end‐stage disease, and care plans for these patients help facilitate referrals to hospice programs.

Care plans provide a consistent message of patient histories, utilization patterns, and management strategies, and also serve as a communication tool between hospitalists and ED providers. A systematic review of all ED‐based interventions for high utilizers revealed that most studies did show a reduction in ED visits, but all incorporated case management programs to do so.[15] We did not reduce ED visits, possibly because we lacked the resources and care coordination a community‐based case management program provides. However, care plans did serve as a platform with which hospitalists and ED providers can help coordinate care among multiple outpatient providers. This has potentially limited admissions by providing a referral destination or outpatient point of contact for ED providers. For example, as a result of our intervention, referral mechanisms to our comprehensive pain clinic and outpatient psychiatry clinic have both been strengthened and streamlined. The fact that care plans decreased admissions and readmissions, but not ED visits, suggests that our intervention may not have actually changed patient behavior, but instead changed provider practices in relation to disposition decisions in the ED.

Our QI intervention has several strengths. First, it is fully integrated within our existing healthcare workforce, without the need for an extra case management system. Second, it is seamlessly incorporated into our EMR and represents another potential use of an EMR that has not been previously touted. Third, the multidisciplinary nature of the CCPC ensures that all stakeholders involved in the care of high utilizers are represented. Fourth, the outcome analysis across all 3 hospitals in our health system provides a balancing metric against the notion that our intervention simply caused patients to seek care elsewhere in the region. Last, the QI design and lack of strict inclusion and exclusion criteria adds practicality and shows effectiveness, not just efficacy, of the intervention.

Because this was developed as a QI intervention without strict inclusion and exclusion criteria, generalizability is lacking. In the future, one could use the EMR to more systematically identify high‐utilizing, complex patients. One study showed the ability to use the EMR with a standardized framework to identify hot spotting (high utilizers) and contextual anomaly detection (ie, anomalous utilization cases where patient‐incurred levels of utilization are unexpected given their clinical characteristics).[16] The nonrandomized, retrospective pre/post‐intervention analysis without a control group diminishes the external validity of the results and does introduce the potential for bias.

One of the primary study limitations includes the small sample size of only 24 patients. Admittedly, these first 24 patients are the absolute highest utilizers of care at our hospital, possibly making their utilization patterns more amenable to our intervention. The 96% prevalence rate of mental health and substance abuse in our cohort is significantly higher than other published data among high utilizers.[4, 17, 18] We are continuing to develop care plans for additional high‐utilizing, complex patients, and expect to enroll more patients with end‐stage disease, and relatively fewer with substance abuse or psychiatric illness as time goes on. It is possible this new cohort of patients has proportionally less unnecessary utilization, thus limiting our intervention effect. One final limitation of our study is the lack of care quality and patient safety outcomes. In future studies, health outcomes, adverse events, and outpatient care utilization will be important balancing measures to include.

In conclusion, we showed that a QI intervention using individualized care plans reduces hospital admissions, 30‐day readmissions, and hospital costs across a regional health system for a group of complex, high‐utilizing patients. This intervention can, and should, be developed by a multidisciplinary team and fully integrated into the existing healthcare workforce and EMR to ensure appropriateness, effectiveness, and longevity. Going forward, it will be imperative to evaluate this intervention prospectively, at multiple sites, in coordination with outpatient providers, and including quality and safety outcomes to determine if this hospital‐based intervention impacts care coordination, utilization rates, cost, and health outcomes across the broader healthcare system.

Disclosure

Nothing to report.

References
  1. Hansagi H, Olsson M, Sjoberg S, Tomson Y, Goransson S. Frequent use of the hospital emergency department is indicative of high use of other health care services. Ann Emerg Med. 2001;37:561567.
  2. Matzer F, Wisiak UV, Graninger M, et al. Biopsychosocial health care needs at the emergency room: challenge of complexity. PLoS One. 2012;7:e41775.
  3. Agency for Healthcare Research and Quality. The high concentration of U.S. health care expenditures. Research in Action. Available at: http://meps.ahrq.gov/mepsweb/data_files/publications/ra19/ra19.pdf. Published June 2006. Accessed November 18, 2013.
  4. Byrne M, Murphy AW, Plunkett PK, McGee HM, Murray A, Bury G. Frequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristics. Ann Emerg Med. 2003;41:309318.
  5. Centers for Medicare 8:665671.
  6. Katzelnick DJ, Simon GE, Pearson SD, et al. Randomized trial of a depression management program in high utilizers of medical care. Arch Fam Med. 2000;9:345351.
  7. Badger T, Gelenberg AJ, Berren M. Consultative intervention to improve outcomes of high utilizers in a public mental health system. Perspect Psychiatr Care. 2004;40:5360, 69.
  8. Law DD, Crane DR, Berge JM. The influence of individual, marital, and family therapy on high utilizers of health care. J Marital Fam Ther. 2003;29:353363.
  9. Okin RL, Boccellari A, Azocar F, et al. The effects of clinical case management on hospital service use among ED frequent users. Am J Emerg Med. 2000;18:603608.
  10. For success with frequent ED utilizers, take steps to understand patient needs, connect them with appropriate resources. ED Manag. 2013;25:5759.
  11. ED diversion: multidisciplinary approach engages high utilizers, helps them better navigate the health care system. ED Manag. 2011;23:127130.
  12. CM program keeps high utilizers out of hospital. Hosp Case Manag. 2012;20:108109.
  13. Hilger R, Quirk R, Dahms R. Use of restriction care plans to decrease medically unnecessary admissions and emergency department visits. J Hosp Med. 2012;7:S2.
  14. Althaus F, Paroz S, Hugli O, et al. Effectiveness of interventions targeting frequent users of emergency departments: a systematic review. Ann Emerg Med. 2011;58:4152.
  15. Hu J, Wang F, Sun J, Sorrentino R, Ebadollahi S. A healthcare utilization analysis framework for hot spotting and contextual anomaly detection. AMIA Annu Symp Proc. 2012;2012:360369.
  16. Pasic J, Russo J, Roy‐Byrne P. High utilizers of psychiatric emergency services. Psychiatr Serv. 2005;56:678684.
  17. Henk HJ, Katzelnick DJ, Kobak KA, Greist JH, Jefferson JW. Medical costs attributed to depression among patients with a history of high medical expenses in a health maintenance organization. Arch Gen Psychiatry. 1996;53:899904.
References
  1. Hansagi H, Olsson M, Sjoberg S, Tomson Y, Goransson S. Frequent use of the hospital emergency department is indicative of high use of other health care services. Ann Emerg Med. 2001;37:561567.
  2. Matzer F, Wisiak UV, Graninger M, et al. Biopsychosocial health care needs at the emergency room: challenge of complexity. PLoS One. 2012;7:e41775.
  3. Agency for Healthcare Research and Quality. The high concentration of U.S. health care expenditures. Research in Action. Available at: http://meps.ahrq.gov/mepsweb/data_files/publications/ra19/ra19.pdf. Published June 2006. Accessed November 18, 2013.
  4. Byrne M, Murphy AW, Plunkett PK, McGee HM, Murray A, Bury G. Frequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristics. Ann Emerg Med. 2003;41:309318.
  5. Centers for Medicare 8:665671.
  6. Katzelnick DJ, Simon GE, Pearson SD, et al. Randomized trial of a depression management program in high utilizers of medical care. Arch Fam Med. 2000;9:345351.
  7. Badger T, Gelenberg AJ, Berren M. Consultative intervention to improve outcomes of high utilizers in a public mental health system. Perspect Psychiatr Care. 2004;40:5360, 69.
  8. Law DD, Crane DR, Berge JM. The influence of individual, marital, and family therapy on high utilizers of health care. J Marital Fam Ther. 2003;29:353363.
  9. Okin RL, Boccellari A, Azocar F, et al. The effects of clinical case management on hospital service use among ED frequent users. Am J Emerg Med. 2000;18:603608.
  10. For success with frequent ED utilizers, take steps to understand patient needs, connect them with appropriate resources. ED Manag. 2013;25:5759.
  11. ED diversion: multidisciplinary approach engages high utilizers, helps them better navigate the health care system. ED Manag. 2011;23:127130.
  12. CM program keeps high utilizers out of hospital. Hosp Case Manag. 2012;20:108109.
  13. Hilger R, Quirk R, Dahms R. Use of restriction care plans to decrease medically unnecessary admissions and emergency department visits. J Hosp Med. 2012;7:S2.
  14. Althaus F, Paroz S, Hugli O, et al. Effectiveness of interventions targeting frequent users of emergency departments: a systematic review. Ann Emerg Med. 2011;58:4152.
  15. Hu J, Wang F, Sun J, Sorrentino R, Ebadollahi S. A healthcare utilization analysis framework for hot spotting and contextual anomaly detection. AMIA Annu Symp Proc. 2012;2012:360369.
  16. Pasic J, Russo J, Roy‐Byrne P. High utilizers of psychiatric emergency services. Psychiatr Serv. 2005;56:678684.
  17. Henk HJ, Katzelnick DJ, Kobak KA, Greist JH, Jefferson JW. Medical costs attributed to depression among patients with a history of high medical expenses in a health maintenance organization. Arch Gen Psychiatry. 1996;53:899904.
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The highest utilizers of care: Individualized care plans to coordinate care, improve healthcare service utilization, and reduce costs at an academic tertiary care center
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Address for correspondence and reprint requests: Noppon Setji, MD, Duke University Medical Center, PO Box 100800, Durham, NC 27710; Telephone: 919‐681‐8263; Fax: 919‐668‐5394; E‐mail: noppon.setji@duke.edu
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Left Atrial Appendage Closure Noninferior to Warfain for Cardioembolic Event Prophylaxis in Nonvalvular Afibrillation

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Left Atrial Appendage Closure Noninferior to Warfain for Cardioembolic Event Prophylaxis in Nonvalvular Afibrillation

Clinical question: Is mechanical, left atrial appendage (LAA) closure as effective as warfarin therapy in preventing cardioembolic events in patients with nonvalvular atrial fibrillation (Afib)?

Background: Anticoagulation with warfarin has long been the standard therapy for prevention of thromboembolic complications of nonvalvular Afib; however, its use is limited by the need for monitoring and lifelong adherence, as well as its many dietary and medication interactions. Prior studies investigating the efficacy of a deployable device intended to close the LAA have shown noninferiority of the device when compared with standard warfarin anticoagulation. This study evaluated LAA closure device efficacy after a 3.8-year interval.

Study design: Randomized, unblinded controlled trial.

Setting: Fifty-nine centers in the U.S. and Europe.

Synopsis: Authors randomized 707 participants 18 years or older with nonvalvular Afib and CHADS2 score ≥1 in a 2:1 fashion to the intervention and warfarin therapy groups. The primary outcome was a composite endpoint including stroke, systemic embolism, and cardiovascular or unexplained death. The event rate in the device group was 2.3 per 100 patient-years, compared with 3.8 in the warfarin group. Rate ratio was 0.60, meeting noninferiority criteria. Primary safety events were not statistically different.

Although the authors concluded that LAA device closure was noninferior to warfarin therapy, it should be noted that there was a high dropout rate, especially in the warfarin group, motivated either by a desire to try a novel oral anticoagulant or the perception that warfarin therapy was not beneficial. It should also be noted that device placement involved not only a percutaneous procedure, but also 45 days of aspirin and warfarin therapy initially to promote endothelization, followed by six months of clopidogrel.

Bottom line: Percutaneous device closure of the LAA appears to be noninferior to warfarin therapy in the prevention of cardioembolic events over a period of several years, and might be superior.

Citation: Reddy VY, Sievert H, Halperin J, et al. Percutaneous left atrial appendage closure vs warfarin for atrial fibrillation: a randomized clinical trial. JAMA. 2014;312(19):1988-1998.

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Clinical question: Is mechanical, left atrial appendage (LAA) closure as effective as warfarin therapy in preventing cardioembolic events in patients with nonvalvular atrial fibrillation (Afib)?

Background: Anticoagulation with warfarin has long been the standard therapy for prevention of thromboembolic complications of nonvalvular Afib; however, its use is limited by the need for monitoring and lifelong adherence, as well as its many dietary and medication interactions. Prior studies investigating the efficacy of a deployable device intended to close the LAA have shown noninferiority of the device when compared with standard warfarin anticoagulation. This study evaluated LAA closure device efficacy after a 3.8-year interval.

Study design: Randomized, unblinded controlled trial.

Setting: Fifty-nine centers in the U.S. and Europe.

Synopsis: Authors randomized 707 participants 18 years or older with nonvalvular Afib and CHADS2 score ≥1 in a 2:1 fashion to the intervention and warfarin therapy groups. The primary outcome was a composite endpoint including stroke, systemic embolism, and cardiovascular or unexplained death. The event rate in the device group was 2.3 per 100 patient-years, compared with 3.8 in the warfarin group. Rate ratio was 0.60, meeting noninferiority criteria. Primary safety events were not statistically different.

Although the authors concluded that LAA device closure was noninferior to warfarin therapy, it should be noted that there was a high dropout rate, especially in the warfarin group, motivated either by a desire to try a novel oral anticoagulant or the perception that warfarin therapy was not beneficial. It should also be noted that device placement involved not only a percutaneous procedure, but also 45 days of aspirin and warfarin therapy initially to promote endothelization, followed by six months of clopidogrel.

Bottom line: Percutaneous device closure of the LAA appears to be noninferior to warfarin therapy in the prevention of cardioembolic events over a period of several years, and might be superior.

Citation: Reddy VY, Sievert H, Halperin J, et al. Percutaneous left atrial appendage closure vs warfarin for atrial fibrillation: a randomized clinical trial. JAMA. 2014;312(19):1988-1998.

Clinical question: Is mechanical, left atrial appendage (LAA) closure as effective as warfarin therapy in preventing cardioembolic events in patients with nonvalvular atrial fibrillation (Afib)?

Background: Anticoagulation with warfarin has long been the standard therapy for prevention of thromboembolic complications of nonvalvular Afib; however, its use is limited by the need for monitoring and lifelong adherence, as well as its many dietary and medication interactions. Prior studies investigating the efficacy of a deployable device intended to close the LAA have shown noninferiority of the device when compared with standard warfarin anticoagulation. This study evaluated LAA closure device efficacy after a 3.8-year interval.

Study design: Randomized, unblinded controlled trial.

Setting: Fifty-nine centers in the U.S. and Europe.

Synopsis: Authors randomized 707 participants 18 years or older with nonvalvular Afib and CHADS2 score ≥1 in a 2:1 fashion to the intervention and warfarin therapy groups. The primary outcome was a composite endpoint including stroke, systemic embolism, and cardiovascular or unexplained death. The event rate in the device group was 2.3 per 100 patient-years, compared with 3.8 in the warfarin group. Rate ratio was 0.60, meeting noninferiority criteria. Primary safety events were not statistically different.

Although the authors concluded that LAA device closure was noninferior to warfarin therapy, it should be noted that there was a high dropout rate, especially in the warfarin group, motivated either by a desire to try a novel oral anticoagulant or the perception that warfarin therapy was not beneficial. It should also be noted that device placement involved not only a percutaneous procedure, but also 45 days of aspirin and warfarin therapy initially to promote endothelization, followed by six months of clopidogrel.

Bottom line: Percutaneous device closure of the LAA appears to be noninferior to warfarin therapy in the prevention of cardioembolic events over a period of several years, and might be superior.

Citation: Reddy VY, Sievert H, Halperin J, et al. Percutaneous left atrial appendage closure vs warfarin for atrial fibrillation: a randomized clinical trial. JAMA. 2014;312(19):1988-1998.

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Tramadol Associated with Increased Rate of Hypoglycemia

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Tramadol Associated with Increased Rate of Hypoglycemia

Clinical question: Does tramadol increase rates of hospitalization from hypoglycemia compared to other opioids?

Background: As tramadol use has increased in the general population, there have been multiple reports of hypoglycemia after initiation of the painkiller, including in patients with no other known risk factors, such as diabetes mellitus.

Study design: Case control study.

Setting: United Kingdom.

Synopsis: Using the United Kingdom’s Clinical Practice Research Datalink, a cohort of 334,034 patients was identified, including 1,105 hospitalized for hypoglycemia. To compare incidence of hypoglycemia in patients taking tramadol versus nontramadol opioids, patients newly treated with tramadol for noncancer pain were compared with those treated with codeine.

Use of tramadol was associated with increase in hospitalization for treatment of hypoglycemia compared with codeine. Specifically, tramadol use had an odds ratio (OR) of 1.52 (95% confidence interval, 1.09-2.10). The risk of hypoglycemia was higher in the first 30 days of use, with an OR of 2.61 (95% confidence interval, 1.61-4.23).

Since tramadol prescribing has increased over the past 10 years, clinicians should be mindful of the potential association between tramadol and severe hypoglycemia requiring hospitalization. Although the details of the pathophysiology leading to this outcome remain unclear, evidence of a causal relationship is mounting. The association with hypoglycemia was seen particularly in the first 30 days of therapy. The incidence of less severe hypoglycemia not requiring hospitalization remains unknown.

Bottom line: Tramadol use is associated with increased rates of hypoglycemia requiring hospitalization.

Citation: Fournier JP, Azoulay L, Yin H, Montastruc JL, Suissa S. Tramadol use and the risk of hospitalization for hypoglycemia in patients with noncancer pain. JAMA Intern Med. 2015;175(2):186-193.

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Clinical question: Does tramadol increase rates of hospitalization from hypoglycemia compared to other opioids?

Background: As tramadol use has increased in the general population, there have been multiple reports of hypoglycemia after initiation of the painkiller, including in patients with no other known risk factors, such as diabetes mellitus.

Study design: Case control study.

Setting: United Kingdom.

Synopsis: Using the United Kingdom’s Clinical Practice Research Datalink, a cohort of 334,034 patients was identified, including 1,105 hospitalized for hypoglycemia. To compare incidence of hypoglycemia in patients taking tramadol versus nontramadol opioids, patients newly treated with tramadol for noncancer pain were compared with those treated with codeine.

Use of tramadol was associated with increase in hospitalization for treatment of hypoglycemia compared with codeine. Specifically, tramadol use had an odds ratio (OR) of 1.52 (95% confidence interval, 1.09-2.10). The risk of hypoglycemia was higher in the first 30 days of use, with an OR of 2.61 (95% confidence interval, 1.61-4.23).

Since tramadol prescribing has increased over the past 10 years, clinicians should be mindful of the potential association between tramadol and severe hypoglycemia requiring hospitalization. Although the details of the pathophysiology leading to this outcome remain unclear, evidence of a causal relationship is mounting. The association with hypoglycemia was seen particularly in the first 30 days of therapy. The incidence of less severe hypoglycemia not requiring hospitalization remains unknown.

Bottom line: Tramadol use is associated with increased rates of hypoglycemia requiring hospitalization.

Citation: Fournier JP, Azoulay L, Yin H, Montastruc JL, Suissa S. Tramadol use and the risk of hospitalization for hypoglycemia in patients with noncancer pain. JAMA Intern Med. 2015;175(2):186-193.

Clinical question: Does tramadol increase rates of hospitalization from hypoglycemia compared to other opioids?

Background: As tramadol use has increased in the general population, there have been multiple reports of hypoglycemia after initiation of the painkiller, including in patients with no other known risk factors, such as diabetes mellitus.

Study design: Case control study.

Setting: United Kingdom.

Synopsis: Using the United Kingdom’s Clinical Practice Research Datalink, a cohort of 334,034 patients was identified, including 1,105 hospitalized for hypoglycemia. To compare incidence of hypoglycemia in patients taking tramadol versus nontramadol opioids, patients newly treated with tramadol for noncancer pain were compared with those treated with codeine.

Use of tramadol was associated with increase in hospitalization for treatment of hypoglycemia compared with codeine. Specifically, tramadol use had an odds ratio (OR) of 1.52 (95% confidence interval, 1.09-2.10). The risk of hypoglycemia was higher in the first 30 days of use, with an OR of 2.61 (95% confidence interval, 1.61-4.23).

Since tramadol prescribing has increased over the past 10 years, clinicians should be mindful of the potential association between tramadol and severe hypoglycemia requiring hospitalization. Although the details of the pathophysiology leading to this outcome remain unclear, evidence of a causal relationship is mounting. The association with hypoglycemia was seen particularly in the first 30 days of therapy. The incidence of less severe hypoglycemia not requiring hospitalization remains unknown.

Bottom line: Tramadol use is associated with increased rates of hypoglycemia requiring hospitalization.

Citation: Fournier JP, Azoulay L, Yin H, Montastruc JL, Suissa S. Tramadol use and the risk of hospitalization for hypoglycemia in patients with noncancer pain. JAMA Intern Med. 2015;175(2):186-193.

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Longer Surgeries Associated with Increased VTE Risk

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Longer Surgeries Associated with Increased VTE Risk

Clinical question: Does duration of surgical procedure influence venous thromboembolism (VTE) risk?

Background: The relationship between surgical procedure length and VTE risk has not been vigorously assessed, although it has been postulated that longer procedures are associated with increased VTE risk. Improved understanding of this relationship may be beneficial to surgeons deciding on VTE prophylaxis strategies or determining whether to perform coupled procedures.

Study design: Retrospective cohort study.

Setting: Data collected from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP).

Synopsis: Study authors divided 1,432,855 surgical cases during which general anesthesia was administered for a specified duration into five quintiles based on length of operative time, defined as the period during which a patient was under general anesthesia. The primary outcome was the development of a VTE within 30 days of the procedure, defined as deep venous thrombosis (DVT), pulmonary embolism (PE), or both. Logistic regression analyses were performed to assess the relationship between procedure length and VTE occurrence.

The middle quintile of procedures carried a VTE rate of 0.86%. There was a significant association between procedure duration and VTE risk when the first and second quintiles, and fourth and fifth quintiles, were compared to the middle quintile. The association was present across all surgical subspecialties.

Bottom line: Longer duration of surgical procedures is associated with increased VTE risk.

Citation: Kim JY, Khavanin N, Rambachan A, et al. Surgical duration and risk of venous thromboembolism [published online ahead of print December 3, 2014]. JAMA Surg. doi:10.1001/jamasurg.2014.1841.

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Clinical question: Does duration of surgical procedure influence venous thromboembolism (VTE) risk?

Background: The relationship between surgical procedure length and VTE risk has not been vigorously assessed, although it has been postulated that longer procedures are associated with increased VTE risk. Improved understanding of this relationship may be beneficial to surgeons deciding on VTE prophylaxis strategies or determining whether to perform coupled procedures.

Study design: Retrospective cohort study.

Setting: Data collected from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP).

Synopsis: Study authors divided 1,432,855 surgical cases during which general anesthesia was administered for a specified duration into five quintiles based on length of operative time, defined as the period during which a patient was under general anesthesia. The primary outcome was the development of a VTE within 30 days of the procedure, defined as deep venous thrombosis (DVT), pulmonary embolism (PE), or both. Logistic regression analyses were performed to assess the relationship between procedure length and VTE occurrence.

The middle quintile of procedures carried a VTE rate of 0.86%. There was a significant association between procedure duration and VTE risk when the first and second quintiles, and fourth and fifth quintiles, were compared to the middle quintile. The association was present across all surgical subspecialties.

Bottom line: Longer duration of surgical procedures is associated with increased VTE risk.

Citation: Kim JY, Khavanin N, Rambachan A, et al. Surgical duration and risk of venous thromboembolism [published online ahead of print December 3, 2014]. JAMA Surg. doi:10.1001/jamasurg.2014.1841.

Clinical question: Does duration of surgical procedure influence venous thromboembolism (VTE) risk?

Background: The relationship between surgical procedure length and VTE risk has not been vigorously assessed, although it has been postulated that longer procedures are associated with increased VTE risk. Improved understanding of this relationship may be beneficial to surgeons deciding on VTE prophylaxis strategies or determining whether to perform coupled procedures.

Study design: Retrospective cohort study.

Setting: Data collected from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP).

Synopsis: Study authors divided 1,432,855 surgical cases during which general anesthesia was administered for a specified duration into five quintiles based on length of operative time, defined as the period during which a patient was under general anesthesia. The primary outcome was the development of a VTE within 30 days of the procedure, defined as deep venous thrombosis (DVT), pulmonary embolism (PE), or both. Logistic regression analyses were performed to assess the relationship between procedure length and VTE occurrence.

The middle quintile of procedures carried a VTE rate of 0.86%. There was a significant association between procedure duration and VTE risk when the first and second quintiles, and fourth and fifth quintiles, were compared to the middle quintile. The association was present across all surgical subspecialties.

Bottom line: Longer duration of surgical procedures is associated with increased VTE risk.

Citation: Kim JY, Khavanin N, Rambachan A, et al. Surgical duration and risk of venous thromboembolism [published online ahead of print December 3, 2014]. JAMA Surg. doi:10.1001/jamasurg.2014.1841.

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Mortality, Readmission Rates Unchanged by Duty Hour Reforms

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Mortality, Readmission Rates Unchanged by Duty Hour Reforms

Clinical question: Did the 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour reforms change mortality rates or readmission rates at teaching hospitals?

Background: The 2011 ACGME duty hour reforms maintained the 80-hour weekly work limit for medical residents, decreased the number of continuous hours to 16 hours from 30 hours for interns, and decreased the number of continuous hours for residents to 24 hours, with an additional four hours allowed for transitions of care. These changes have raised concerns about increased handoffs and potential changes in patient safety.

Study design: Observational study of Medicare admissions before and after duty hour reforms.

Setting: Short-term, acute-care hospitals.

Synopsis: Investigators compared 4,325,854 inpatient Medicare admissions from the two years prior to duty hour reforms with 2,058,419 admissions the year after the reforms. For each time period, the 30-day mortality and 30-day readmission rates were assessed; outcomes from more intensive teaching hospitals were compared with the outcomes from less intensive teaching hospitals. Teaching intensity was assessed according to the resident-to-bed ratio, a measure that has been used in prior research.

No significant differences were found in the primary outcomes of 30-day all-location mortality or 30-day all-cause readmissions.

When looking at specific diagnoses, only stroke was found to have a higher risk of readmission in the post-reform period (OR 1.06, 95% CI 1.01-1.13).

Although 2011 duty hour reforms represented a large, national structural change in resident education, no significant positive or negative effect was found on these important patient safety measures, consistent with what has been found in prior reviews.

Bottom line: The 2011 ACGME duty hour reforms showed no significant changes in mortality or readmissions when comparing hospitals with intensive teaching roles to those with fewer trainees.

Citation: Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA. 2014;312(22):2364-2373.

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Clinical question: Did the 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour reforms change mortality rates or readmission rates at teaching hospitals?

Background: The 2011 ACGME duty hour reforms maintained the 80-hour weekly work limit for medical residents, decreased the number of continuous hours to 16 hours from 30 hours for interns, and decreased the number of continuous hours for residents to 24 hours, with an additional four hours allowed for transitions of care. These changes have raised concerns about increased handoffs and potential changes in patient safety.

Study design: Observational study of Medicare admissions before and after duty hour reforms.

Setting: Short-term, acute-care hospitals.

Synopsis: Investigators compared 4,325,854 inpatient Medicare admissions from the two years prior to duty hour reforms with 2,058,419 admissions the year after the reforms. For each time period, the 30-day mortality and 30-day readmission rates were assessed; outcomes from more intensive teaching hospitals were compared with the outcomes from less intensive teaching hospitals. Teaching intensity was assessed according to the resident-to-bed ratio, a measure that has been used in prior research.

No significant differences were found in the primary outcomes of 30-day all-location mortality or 30-day all-cause readmissions.

When looking at specific diagnoses, only stroke was found to have a higher risk of readmission in the post-reform period (OR 1.06, 95% CI 1.01-1.13).

Although 2011 duty hour reforms represented a large, national structural change in resident education, no significant positive or negative effect was found on these important patient safety measures, consistent with what has been found in prior reviews.

Bottom line: The 2011 ACGME duty hour reforms showed no significant changes in mortality or readmissions when comparing hospitals with intensive teaching roles to those with fewer trainees.

Citation: Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA. 2014;312(22):2364-2373.

Clinical question: Did the 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour reforms change mortality rates or readmission rates at teaching hospitals?

Background: The 2011 ACGME duty hour reforms maintained the 80-hour weekly work limit for medical residents, decreased the number of continuous hours to 16 hours from 30 hours for interns, and decreased the number of continuous hours for residents to 24 hours, with an additional four hours allowed for transitions of care. These changes have raised concerns about increased handoffs and potential changes in patient safety.

Study design: Observational study of Medicare admissions before and after duty hour reforms.

Setting: Short-term, acute-care hospitals.

Synopsis: Investigators compared 4,325,854 inpatient Medicare admissions from the two years prior to duty hour reforms with 2,058,419 admissions the year after the reforms. For each time period, the 30-day mortality and 30-day readmission rates were assessed; outcomes from more intensive teaching hospitals were compared with the outcomes from less intensive teaching hospitals. Teaching intensity was assessed according to the resident-to-bed ratio, a measure that has been used in prior research.

No significant differences were found in the primary outcomes of 30-day all-location mortality or 30-day all-cause readmissions.

When looking at specific diagnoses, only stroke was found to have a higher risk of readmission in the post-reform period (OR 1.06, 95% CI 1.01-1.13).

Although 2011 duty hour reforms represented a large, national structural change in resident education, no significant positive or negative effect was found on these important patient safety measures, consistent with what has been found in prior reviews.

Bottom line: The 2011 ACGME duty hour reforms showed no significant changes in mortality or readmissions when comparing hospitals with intensive teaching roles to those with fewer trainees.

Citation: Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA. 2014;312(22):2364-2373.

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Emergency Department Utilization May Be Lower for Attending-Only Physician Visits versus Supervised Visits

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Emergency Department Utilization May Be Lower for Attending-Only Physician Visits versus Supervised Visits

Clinical question: Does supervised learning in the ED lead to higher rates of resource utilization?

Background: Care at academic medical centers might be more expensive than nonteaching hospitals because of the increased testing and resource utilization that occurs among learners. Although there is a growing emphasis in training programs on cost-conscious care, little data has looked at resource use as an outcome.

Study design: Cross-sectional study of the National Hospital Ambulatory Medical Care Survey in 2010.

Setting: Probability sample of American EDs and ED visits.

Synopsis: Using the 2010 National Hospital Ambulatory Medical Care Survey ED sub-file, a probability sample of 29,182 ED visits was obtained—25,808 attending-only visits and 3,374 supervised visits.

Supervised visits were more likely to lead to hospital admissions (21% versus 14%), advanced imaging (28% vs. 21%), and a longer median ED stay, but not with more blood testing than attending-only ED visits. EDs were placed into three categories: “nonteaching”; “minor teaching,” where trainees are involved in fewer than 50% of visits; and “major teaching,” where trainees are involved in more than 50% of visits. Study results showed no increase in resource utilization in major teaching EDs, except for an increase in ED length of stay.

Although there was an attempt to adjust for biased selection and complexity, there is a risk that biased selection of “teaching cases” in minor teaching EDs could explain some of the higher resource use for these cases. This study does not imply causation; however, it suggests that further studies might be warranted to evaluate the relationship between learners and resource utilization.

Bottom line: Supervised visits were associated with increased hospital admissions, advanced imaging, and longer ED length of stay (LOS), but other than LOS, this relationship did not persist in major teaching EDs.

Citation: Pitts SR, Morgan SR, Schrager JD, Berger TJ. Emergency department resource use by supervised residents vs attending physicians alone. JAMA. 2014;312(22):2394-2400.

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Clinical question: Does supervised learning in the ED lead to higher rates of resource utilization?

Background: Care at academic medical centers might be more expensive than nonteaching hospitals because of the increased testing and resource utilization that occurs among learners. Although there is a growing emphasis in training programs on cost-conscious care, little data has looked at resource use as an outcome.

Study design: Cross-sectional study of the National Hospital Ambulatory Medical Care Survey in 2010.

Setting: Probability sample of American EDs and ED visits.

Synopsis: Using the 2010 National Hospital Ambulatory Medical Care Survey ED sub-file, a probability sample of 29,182 ED visits was obtained—25,808 attending-only visits and 3,374 supervised visits.

Supervised visits were more likely to lead to hospital admissions (21% versus 14%), advanced imaging (28% vs. 21%), and a longer median ED stay, but not with more blood testing than attending-only ED visits. EDs were placed into three categories: “nonteaching”; “minor teaching,” where trainees are involved in fewer than 50% of visits; and “major teaching,” where trainees are involved in more than 50% of visits. Study results showed no increase in resource utilization in major teaching EDs, except for an increase in ED length of stay.

Although there was an attempt to adjust for biased selection and complexity, there is a risk that biased selection of “teaching cases” in minor teaching EDs could explain some of the higher resource use for these cases. This study does not imply causation; however, it suggests that further studies might be warranted to evaluate the relationship between learners and resource utilization.

Bottom line: Supervised visits were associated with increased hospital admissions, advanced imaging, and longer ED length of stay (LOS), but other than LOS, this relationship did not persist in major teaching EDs.

Citation: Pitts SR, Morgan SR, Schrager JD, Berger TJ. Emergency department resource use by supervised residents vs attending physicians alone. JAMA. 2014;312(22):2394-2400.

Clinical question: Does supervised learning in the ED lead to higher rates of resource utilization?

Background: Care at academic medical centers might be more expensive than nonteaching hospitals because of the increased testing and resource utilization that occurs among learners. Although there is a growing emphasis in training programs on cost-conscious care, little data has looked at resource use as an outcome.

Study design: Cross-sectional study of the National Hospital Ambulatory Medical Care Survey in 2010.

Setting: Probability sample of American EDs and ED visits.

Synopsis: Using the 2010 National Hospital Ambulatory Medical Care Survey ED sub-file, a probability sample of 29,182 ED visits was obtained—25,808 attending-only visits and 3,374 supervised visits.

Supervised visits were more likely to lead to hospital admissions (21% versus 14%), advanced imaging (28% vs. 21%), and a longer median ED stay, but not with more blood testing than attending-only ED visits. EDs were placed into three categories: “nonteaching”; “minor teaching,” where trainees are involved in fewer than 50% of visits; and “major teaching,” where trainees are involved in more than 50% of visits. Study results showed no increase in resource utilization in major teaching EDs, except for an increase in ED length of stay.

Although there was an attempt to adjust for biased selection and complexity, there is a risk that biased selection of “teaching cases” in minor teaching EDs could explain some of the higher resource use for these cases. This study does not imply causation; however, it suggests that further studies might be warranted to evaluate the relationship between learners and resource utilization.

Bottom line: Supervised visits were associated with increased hospital admissions, advanced imaging, and longer ED length of stay (LOS), but other than LOS, this relationship did not persist in major teaching EDs.

Citation: Pitts SR, Morgan SR, Schrager JD, Berger TJ. Emergency department resource use by supervised residents vs attending physicians alone. JAMA. 2014;312(22):2394-2400.

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Physician Spending Habits During Residency Training Can Persist for Years

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Physician Spending Habits During Residency Training Can Persist for Years

Clinical question: For primary care physicians (PCPs), does residency training area affect the pattern of physician spending after training is complete?

Background: Regional and system-level variations in the intensity of medical services provided are common in the U.S. Residency training practice patterns could explain these variations. This study examines the relationship between spending patterns in the region of residency training and individual physician practice spending patterns after training.

Study design: Secondary, multilevel, multivariable analysis of 2011 Medicare claims data.

Setting: Random, nationally representative sample of family and internal medicine physicians completing residency between 1992 and 2010, with Medicare patient panels of 40 or more patients.

Synopsis: Investigators randomly selected 2,851 PCPs who completed residency training from 1992-2010, providing care to 491,948 Medicare beneficiaries. Practice locations and residency training were matched with the Dartmouth Atlas Hospital Referral Region (HRR) files. Training and practice HRRs were categorized into low-, average-, and high-spending groups.

Physicians practicing in high-spending regions who trained in high-spending regions spent $1,926 more per Medicare beneficiary than those trained in low-spending regions. In average-spending regions, physicians who trained in high-spending regions spent an average of $897 higher than those who trained in low-spending regions. No differences were found in low-spending regions.

This association varied significantly according to years in practice. For physicians in the first seven years of practice, patient expenditures in the highest-spending training HRR were 29% greater than those in the lowest-spending training HRR. After 16 years of practice, this variation disappeared.

Although this study does not establish causality, there may be opportunities to control spending with focused interventions in residency.

Bottom line: Spending patterns vary even within HRRs; however, this study’s findings suggest that physicians’ practice patterns are developed in residency training and that training in high-spending regions likely leads to increased expenditures. Focusing on cost-conscious care during residency training could be a significant option for curtailing healthcare costs in the future.

Citation: Chen C, Petterson S, Phillips R, Bazemore A, Mullan F. Spending patterns in region of residency training and subsequent expenditures for care provided by practicing physicians for Medicare beneficiaries. JAMA. 2014;312(22):2385-2393.

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Clinical question: For primary care physicians (PCPs), does residency training area affect the pattern of physician spending after training is complete?

Background: Regional and system-level variations in the intensity of medical services provided are common in the U.S. Residency training practice patterns could explain these variations. This study examines the relationship between spending patterns in the region of residency training and individual physician practice spending patterns after training.

Study design: Secondary, multilevel, multivariable analysis of 2011 Medicare claims data.

Setting: Random, nationally representative sample of family and internal medicine physicians completing residency between 1992 and 2010, with Medicare patient panels of 40 or more patients.

Synopsis: Investigators randomly selected 2,851 PCPs who completed residency training from 1992-2010, providing care to 491,948 Medicare beneficiaries. Practice locations and residency training were matched with the Dartmouth Atlas Hospital Referral Region (HRR) files. Training and practice HRRs were categorized into low-, average-, and high-spending groups.

Physicians practicing in high-spending regions who trained in high-spending regions spent $1,926 more per Medicare beneficiary than those trained in low-spending regions. In average-spending regions, physicians who trained in high-spending regions spent an average of $897 higher than those who trained in low-spending regions. No differences were found in low-spending regions.

This association varied significantly according to years in practice. For physicians in the first seven years of practice, patient expenditures in the highest-spending training HRR were 29% greater than those in the lowest-spending training HRR. After 16 years of practice, this variation disappeared.

Although this study does not establish causality, there may be opportunities to control spending with focused interventions in residency.

Bottom line: Spending patterns vary even within HRRs; however, this study’s findings suggest that physicians’ practice patterns are developed in residency training and that training in high-spending regions likely leads to increased expenditures. Focusing on cost-conscious care during residency training could be a significant option for curtailing healthcare costs in the future.

Citation: Chen C, Petterson S, Phillips R, Bazemore A, Mullan F. Spending patterns in region of residency training and subsequent expenditures for care provided by practicing physicians for Medicare beneficiaries. JAMA. 2014;312(22):2385-2393.

Clinical question: For primary care physicians (PCPs), does residency training area affect the pattern of physician spending after training is complete?

Background: Regional and system-level variations in the intensity of medical services provided are common in the U.S. Residency training practice patterns could explain these variations. This study examines the relationship between spending patterns in the region of residency training and individual physician practice spending patterns after training.

Study design: Secondary, multilevel, multivariable analysis of 2011 Medicare claims data.

Setting: Random, nationally representative sample of family and internal medicine physicians completing residency between 1992 and 2010, with Medicare patient panels of 40 or more patients.

Synopsis: Investigators randomly selected 2,851 PCPs who completed residency training from 1992-2010, providing care to 491,948 Medicare beneficiaries. Practice locations and residency training were matched with the Dartmouth Atlas Hospital Referral Region (HRR) files. Training and practice HRRs were categorized into low-, average-, and high-spending groups.

Physicians practicing in high-spending regions who trained in high-spending regions spent $1,926 more per Medicare beneficiary than those trained in low-spending regions. In average-spending regions, physicians who trained in high-spending regions spent an average of $897 higher than those who trained in low-spending regions. No differences were found in low-spending regions.

This association varied significantly according to years in practice. For physicians in the first seven years of practice, patient expenditures in the highest-spending training HRR were 29% greater than those in the lowest-spending training HRR. After 16 years of practice, this variation disappeared.

Although this study does not establish causality, there may be opportunities to control spending with focused interventions in residency.

Bottom line: Spending patterns vary even within HRRs; however, this study’s findings suggest that physicians’ practice patterns are developed in residency training and that training in high-spending regions likely leads to increased expenditures. Focusing on cost-conscious care during residency training could be a significant option for curtailing healthcare costs in the future.

Citation: Chen C, Petterson S, Phillips R, Bazemore A, Mullan F. Spending patterns in region of residency training and subsequent expenditures for care provided by practicing physicians for Medicare beneficiaries. JAMA. 2014;312(22):2385-2393.

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Malpractice Reform Does Not Change Physician Practice Patterns

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Malpractice Reform Does Not Change Physician Practice Patterns

Clinical question: Do malpractice reform policies shift physician practice patterns toward lower utilization of healthcare resources?

Background: Physician-reported fears of lawsuits lead to defensive medicine practices, which contribute to high healthcare costs. It is unclear whether malpractice reform legislation reduces these costly physician practice patterns. The ED is a high-risk environment that may promote defensive medicine practices and is the focus of recent malpractice reform legislation in Texas, Georgia, and South Carolina.

Study design: Case-control.

Setting: EDs in Texas, Georgia, South Carolina, and adjacent states.

Synopsis: Using a 5% random sample of Medicare claims from 1997-2011, the investigators evaluated the impact of recent malpractice reform legislation on intensity of practice by ED physicians, as defined by rates of use of advanced imaging (computed tomography [CT] or magnetic resonance imaging [MRI]), hospital admission, and average charges. ED claims from the three reform states (Texas, Ga., and S.C.) were compared to neighboring (control) states.

Adjusted analysis of 3,868,110 ED visits from 1,166 eligible hospitals demonstrated no significant reductions in CT/MRI utilization, rates of hospital admission, or (in two of the three reform states) average per-visit ED charges attributable to policy reforms.

Bottom line: Broadly protective malpractice reform had minimal impact on emergency physicians’ intensity of practice, as measured by rates of advanced imaging use, hospital admission, and average charges. Such “pro-physician” legal reforms may be inadequate in isolation to significantly reduce costs.

Citaton: Waxman DA, Greenberg MD, Ridgely MS, Kellermann AL, Heaton P. The effect of malpractice reform on emergency department care. N Engl J Med. 2014;371(16):1518-1525.

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Clinical question: Do malpractice reform policies shift physician practice patterns toward lower utilization of healthcare resources?

Background: Physician-reported fears of lawsuits lead to defensive medicine practices, which contribute to high healthcare costs. It is unclear whether malpractice reform legislation reduces these costly physician practice patterns. The ED is a high-risk environment that may promote defensive medicine practices and is the focus of recent malpractice reform legislation in Texas, Georgia, and South Carolina.

Study design: Case-control.

Setting: EDs in Texas, Georgia, South Carolina, and adjacent states.

Synopsis: Using a 5% random sample of Medicare claims from 1997-2011, the investigators evaluated the impact of recent malpractice reform legislation on intensity of practice by ED physicians, as defined by rates of use of advanced imaging (computed tomography [CT] or magnetic resonance imaging [MRI]), hospital admission, and average charges. ED claims from the three reform states (Texas, Ga., and S.C.) were compared to neighboring (control) states.

Adjusted analysis of 3,868,110 ED visits from 1,166 eligible hospitals demonstrated no significant reductions in CT/MRI utilization, rates of hospital admission, or (in two of the three reform states) average per-visit ED charges attributable to policy reforms.

Bottom line: Broadly protective malpractice reform had minimal impact on emergency physicians’ intensity of practice, as measured by rates of advanced imaging use, hospital admission, and average charges. Such “pro-physician” legal reforms may be inadequate in isolation to significantly reduce costs.

Citaton: Waxman DA, Greenberg MD, Ridgely MS, Kellermann AL, Heaton P. The effect of malpractice reform on emergency department care. N Engl J Med. 2014;371(16):1518-1525.

Clinical question: Do malpractice reform policies shift physician practice patterns toward lower utilization of healthcare resources?

Background: Physician-reported fears of lawsuits lead to defensive medicine practices, which contribute to high healthcare costs. It is unclear whether malpractice reform legislation reduces these costly physician practice patterns. The ED is a high-risk environment that may promote defensive medicine practices and is the focus of recent malpractice reform legislation in Texas, Georgia, and South Carolina.

Study design: Case-control.

Setting: EDs in Texas, Georgia, South Carolina, and adjacent states.

Synopsis: Using a 5% random sample of Medicare claims from 1997-2011, the investigators evaluated the impact of recent malpractice reform legislation on intensity of practice by ED physicians, as defined by rates of use of advanced imaging (computed tomography [CT] or magnetic resonance imaging [MRI]), hospital admission, and average charges. ED claims from the three reform states (Texas, Ga., and S.C.) were compared to neighboring (control) states.

Adjusted analysis of 3,868,110 ED visits from 1,166 eligible hospitals demonstrated no significant reductions in CT/MRI utilization, rates of hospital admission, or (in two of the three reform states) average per-visit ED charges attributable to policy reforms.

Bottom line: Broadly protective malpractice reform had minimal impact on emergency physicians’ intensity of practice, as measured by rates of advanced imaging use, hospital admission, and average charges. Such “pro-physician” legal reforms may be inadequate in isolation to significantly reduce costs.

Citaton: Waxman DA, Greenberg MD, Ridgely MS, Kellermann AL, Heaton P. The effect of malpractice reform on emergency department care. N Engl J Med. 2014;371(16):1518-1525.

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Malpractice Reform Does Not Change Physician Practice Patterns
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