Stacy Fischer, MD

The fraction of the US population identifying themselves as ethnic minorities was 36% in 2010 and will exceed 50% by 2050.[1, 2] This has resulted in an increasing gap in healthcare, as minorities have well‐documented disparities in access to healthcare and a disproportionately high morbidity and mortality.[3] In 2008, only 12.3% of US physicians were from under‐represented minority (URM) groups (see Figure in Castillo‐Page 4) (ie, those racial and ethnic populations that are underrepresented in the medical profession relative to their numbers in the general population as defined by the American Association of Medical Colleges[4, 5]). Diversifying the healthcare workforce may be an effective approach to reducing healthcare disparities, as URM physicians are more likely to choose primary care specialties,[6] work in underserved communities with socioeconomic or racial mixes similar to their own, thereby increasing access to care,[6, 7, 8] increasing minority patient satisfaction, and improving the quality of care received by minorities.[9, 10, 11]

The number of URM students attending medical school is slowly increasing, but in 2011, only 15% of the matriculating medical school students were URMs (see Figure 12 and Table 10 in Castillo‐Page[12]), and medical schools actively compete for this limited number of applicants. To increase the pool of qualified candidates, more URM students need to graduate college and pursue postgraduate healthcare training.[12]

URM undergraduate freshmen with intentions to enter medical school are 50% less likely to apply to medical school by the time they are seniors than their non‐Latino, white, and Asian counterparts.[13] Higher attrition rates have been linked to students having negative experiences in the basic science courses and with a lack of role models and exposure to careers in healthcare.[13, 14, 15, 16] We developed a hospitalist‐led mentoring program that was focused on overcoming these perceived limitations. This report describes the program and follow‐up data from our first year cohort documenting its success.

METHODS

The Healthcare Interest Program (HIP) was developed by 2 hospitalists (L. C., E. C.) and a physician's assistant (C. N.) who worked at Denver Health (DH), a university‐affiliated public hospital. We worked in conjunction with the chief diversity officer of the University of Colorado, Denver (UCD), primarily a commuter university in metropolitan Denver, where URMs composed 51% of the 2011 freshmen class. We reviewed articles describing mentoring programs for undergraduate students, and by consensus, designed a 7‐component program, each of which was intended to address a specific barrier identified in the literature as possibly contributing to reduced interest of minority students in pursuing medical careers (Table 1).[13, 14, 15, 16]

During the 2009 to 2010 academic year, information about the program, together with an application, was e‐mailed to all students at UCD who self‐identified as having interest in healthcare careers. This information was also distributed at all prehealth clubs and gatherings (ie, to students expressing interest in graduate and professional programs in healthcare‐related fields). All sophomore and junior students who submitted an application and had grade point averages (GPA) 2.8 were interviewed by the program director. Twenty‐three students were selected on the basis of their GPAs (attempting to include those with a range of GPAs), interviews, and the essays prepared as part of their applications.

An e‐mail soliciting mentors was sent to all hospitalists physicians and midlevels working at DH; 25/30 volunteered, and 20 were selected on the basis of their gender (as mentors were matched to students based on gender). The HIP director met with the mentors in person to introduce the program and its goals. All mentors had been practicing hospital medicine for 10 years after their training, and all but 3 were non‐Latino white. Each student accepted into the program was paired with a hospitalist who served as their mentor for the year.

The mentors were instructed in life coaching in both e‐mails and individual discussions. Every 2 or 3 months each hospitalist was contacted by e‐mail to see if questions or problems had arisen and to emphasize the need to meet with their mentees monthly.

Students filled out a written survey after each Books‐to‐Bedside (described in Table 1) discussion. The HIP director met with each student for at least 1 hour per semester and gathered feedback regarding mentor‐mentee success, shadowing experience, and the quality of the book club. At the end of the academic year, students completed a written, anonymous survey assessing their impressions of the program and their intentions of pursuing additional training in healthcare careers (Table 2). We used descriptive statistics to analyze the data including frequencies and mean tests.

Two years after completing the program, each student was contacted via e‐mail and/or phone to determine whether they were still pursuing healthcare careers.

RESULTS

Twenty‐three students were accepted into the program (14 female, 9 male, mean age 19 [standard deviation1]). Their GPAs ranged from 2.8 to 4.0. Eleven (48%) were the first in their family to attend college, 6 (26%) indicated that English was not their primary language, and 16 (70%) were working while attending school. All 23 students stayed in the HIP program for the full academic year.

Nineteen of the 23 students (83%) completed the survey at the end of the year. Of these, 19 (100%) strongly agreed that the HIP expanded their perceptions of what they might accomplish and increased their confidence in being able to succeed in a healthcare profession. All 19 (100%) stated that they hoped to care for underserved minority patients in the future. Sixteen (84%) strongly agreed that their role model in life was their HIP mentor. These findings suggest that many of the HIP components successfully accomplished their goals (Table 1).

Two‐year follow‐up was available for 21 of the 23 students (91%). Twenty (95%) remained committed to a career in healthcare, 18 (86%) had graduated college, 6 (29%) were enrolled in graduate training in the healthcare professions (2 in medical school, 1 in nursing school, and 3 in a master's programs in public health, counseling, and medical science, respectively), and 9 (43%) were in the process of applying to postgraduate healthcare training programs (7 to medical school, 1 to dental school, and 1 to nursing school, respectively). Five students were preparing to take the Medical College Admissions Test, and 7 were working at various jobs in the healthcare field (eg, phlebotomists, certified nurse assistants, research assistants). Of the 16 students who expressed an interest in attending medical school at the beginning of the program, 15 (94%) maintained that interest.

DISCUSSION

HIP was extremely well‐received by the participating students, the majority graduated college and remained committed to a career in healthcare, and 29% were enrolled in postgraduate training in healthcare professions 2 years after graduation.

The 86% graduation rate that we observed compares highly favorably to the UCD campus‐wide graduation rates for minority students of 12.5% at 4 years and 30.8% at 5 years. Although there may be selection bias in the students participating in HIP, the extremely high graduation rate is consistent with HIP meeting 1 or more of its stated objectives.

Many universities have prehealthcare pipeline programs that are designed to provide short‐term summer medical experiences, research opportunities, and assistance with the Medical College Admissions Test.[17, 18, 19] We believe, however, that several aspects of our program are unique. First, we designed HIP to be year‐long, rather than a summertime program. Continuing the mentoring and life coaching throughout the year may allow stronger relationships to develop between the mentor and the student. In addition, ongoing student‐mentor interactions during the time when a student may be encountering problems with their undergraduate basic science courses may be beneficial. Second, the Books‐to‐Bedside lectures series, which was designed to link the students' basic science training with clinical medicine, has not previously been described and may contribute to a higher rate of completion of their basic science training. Third, those aspects of the program resulting in increased peer interactions (eg, book club discussions, diversity lectures, and social gatherings) provided an important venue for students with similar interests to interact, an opportunity that is limited at UCD as it is primarily a commuter university.

A number of lessons were learned during the first year of the program. First, a program such as ours must include rigorous evaluation from the start to make a case for support to the university and key stakeholders. With this in mind, it is possible to obtain funding and ensure long‐term sustainability. Second, by involving UCD's chief diversity officer in the development, the program fostered a strong partnership between DH and UCD and facilitated growing the program. Third, the hospitalists who attended the diversity‐training aspects of the program stated through informal feedback that they felt better equipped to care for the underserved and felt that providing mentorship increased their personal job satisfaction. Fourth, the students requested more opportunities for them to participate in health disparities research and in shadowing in subspecialties in addition to internal medicine. In response to this feedback, we now offer research opportunities, lectures on health disparities research, and interactions with community leaders working in improving healthcare for the underserved.

Although influencing the graduation rate from graduate level schooling is beyond the scope of HIP, we can conclude that the large majority of students participating in HIP maintained their interest in the healthcare professions, graduated college, and that many went on to postgraduate healthcare training. The data we present pertain to the cohort of students in the first year of the HIP. As the program matures, we will continue to evaluate the long‐term outcomes of our students and hospitalist mentors. This may provide opportunities for other academic hospitalists to replicate our program in their own communities.

The fraction of the US population identifying themselves as ethnic minorities was 36% in 2010 and will exceed 50% by 2050.[1, 2] This has resulted in an increasing gap in healthcare, as minorities have well‐documented disparities in access to healthcare and a disproportionately high morbidity and mortality.[3] In 2008, only 12.3% of US physicians were from under‐represented minority (URM) groups (see Figure in Castillo‐Page 4) (ie, those racial and ethnic populations that are underrepresented in the medical profession relative to their numbers in the general population as defined by the American Association of Medical Colleges[4, 5]). Diversifying the healthcare workforce may be an effective approach to reducing healthcare disparities, as URM physicians are more likely to choose primary care specialties,[6] work in underserved communities with socioeconomic or racial mixes similar to their own, thereby increasing access to care,[6, 7, 8] increasing minority patient satisfaction, and improving the quality of care received by minorities.[9, 10, 11]

The number of URM students attending medical school is slowly increasing, but in 2011, only 15% of the matriculating medical school students were URMs (see Figure 12 and Table 10 in Castillo‐Page[12]), and medical schools actively compete for this limited number of applicants. To increase the pool of qualified candidates, more URM students need to graduate college and pursue postgraduate healthcare training.[12]

URM undergraduate freshmen with intentions to enter medical school are 50% less likely to apply to medical school by the time they are seniors than their non‐Latino, white, and Asian counterparts.[13] Higher attrition rates have been linked to students having negative experiences in the basic science courses and with a lack of role models and exposure to careers in healthcare.[13, 14, 15, 16] We developed a hospitalist‐led mentoring program that was focused on overcoming these perceived limitations. This report describes the program and follow‐up data from our first year cohort documenting its success.

METHODS

The Healthcare Interest Program (HIP) was developed by 2 hospitalists (L. C., E. C.) and a physician's assistant (C. N.) who worked at Denver Health (DH), a university‐affiliated public hospital. We worked in conjunction with the chief diversity officer of the University of Colorado, Denver (UCD), primarily a commuter university in metropolitan Denver, where URMs composed 51% of the 2011 freshmen class. We reviewed articles describing mentoring programs for undergraduate students, and by consensus, designed a 7‐component program, each of which was intended to address a specific barrier identified in the literature as possibly contributing to reduced interest of minority students in pursuing medical careers (Table 1).[13, 14, 15, 16]

During the 2009 to 2010 academic year, information about the program, together with an application, was e‐mailed to all students at UCD who self‐identified as having interest in healthcare careers. This information was also distributed at all prehealth clubs and gatherings (ie, to students expressing interest in graduate and professional programs in healthcare‐related fields). All sophomore and junior students who submitted an application and had grade point averages (GPA) 2.8 were interviewed by the program director. Twenty‐three students were selected on the basis of their GPAs (attempting to include those with a range of GPAs), interviews, and the essays prepared as part of their applications.

An e‐mail soliciting mentors was sent to all hospitalists physicians and midlevels working at DH; 25/30 volunteered, and 20 were selected on the basis of their gender (as mentors were matched to students based on gender). The HIP director met with the mentors in person to introduce the program and its goals. All mentors had been practicing hospital medicine for 10 years after their training, and all but 3 were non‐Latino white. Each student accepted into the program was paired with a hospitalist who served as their mentor for the year.

The mentors were instructed in life coaching in both e‐mails and individual discussions. Every 2 or 3 months each hospitalist was contacted by e‐mail to see if questions or problems had arisen and to emphasize the need to meet with their mentees monthly.

Students filled out a written survey after each Books‐to‐Bedside (described in Table 1) discussion. The HIP director met with each student for at least 1 hour per semester and gathered feedback regarding mentor‐mentee success, shadowing experience, and the quality of the book club. At the end of the academic year, students completed a written, anonymous survey assessing their impressions of the program and their intentions of pursuing additional training in healthcare careers (Table 2). We used descriptive statistics to analyze the data including frequencies and mean tests.

Two years after completing the program, each student was contacted via e‐mail and/or phone to determine whether they were still pursuing healthcare careers.

RESULTS

Twenty‐three students were accepted into the program (14 female, 9 male, mean age 19 [standard deviation1]). Their GPAs ranged from 2.8 to 4.0. Eleven (48%) were the first in their family to attend college, 6 (26%) indicated that English was not their primary language, and 16 (70%) were working while attending school. All 23 students stayed in the HIP program for the full academic year.

Nineteen of the 23 students (83%) completed the survey at the end of the year. Of these, 19 (100%) strongly agreed that the HIP expanded their perceptions of what they might accomplish and increased their confidence in being able to succeed in a healthcare profession. All 19 (100%) stated that they hoped to care for underserved minority patients in the future. Sixteen (84%) strongly agreed that their role model in life was their HIP mentor. These findings suggest that many of the HIP components successfully accomplished their goals (Table 1).

Two‐year follow‐up was available for 21 of the 23 students (91%). Twenty (95%) remained committed to a career in healthcare, 18 (86%) had graduated college, 6 (29%) were enrolled in graduate training in the healthcare professions (2 in medical school, 1 in nursing school, and 3 in a master's programs in public health, counseling, and medical science, respectively), and 9 (43%) were in the process of applying to postgraduate healthcare training programs (7 to medical school, 1 to dental school, and 1 to nursing school, respectively). Five students were preparing to take the Medical College Admissions Test, and 7 were working at various jobs in the healthcare field (eg, phlebotomists, certified nurse assistants, research assistants). Of the 16 students who expressed an interest in attending medical school at the beginning of the program, 15 (94%) maintained that interest.

DISCUSSION

HIP was extremely well‐received by the participating students, the majority graduated college and remained committed to a career in healthcare, and 29% were enrolled in postgraduate training in healthcare professions 2 years after graduation.

The 86% graduation rate that we observed compares highly favorably to the UCD campus‐wide graduation rates for minority students of 12.5% at 4 years and 30.8% at 5 years. Although there may be selection bias in the students participating in HIP, the extremely high graduation rate is consistent with HIP meeting 1 or more of its stated objectives.

Many universities have prehealthcare pipeline programs that are designed to provide short‐term summer medical experiences, research opportunities, and assistance with the Medical College Admissions Test.[17, 18, 19] We believe, however, that several aspects of our program are unique. First, we designed HIP to be year‐long, rather than a summertime program. Continuing the mentoring and life coaching throughout the year may allow stronger relationships to develop between the mentor and the student. In addition, ongoing student‐mentor interactions during the time when a student may be encountering problems with their undergraduate basic science courses may be beneficial. Second, the Books‐to‐Bedside lectures series, which was designed to link the students' basic science training with clinical medicine, has not previously been described and may contribute to a higher rate of completion of their basic science training. Third, those aspects of the program resulting in increased peer interactions (eg, book club discussions, diversity lectures, and social gatherings) provided an important venue for students with similar interests to interact, an opportunity that is limited at UCD as it is primarily a commuter university.

A number of lessons were learned during the first year of the program. First, a program such as ours must include rigorous evaluation from the start to make a case for support to the university and key stakeholders. With this in mind, it is possible to obtain funding and ensure long‐term sustainability. Second, by involving UCD's chief diversity officer in the development, the program fostered a strong partnership between DH and UCD and facilitated growing the program. Third, the hospitalists who attended the diversity‐training aspects of the program stated through informal feedback that they felt better equipped to care for the underserved and felt that providing mentorship increased their personal job satisfaction. Fourth, the students requested more opportunities for them to participate in health disparities research and in shadowing in subspecialties in addition to internal medicine. In response to this feedback, we now offer research opportunities, lectures on health disparities research, and interactions with community leaders working in improving healthcare for the underserved.

Although influencing the graduation rate from graduate level schooling is beyond the scope of HIP, we can conclude that the large majority of students participating in HIP maintained their interest in the healthcare professions, graduated college, and that many went on to postgraduate healthcare training. The data we present pertain to the cohort of students in the first year of the HIP. As the program matures, we will continue to evaluate the long‐term outcomes of our students and hospitalist mentors. This may provide opportunities for other academic hospitalists to replicate our program in their own communities.

The fraction of the US population identifying themselves as ethnic minorities was 36% in 2010 and will exceed 50% by 2050.[1, 2] This has resulted in an increasing gap in healthcare, as minorities have well‐documented disparities in access to healthcare and a disproportionately high morbidity and mortality.[3] In 2008, only 12.3% of US physicians were from under‐represented minority (URM) groups (see Figure in Castillo‐Page 4) (ie, those racial and ethnic populations that are underrepresented in the medical profession relative to their numbers in the general population as defined by the American Association of Medical Colleges[4, 5]). Diversifying the healthcare workforce may be an effective approach to reducing healthcare disparities, as URM physicians are more likely to choose primary care specialties,[6] work in underserved communities with socioeconomic or racial mixes similar to their own, thereby increasing access to care,[6, 7, 8] increasing minority patient satisfaction, and improving the quality of care received by minorities.[9, 10, 11]

The number of URM students attending medical school is slowly increasing, but in 2011, only 15% of the matriculating medical school students were URMs (see Figure 12 and Table 10 in Castillo‐Page[12]), and medical schools actively compete for this limited number of applicants. To increase the pool of qualified candidates, more URM students need to graduate college and pursue postgraduate healthcare training.[12]

URM undergraduate freshmen with intentions to enter medical school are 50% less likely to apply to medical school by the time they are seniors than their non‐Latino, white, and Asian counterparts.[13] Higher attrition rates have been linked to students having negative experiences in the basic science courses and with a lack of role models and exposure to careers in healthcare.[13, 14, 15, 16] We developed a hospitalist‐led mentoring program that was focused on overcoming these perceived limitations. This report describes the program and follow‐up data from our first year cohort documenting its success.

METHODS

The Healthcare Interest Program (HIP) was developed by 2 hospitalists (L. C., E. C.) and a physician's assistant (C. N.) who worked at Denver Health (DH), a university‐affiliated public hospital. We worked in conjunction with the chief diversity officer of the University of Colorado, Denver (UCD), primarily a commuter university in metropolitan Denver, where URMs composed 51% of the 2011 freshmen class. We reviewed articles describing mentoring programs for undergraduate students, and by consensus, designed a 7‐component program, each of which was intended to address a specific barrier identified in the literature as possibly contributing to reduced interest of minority students in pursuing medical careers (Table 1).[13, 14, 15, 16]

During the 2009 to 2010 academic year, information about the program, together with an application, was e‐mailed to all students at UCD who self‐identified as having interest in healthcare careers. This information was also distributed at all prehealth clubs and gatherings (ie, to students expressing interest in graduate and professional programs in healthcare‐related fields). All sophomore and junior students who submitted an application and had grade point averages (GPA) 2.8 were interviewed by the program director. Twenty‐three students were selected on the basis of their GPAs (attempting to include those with a range of GPAs), interviews, and the essays prepared as part of their applications.

An e‐mail soliciting mentors was sent to all hospitalists physicians and midlevels working at DH; 25/30 volunteered, and 20 were selected on the basis of their gender (as mentors were matched to students based on gender). The HIP director met with the mentors in person to introduce the program and its goals. All mentors had been practicing hospital medicine for 10 years after their training, and all but 3 were non‐Latino white. Each student accepted into the program was paired with a hospitalist who served as their mentor for the year.

The mentors were instructed in life coaching in both e‐mails and individual discussions. Every 2 or 3 months each hospitalist was contacted by e‐mail to see if questions or problems had arisen and to emphasize the need to meet with their mentees monthly.

Students filled out a written survey after each Books‐to‐Bedside (described in Table 1) discussion. The HIP director met with each student for at least 1 hour per semester and gathered feedback regarding mentor‐mentee success, shadowing experience, and the quality of the book club. At the end of the academic year, students completed a written, anonymous survey assessing their impressions of the program and their intentions of pursuing additional training in healthcare careers (Table 2). We used descriptive statistics to analyze the data including frequencies and mean tests.

Two years after completing the program, each student was contacted via e‐mail and/or phone to determine whether they were still pursuing healthcare careers.

RESULTS

Twenty‐three students were accepted into the program (14 female, 9 male, mean age 19 [standard deviation1]). Their GPAs ranged from 2.8 to 4.0. Eleven (48%) were the first in their family to attend college, 6 (26%) indicated that English was not their primary language, and 16 (70%) were working while attending school. All 23 students stayed in the HIP program for the full academic year.

Nineteen of the 23 students (83%) completed the survey at the end of the year. Of these, 19 (100%) strongly agreed that the HIP expanded their perceptions of what they might accomplish and increased their confidence in being able to succeed in a healthcare profession. All 19 (100%) stated that they hoped to care for underserved minority patients in the future. Sixteen (84%) strongly agreed that their role model in life was their HIP mentor. These findings suggest that many of the HIP components successfully accomplished their goals (Table 1).

Two‐year follow‐up was available for 21 of the 23 students (91%). Twenty (95%) remained committed to a career in healthcare, 18 (86%) had graduated college, 6 (29%) were enrolled in graduate training in the healthcare professions (2 in medical school, 1 in nursing school, and 3 in a master's programs in public health, counseling, and medical science, respectively), and 9 (43%) were in the process of applying to postgraduate healthcare training programs (7 to medical school, 1 to dental school, and 1 to nursing school, respectively). Five students were preparing to take the Medical College Admissions Test, and 7 were working at various jobs in the healthcare field (eg, phlebotomists, certified nurse assistants, research assistants). Of the 16 students who expressed an interest in attending medical school at the beginning of the program, 15 (94%) maintained that interest.

DISCUSSION

HIP was extremely well‐received by the participating students, the majority graduated college and remained committed to a career in healthcare, and 29% were enrolled in postgraduate training in healthcare professions 2 years after graduation.

The 86% graduation rate that we observed compares highly favorably to the UCD campus‐wide graduation rates for minority students of 12.5% at 4 years and 30.8% at 5 years. Although there may be selection bias in the students participating in HIP, the extremely high graduation rate is consistent with HIP meeting 1 or more of its stated objectives.

Many universities have prehealthcare pipeline programs that are designed to provide short‐term summer medical experiences, research opportunities, and assistance with the Medical College Admissions Test.[17, 18, 19] We believe, however, that several aspects of our program are unique. First, we designed HIP to be year‐long, rather than a summertime program. Continuing the mentoring and life coaching throughout the year may allow stronger relationships to develop between the mentor and the student. In addition, ongoing student‐mentor interactions during the time when a student may be encountering problems with their undergraduate basic science courses may be beneficial. Second, the Books‐to‐Bedside lectures series, which was designed to link the students' basic science training with clinical medicine, has not previously been described and may contribute to a higher rate of completion of their basic science training. Third, those aspects of the program resulting in increased peer interactions (eg, book club discussions, diversity lectures, and social gatherings) provided an important venue for students with similar interests to interact, an opportunity that is limited at UCD as it is primarily a commuter university.

A number of lessons were learned during the first year of the program. First, a program such as ours must include rigorous evaluation from the start to make a case for support to the university and key stakeholders. With this in mind, it is possible to obtain funding and ensure long‐term sustainability. Second, by involving UCD's chief diversity officer in the development, the program fostered a strong partnership between DH and UCD and facilitated growing the program. Third, the hospitalists who attended the diversity‐training aspects of the program stated through informal feedback that they felt better equipped to care for the underserved and felt that providing mentorship increased their personal job satisfaction. Fourth, the students requested more opportunities for them to participate in health disparities research and in shadowing in subspecialties in addition to internal medicine. In response to this feedback, we now offer research opportunities, lectures on health disparities research, and interactions with community leaders working in improving healthcare for the underserved.

Although influencing the graduation rate from graduate level schooling is beyond the scope of HIP, we can conclude that the large majority of students participating in HIP maintained their interest in the healthcare professions, graduated college, and that many went on to postgraduate healthcare training. The data we present pertain to the cohort of students in the first year of the HIP. As the program matures, we will continue to evaluate the long‐term outcomes of our students and hospitalist mentors. This may provide opportunities for other academic hospitalists to replicate our program in their own communities.

Prognostication continues to be a challenge to the clinician despite over 100 prognostic indices that have been developed during the past few decades to inform clinical practice and medical decision making.[1] Physicians are not accurate in prognostication of patients' risk of death and tend to overestimate survival.[2, 3] In addition, many physicians do not feel comfortable offering a prognosis to patients, despite patients' wishes to be informed.[4, 5] Regardless of the prevalence in the literature and value in improving physicians' prognostic accuracy, prognostic indices of survival are not regularly utilized in the hospital setting. Prognostic tools available for providers are often complicated and may require data about patients that are not readily available.[6, 7, 8] Prognostic indices may be too specific to a patient population, too difficult to remember, or too time consuming to use. A simple, rapid, and practical prognostic index is important in the hospital setting to assist in identifying patients at high risk of death so that primary palliative interventions can be incorporated into the plan of care early in the hospital stay. Patient and family education, advance care planning, formulating the plan of care based on patientfamily goals, and improved resource utilization could be better executed by more accurate risk of death prediction on hospital admission.

The CARING criteria are the only prognostic index to our knowledge that evaluates a patient's risk of death in the next year, with information readily available at the time of hospital admission (Table 1).[9] The CARING criteria are a unique prognostic tool: (1) CARING is a mnemonic acronym, making it more user friendly to the clinician. (2) The 5 prognostic indicators are readily available from the patient's chart on admission; gathering further data by patient or caretaker interviews or by obtaining laboratory data is not needed. (3) The timing for application of the tool on admission to the hospital is an ideal opportunity to intervene and introduce palliative interventions early on the hospital stay. The CARING criteria were developed and validated in a Veteran's Administration hospital setting by Fischer et al.[9] We sought to validate the CARING criteria in a broader patient populationmedical and surgical patients from a tertiary referral university hospital setting and a safety‐net hospital setting.

METHODS

RESULTS

There were a total of 1064 patients admitted to the medical and surgical services during the study period568 patients at DHH and 496 patients at UCH. Sample characteristics of each individual hospital cohort and the entire combined study cohort are detailed in Table 2. Overall, slightly over half the population were male, with a mean age of 50 years, and the ethnic breakdown roughly reflects the population in Denver. A total of 36.5% (n=388) of the study population met 1 of the CARING criteria, and 12.6% (n=134 among 1063 excluding 1 without an admit date) died within 1 year of the index hospitalization. These were younger and healthier patients compared to the VA sample used in developing the CARING criteria.

Reliability testing demonstrated excellent inter‐rater reliability. Kappa for each criterion is as follows: (1) primary diagnosis of cancer=1.0, (2) 2 admissions to the hospital in the past year=0.91, (3) resident in a nursing home=1.0, (4) ICU admission with MOF=1.0, and (5) 2 noncancer hospice guidelines=0.78.

This study aimed to validate the CARING criteria⁹; therefore, all original individual CARING criterion were included in the validation logistic regression models. The 1 exception to this was in the university hospital study cohort, where the ICU criterion was excluded from the model due to small sample size and quasiseparation in the model. The model results are presented in Table 3 for the individual hospitals and combined study cohort.

In the safety‐net hospital, admission to the hospital with a primary diagnosis related to cancer, 2 noncancer hospice guidelines, ICU admission with MOF, and age by category all were significant predictors of 1‐year mortality. In the university hospital cohort, primary diagnosis of cancer, 2 noncancer hospice guidelines, and age by category were predictive of 1‐year mortality. Finally, in the entire study cohort, primary diagnosis of cancer, ICU with MOF, 2 noncancer hospice guidelines, and age were all predictive of 1‐year mortality. Parameter estimates were similar in 3 of the criteria compared to the VA setting. Differences in patient characteristics may have caused the differences in the estimates. Gender was additionally tested but not significant in any model. One‐year survival was significantly lower for those who met 1 of the CARING criteria versus those who did not (Figure 1).

Figure 1

Based on the framework from the original CARING criteria analysis, a scoring rule was developed using the regression results of this validation cohort. To predict a high probability of 1‐year mortality, sensitivity was set to 58% and specificity was set at 86% (error rate=17%). Medium to high probability was set with a sensitivity of 73% and specificity of 72% (error rate=28%). The coefficients from the regression model of the entire study cohort were converted to scores for each of the CARING criteria. The scores are as follows: 0.5 points for admission from a nursing home, 1 point for 2 hospital admissions in the past year for a chronic illness, 10 points for primary diagnosis of cancer, 10 points for ICU admission with MOF, and 14 points for 2 noncancer hospice guidelines. For every age category increase, 2 points are assigned so that 0 points for age <55 years, 2 points for ages 56 to 65 years, 4 points for ages 66 to 75 years, and 6 points for >75 years. Points for individual risk factors were proportional to s (ie, log odds) in the logistic regression model for death at 1 year. Although no linear transformation exists between s and probabilities (of death at 1 year), the aggregated points for combinations of risk factors shown in Table 4 follow the probabilities in an approximately linear fashion, so that different degrees of risk of death can be represented contiguously (as highlighted by differently shaded regions in the scoring matrix) (Table 4). The scoring matrix allows for quick identification for patients at high risk for 1‐year mortality. In this non‐VA setting with healthier patients, low risk is defined at a lower probability threshold (0.1) compared to the VA setting (0.175).

DISCUSSION

The CARING criteria are a practical prognostic tool that can be easily and rapidly applied to patients admitted to the hospital to estimate risk of death in 1 year, with the goal of identifying patients who may benefit most from incorporating palliative interventions into their plan of care. This study validated the CARING criteria in a tertiary referral university hospital and safety‐net hospital setting, demonstrating applicability in a much broader population than the VA hospital of the original CARING criteria study. The population studied represented a younger population by over 10 years, a more equitable proportion of males to females, a broader ethnic diversity, and lower 1‐year deaths rates than the original study. Despite the broader representation of the population, the significance of each of the individual CARING criterion was maintained except for 2 hospital admissions in the past year for a chronic illness (admission from a nursing home did not meet significance in either study as a sole criterion). As with the original study, meeting 2 of the NHPCO noncancer hospice guidelines demonstrated the highest risk of 1‐year mortality following index hospitalization, followed by primary diagnosis of cancer and ICU admission with MOF. Advancing age, also similar to the original study, conferred increased risk across the criterion.

Hospitalists could be an effective target for utilizing the CARING criteria because they are frequently the first‐line providers in the hospital setting. With the national shortage of palliative care specialists, hospitalists need to be able to identify when a patient has a limited life expectancy so they will be better equipped to make clinical decisions that are aligned with their patients' values, preferences, and goals of care. With the realization that not addressing advance care planning and patient goals of care may be considered medical errors, primary palliative care skills become alarmingly more important as priorities for hospitalists to obtain and feel comfortable using in daily practice.

The CARING criteria are directly applicable to patients who are seen by hospitalists. Other prognostic indices have focused on select patient populations, such as the elderly,[10, 11, 12] require collection of data that are not readily available on admission or would not otherwise be obtained,[10, 13] or apply to patients post‐hospital discharge, thereby missing the opportunity to make an impact earlier in the disease trajectory and incorporate palliative care into the hospital plan of care when key discussions about goals of care and preferences should be encouraged.

Additionally, the CARING criteria could easily be incorporated as a trigger for palliative care consults on hospital admission. Palliative care consults tend to happen late in a hospital stay, limiting the effectiveness of the palliative care team. A trigger system for hospitalists and other primary providers on hospital admission would lend to more effective timing of palliative measures being incorporated into the plan of care. Palliative care consults would not only be initiated earlier, but could be targeted for the more complex and sick patients with the highest risk of death in the next year.

In the time‐pressured environment, the presence of any 1 of the CARING criteria can act as a trigger to begin incorporating primary palliative care measures into the plan of care. The admitting hospitalist provider (ie, physician, nurse practitioner, physician assistant) could access the CARING criteria through an electronic health record prompt when admitting patients. When a more detailed assessment of mortality risk is helpful, the hospitalist can use the scoring matrix, which combines age with the individual criterion to calculate patients at medium or high risk of death within 1 year. Limited resources can then be directed to the patients with the greatest need. Patients with a focused care need, such as advance care planning or hospice referral, can be directed to the social worker or case manager. More complicated patients may be referred to a specialty palliative care team.

Several limitations to this study are recognized, including the small sample size of patients meeting criterion for ICU with MOF in the academic center study cohort. The patient data were collected during a transition time when the university hospital moved to a new campus, resulting in an ICU at each campus that housed patients with differing levels of illness severity, which may have contributed to the lower acuity ICU patient observed. Although we advocate the simplicity of the CARING criteria, the NHPCO noncancer hospice guidelines are more complicated, as they incorporates 8 broad categories of chronic illness. The hospice guidelines may not be general knowledge to the hospitalist or other primary providers. ePrognosis (http://eprognosis.ucsf.edu/) has a Web‐based calculator for the CARING criteria, including a link referencing the NHPCO noncancer hospice guidelines. Alternatively, providing a pocket card, smart phone or tablet app, or electronic health record tool containing the NHPCO criteria and CARING criteria could easily overcome this gap in knowledge. Finally, the reviewer agreement was not 100% for each criterion due to personal interpretation differences in the criterion. NHPCO criterion had the lowest kappa, yet it still was 0.78 and achieved a highly acceptable level of agreement.

CONCLUSION

The CARING criteria are a simple, practical prognostic tool predictive of death within 1 year that has been validated in a broad population of hospitalized patients. The criteria hold up in a younger, healthier population that is more diverse by age, gender, and ethnicity than the VA population. With ready access to critical prognostic information on hospital admission, clinicians will be better informed to make decisions that are aligned with their patients' values, preferences, and goals of care.

Prognostication continues to be a challenge to the clinician despite over 100 prognostic indices that have been developed during the past few decades to inform clinical practice and medical decision making.[1] Physicians are not accurate in prognostication of patients' risk of death and tend to overestimate survival.[2, 3] In addition, many physicians do not feel comfortable offering a prognosis to patients, despite patients' wishes to be informed.[4, 5] Regardless of the prevalence in the literature and value in improving physicians' prognostic accuracy, prognostic indices of survival are not regularly utilized in the hospital setting. Prognostic tools available for providers are often complicated and may require data about patients that are not readily available.[6, 7, 8] Prognostic indices may be too specific to a patient population, too difficult to remember, or too time consuming to use. A simple, rapid, and practical prognostic index is important in the hospital setting to assist in identifying patients at high risk of death so that primary palliative interventions can be incorporated into the plan of care early in the hospital stay. Patient and family education, advance care planning, formulating the plan of care based on patientfamily goals, and improved resource utilization could be better executed by more accurate risk of death prediction on hospital admission.

The CARING criteria are the only prognostic index to our knowledge that evaluates a patient's risk of death in the next year, with information readily available at the time of hospital admission (Table 1).[9] The CARING criteria are a unique prognostic tool: (1) CARING is a mnemonic acronym, making it more user friendly to the clinician. (2) The 5 prognostic indicators are readily available from the patient's chart on admission; gathering further data by patient or caretaker interviews or by obtaining laboratory data is not needed. (3) The timing for application of the tool on admission to the hospital is an ideal opportunity to intervene and introduce palliative interventions early on the hospital stay. The CARING criteria were developed and validated in a Veteran's Administration hospital setting by Fischer et al.[9] We sought to validate the CARING criteria in a broader patient populationmedical and surgical patients from a tertiary referral university hospital setting and a safety‐net hospital setting.

METHODS

RESULTS

There were a total of 1064 patients admitted to the medical and surgical services during the study period568 patients at DHH and 496 patients at UCH. Sample characteristics of each individual hospital cohort and the entire combined study cohort are detailed in Table 2. Overall, slightly over half the population were male, with a mean age of 50 years, and the ethnic breakdown roughly reflects the population in Denver. A total of 36.5% (n=388) of the study population met 1 of the CARING criteria, and 12.6% (n=134 among 1063 excluding 1 without an admit date) died within 1 year of the index hospitalization. These were younger and healthier patients compared to the VA sample used in developing the CARING criteria.

Reliability testing demonstrated excellent inter‐rater reliability. Kappa for each criterion is as follows: (1) primary diagnosis of cancer=1.0, (2) 2 admissions to the hospital in the past year=0.91, (3) resident in a nursing home=1.0, (4) ICU admission with MOF=1.0, and (5) 2 noncancer hospice guidelines=0.78.

This study aimed to validate the CARING criteria⁹; therefore, all original individual CARING criterion were included in the validation logistic regression models. The 1 exception to this was in the university hospital study cohort, where the ICU criterion was excluded from the model due to small sample size and quasiseparation in the model. The model results are presented in Table 3 for the individual hospitals and combined study cohort.

In the safety‐net hospital, admission to the hospital with a primary diagnosis related to cancer, 2 noncancer hospice guidelines, ICU admission with MOF, and age by category all were significant predictors of 1‐year mortality. In the university hospital cohort, primary diagnosis of cancer, 2 noncancer hospice guidelines, and age by category were predictive of 1‐year mortality. Finally, in the entire study cohort, primary diagnosis of cancer, ICU with MOF, 2 noncancer hospice guidelines, and age were all predictive of 1‐year mortality. Parameter estimates were similar in 3 of the criteria compared to the VA setting. Differences in patient characteristics may have caused the differences in the estimates. Gender was additionally tested but not significant in any model. One‐year survival was significantly lower for those who met 1 of the CARING criteria versus those who did not (Figure 1).

Figure 1

Based on the framework from the original CARING criteria analysis, a scoring rule was developed using the regression results of this validation cohort. To predict a high probability of 1‐year mortality, sensitivity was set to 58% and specificity was set at 86% (error rate=17%). Medium to high probability was set with a sensitivity of 73% and specificity of 72% (error rate=28%). The coefficients from the regression model of the entire study cohort were converted to scores for each of the CARING criteria. The scores are as follows: 0.5 points for admission from a nursing home, 1 point for 2 hospital admissions in the past year for a chronic illness, 10 points for primary diagnosis of cancer, 10 points for ICU admission with MOF, and 14 points for 2 noncancer hospice guidelines. For every age category increase, 2 points are assigned so that 0 points for age <55 years, 2 points for ages 56 to 65 years, 4 points for ages 66 to 75 years, and 6 points for >75 years. Points for individual risk factors were proportional to s (ie, log odds) in the logistic regression model for death at 1 year. Although no linear transformation exists between s and probabilities (of death at 1 year), the aggregated points for combinations of risk factors shown in Table 4 follow the probabilities in an approximately linear fashion, so that different degrees of risk of death can be represented contiguously (as highlighted by differently shaded regions in the scoring matrix) (Table 4). The scoring matrix allows for quick identification for patients at high risk for 1‐year mortality. In this non‐VA setting with healthier patients, low risk is defined at a lower probability threshold (0.1) compared to the VA setting (0.175).

DISCUSSION

The CARING criteria are a practical prognostic tool that can be easily and rapidly applied to patients admitted to the hospital to estimate risk of death in 1 year, with the goal of identifying patients who may benefit most from incorporating palliative interventions into their plan of care. This study validated the CARING criteria in a tertiary referral university hospital and safety‐net hospital setting, demonstrating applicability in a much broader population than the VA hospital of the original CARING criteria study. The population studied represented a younger population by over 10 years, a more equitable proportion of males to females, a broader ethnic diversity, and lower 1‐year deaths rates than the original study. Despite the broader representation of the population, the significance of each of the individual CARING criterion was maintained except for 2 hospital admissions in the past year for a chronic illness (admission from a nursing home did not meet significance in either study as a sole criterion). As with the original study, meeting 2 of the NHPCO noncancer hospice guidelines demonstrated the highest risk of 1‐year mortality following index hospitalization, followed by primary diagnosis of cancer and ICU admission with MOF. Advancing age, also similar to the original study, conferred increased risk across the criterion.

Hospitalists could be an effective target for utilizing the CARING criteria because they are frequently the first‐line providers in the hospital setting. With the national shortage of palliative care specialists, hospitalists need to be able to identify when a patient has a limited life expectancy so they will be better equipped to make clinical decisions that are aligned with their patients' values, preferences, and goals of care. With the realization that not addressing advance care planning and patient goals of care may be considered medical errors, primary palliative care skills become alarmingly more important as priorities for hospitalists to obtain and feel comfortable using in daily practice.

The CARING criteria are directly applicable to patients who are seen by hospitalists. Other prognostic indices have focused on select patient populations, such as the elderly,[10, 11, 12] require collection of data that are not readily available on admission or would not otherwise be obtained,[10, 13] or apply to patients post‐hospital discharge, thereby missing the opportunity to make an impact earlier in the disease trajectory and incorporate palliative care into the hospital plan of care when key discussions about goals of care and preferences should be encouraged.

Additionally, the CARING criteria could easily be incorporated as a trigger for palliative care consults on hospital admission. Palliative care consults tend to happen late in a hospital stay, limiting the effectiveness of the palliative care team. A trigger system for hospitalists and other primary providers on hospital admission would lend to more effective timing of palliative measures being incorporated into the plan of care. Palliative care consults would not only be initiated earlier, but could be targeted for the more complex and sick patients with the highest risk of death in the next year.

In the time‐pressured environment, the presence of any 1 of the CARING criteria can act as a trigger to begin incorporating primary palliative care measures into the plan of care. The admitting hospitalist provider (ie, physician, nurse practitioner, physician assistant) could access the CARING criteria through an electronic health record prompt when admitting patients. When a more detailed assessment of mortality risk is helpful, the hospitalist can use the scoring matrix, which combines age with the individual criterion to calculate patients at medium or high risk of death within 1 year. Limited resources can then be directed to the patients with the greatest need. Patients with a focused care need, such as advance care planning or hospice referral, can be directed to the social worker or case manager. More complicated patients may be referred to a specialty palliative care team.

Several limitations to this study are recognized, including the small sample size of patients meeting criterion for ICU with MOF in the academic center study cohort. The patient data were collected during a transition time when the university hospital moved to a new campus, resulting in an ICU at each campus that housed patients with differing levels of illness severity, which may have contributed to the lower acuity ICU patient observed. Although we advocate the simplicity of the CARING criteria, the NHPCO noncancer hospice guidelines are more complicated, as they incorporates 8 broad categories of chronic illness. The hospice guidelines may not be general knowledge to the hospitalist or other primary providers. ePrognosis (http://eprognosis.ucsf.edu/) has a Web‐based calculator for the CARING criteria, including a link referencing the NHPCO noncancer hospice guidelines. Alternatively, providing a pocket card, smart phone or tablet app, or electronic health record tool containing the NHPCO criteria and CARING criteria could easily overcome this gap in knowledge. Finally, the reviewer agreement was not 100% for each criterion due to personal interpretation differences in the criterion. NHPCO criterion had the lowest kappa, yet it still was 0.78 and achieved a highly acceptable level of agreement.

CONCLUSION

The CARING criteria are a simple, practical prognostic tool predictive of death within 1 year that has been validated in a broad population of hospitalized patients. The criteria hold up in a younger, healthier population that is more diverse by age, gender, and ethnicity than the VA population. With ready access to critical prognostic information on hospital admission, clinicians will be better informed to make decisions that are aligned with their patients' values, preferences, and goals of care.

Prognostication continues to be a challenge to the clinician despite over 100 prognostic indices that have been developed during the past few decades to inform clinical practice and medical decision making.[1] Physicians are not accurate in prognostication of patients' risk of death and tend to overestimate survival.[2, 3] In addition, many physicians do not feel comfortable offering a prognosis to patients, despite patients' wishes to be informed.[4, 5] Regardless of the prevalence in the literature and value in improving physicians' prognostic accuracy, prognostic indices of survival are not regularly utilized in the hospital setting. Prognostic tools available for providers are often complicated and may require data about patients that are not readily available.[6, 7, 8] Prognostic indices may be too specific to a patient population, too difficult to remember, or too time consuming to use. A simple, rapid, and practical prognostic index is important in the hospital setting to assist in identifying patients at high risk of death so that primary palliative interventions can be incorporated into the plan of care early in the hospital stay. Patient and family education, advance care planning, formulating the plan of care based on patientfamily goals, and improved resource utilization could be better executed by more accurate risk of death prediction on hospital admission.

The CARING criteria are the only prognostic index to our knowledge that evaluates a patient's risk of death in the next year, with information readily available at the time of hospital admission (Table 1).[9] The CARING criteria are a unique prognostic tool: (1) CARING is a mnemonic acronym, making it more user friendly to the clinician. (2) The 5 prognostic indicators are readily available from the patient's chart on admission; gathering further data by patient or caretaker interviews or by obtaining laboratory data is not needed. (3) The timing for application of the tool on admission to the hospital is an ideal opportunity to intervene and introduce palliative interventions early on the hospital stay. The CARING criteria were developed and validated in a Veteran's Administration hospital setting by Fischer et al.[9] We sought to validate the CARING criteria in a broader patient populationmedical and surgical patients from a tertiary referral university hospital setting and a safety‐net hospital setting.

METHODS

RESULTS

There were a total of 1064 patients admitted to the medical and surgical services during the study period568 patients at DHH and 496 patients at UCH. Sample characteristics of each individual hospital cohort and the entire combined study cohort are detailed in Table 2. Overall, slightly over half the population were male, with a mean age of 50 years, and the ethnic breakdown roughly reflects the population in Denver. A total of 36.5% (n=388) of the study population met 1 of the CARING criteria, and 12.6% (n=134 among 1063 excluding 1 without an admit date) died within 1 year of the index hospitalization. These were younger and healthier patients compared to the VA sample used in developing the CARING criteria.

Reliability testing demonstrated excellent inter‐rater reliability. Kappa for each criterion is as follows: (1) primary diagnosis of cancer=1.0, (2) 2 admissions to the hospital in the past year=0.91, (3) resident in a nursing home=1.0, (4) ICU admission with MOF=1.0, and (5) 2 noncancer hospice guidelines=0.78.

This study aimed to validate the CARING criteria⁹; therefore, all original individual CARING criterion were included in the validation logistic regression models. The 1 exception to this was in the university hospital study cohort, where the ICU criterion was excluded from the model due to small sample size and quasiseparation in the model. The model results are presented in Table 3 for the individual hospitals and combined study cohort.

In the safety‐net hospital, admission to the hospital with a primary diagnosis related to cancer, 2 noncancer hospice guidelines, ICU admission with MOF, and age by category all were significant predictors of 1‐year mortality. In the university hospital cohort, primary diagnosis of cancer, 2 noncancer hospice guidelines, and age by category were predictive of 1‐year mortality. Finally, in the entire study cohort, primary diagnosis of cancer, ICU with MOF, 2 noncancer hospice guidelines, and age were all predictive of 1‐year mortality. Parameter estimates were similar in 3 of the criteria compared to the VA setting. Differences in patient characteristics may have caused the differences in the estimates. Gender was additionally tested but not significant in any model. One‐year survival was significantly lower for those who met 1 of the CARING criteria versus those who did not (Figure 1).

Figure 1

Based on the framework from the original CARING criteria analysis, a scoring rule was developed using the regression results of this validation cohort. To predict a high probability of 1‐year mortality, sensitivity was set to 58% and specificity was set at 86% (error rate=17%). Medium to high probability was set with a sensitivity of 73% and specificity of 72% (error rate=28%). The coefficients from the regression model of the entire study cohort were converted to scores for each of the CARING criteria. The scores are as follows: 0.5 points for admission from a nursing home, 1 point for 2 hospital admissions in the past year for a chronic illness, 10 points for primary diagnosis of cancer, 10 points for ICU admission with MOF, and 14 points for 2 noncancer hospice guidelines. For every age category increase, 2 points are assigned so that 0 points for age <55 years, 2 points for ages 56 to 65 years, 4 points for ages 66 to 75 years, and 6 points for >75 years. Points for individual risk factors were proportional to s (ie, log odds) in the logistic regression model for death at 1 year. Although no linear transformation exists between s and probabilities (of death at 1 year), the aggregated points for combinations of risk factors shown in Table 4 follow the probabilities in an approximately linear fashion, so that different degrees of risk of death can be represented contiguously (as highlighted by differently shaded regions in the scoring matrix) (Table 4). The scoring matrix allows for quick identification for patients at high risk for 1‐year mortality. In this non‐VA setting with healthier patients, low risk is defined at a lower probability threshold (0.1) compared to the VA setting (0.175).

DISCUSSION

The CARING criteria are a practical prognostic tool that can be easily and rapidly applied to patients admitted to the hospital to estimate risk of death in 1 year, with the goal of identifying patients who may benefit most from incorporating palliative interventions into their plan of care. This study validated the CARING criteria in a tertiary referral university hospital and safety‐net hospital setting, demonstrating applicability in a much broader population than the VA hospital of the original CARING criteria study. The population studied represented a younger population by over 10 years, a more equitable proportion of males to females, a broader ethnic diversity, and lower 1‐year deaths rates than the original study. Despite the broader representation of the population, the significance of each of the individual CARING criterion was maintained except for 2 hospital admissions in the past year for a chronic illness (admission from a nursing home did not meet significance in either study as a sole criterion). As with the original study, meeting 2 of the NHPCO noncancer hospice guidelines demonstrated the highest risk of 1‐year mortality following index hospitalization, followed by primary diagnosis of cancer and ICU admission with MOF. Advancing age, also similar to the original study, conferred increased risk across the criterion.

Hospitalists could be an effective target for utilizing the CARING criteria because they are frequently the first‐line providers in the hospital setting. With the national shortage of palliative care specialists, hospitalists need to be able to identify when a patient has a limited life expectancy so they will be better equipped to make clinical decisions that are aligned with their patients' values, preferences, and goals of care. With the realization that not addressing advance care planning and patient goals of care may be considered medical errors, primary palliative care skills become alarmingly more important as priorities for hospitalists to obtain and feel comfortable using in daily practice.

The CARING criteria are directly applicable to patients who are seen by hospitalists. Other prognostic indices have focused on select patient populations, such as the elderly,[10, 11, 12] require collection of data that are not readily available on admission or would not otherwise be obtained,[10, 13] or apply to patients post‐hospital discharge, thereby missing the opportunity to make an impact earlier in the disease trajectory and incorporate palliative care into the hospital plan of care when key discussions about goals of care and preferences should be encouraged.

Additionally, the CARING criteria could easily be incorporated as a trigger for palliative care consults on hospital admission. Palliative care consults tend to happen late in a hospital stay, limiting the effectiveness of the palliative care team. A trigger system for hospitalists and other primary providers on hospital admission would lend to more effective timing of palliative measures being incorporated into the plan of care. Palliative care consults would not only be initiated earlier, but could be targeted for the more complex and sick patients with the highest risk of death in the next year.

In the time‐pressured environment, the presence of any 1 of the CARING criteria can act as a trigger to begin incorporating primary palliative care measures into the plan of care. The admitting hospitalist provider (ie, physician, nurse practitioner, physician assistant) could access the CARING criteria through an electronic health record prompt when admitting patients. When a more detailed assessment of mortality risk is helpful, the hospitalist can use the scoring matrix, which combines age with the individual criterion to calculate patients at medium or high risk of death within 1 year. Limited resources can then be directed to the patients with the greatest need. Patients with a focused care need, such as advance care planning or hospice referral, can be directed to the social worker or case manager. More complicated patients may be referred to a specialty palliative care team.

Several limitations to this study are recognized, including the small sample size of patients meeting criterion for ICU with MOF in the academic center study cohort. The patient data were collected during a transition time when the university hospital moved to a new campus, resulting in an ICU at each campus that housed patients with differing levels of illness severity, which may have contributed to the lower acuity ICU patient observed. Although we advocate the simplicity of the CARING criteria, the NHPCO noncancer hospice guidelines are more complicated, as they incorporates 8 broad categories of chronic illness. The hospice guidelines may not be general knowledge to the hospitalist or other primary providers. ePrognosis (http://eprognosis.ucsf.edu/) has a Web‐based calculator for the CARING criteria, including a link referencing the NHPCO noncancer hospice guidelines. Alternatively, providing a pocket card, smart phone or tablet app, or electronic health record tool containing the NHPCO criteria and CARING criteria could easily overcome this gap in knowledge. Finally, the reviewer agreement was not 100% for each criterion due to personal interpretation differences in the criterion. NHPCO criterion had the lowest kappa, yet it still was 0.78 and achieved a highly acceptable level of agreement.

CONCLUSION

The CARING criteria are a simple, practical prognostic tool predictive of death within 1 year that has been validated in a broad population of hospitalized patients. The criteria hold up in a younger, healthier population that is more diverse by age, gender, and ethnicity than the VA population. With ready access to critical prognostic information on hospital admission, clinicians will be better informed to make decisions that are aligned with their patients' values, preferences, and goals of care.

At the turn of the 20th century, most deaths in the United States occurred at home. By the 1960s, over 70% of deaths occurred in an institutional setting, reflecting an evolution of medical technology.[1, 2, 3] With the birth of the hospice movement in the 1970s, dying patients had the opportunity to have both death at home and aggressive symptom control at the end of life. Although there has been a slow decline in the proportion of deaths that occur in the hospital over the past 2 decades,[3] the overwhelming majority of persons state that they would prefer to die at home. However, recent findings suggest that most people will die in an institutional setting.[3, 4, 5, 6]

Although good data exist describing population preferences for location of death, and we know, based on death records, where deaths occur in the United States, there are few studies that examine concordance between preferred and actual site of death at the individual patient level. Furthermore, although factors have been identified that predict death at home, factors predicting concordance between preferred and actual site of death are not well described.[3, 6, 7, 8, 9, 10, 11, 12, 13]

Regardless of where death ultimately occurs, most adults will experience multiple hospitalizations within the last years of their life. Understanding the preferences and subsequent experiences of this population is of particular relevance to hospitalist physicians who are in a unique position to elicit goals from seriously ill patients and help match patient preferences with their medical care. In this observational study, we sought to determine preferences for site of death in a cohort of adult patients admitted to the hospital for medical illness, and then follow those patients to determine where death occurred for those who died. We also sought to explore factors that may predict concordance between preferred and actual site of death. We hypothesized that ethnic diversity and lower socioeconomic status would be associated with a lower likelihood of concordance between preferred and actual site of death. We also hypothesized that advanced care planning would be associated with a higher likelihood of concordance. The Colorado Multi‐Institutional Review Board approved this study.

METHODS

Participants were recruited from 3 hospitals affiliated with the University of Colorado School of Medicine Internal Medicine Residency program, including the Denver Veterans' Administration Center (DVAMC), Denver Health Medical Center (DHMC), and University of Colorado Hospital (UCH). The DVAMC is a large urban Veterans Administration hospital, serving veterans from the Denver metro area, and is a tertiary referral center for veterans in rural Colorado, Wyoming, and parts of Montana. DHMC, the safety‐net hospital for the Denver area, serves over 25% of the residents in the city and county of Denver, including such special populations as the indigent, chronically mentally ill, and persons with polysubstance dependence. UCH had 350 licensed beds at the time of our study and serves as the Rocky Mountain region's only academic tertiary, specialty care, and referral center. At the time of this study, there was limited inpatient palliative care services at the DVAMC and UH, and no palliative care services at DHMC. Participants were screened on the first day following admission to the adult general medical service. Participants were recruited on 96 postadmission days between February 2004 and June 2006. Recruitment days varied from Monday through Friday, to include admissions from the weekend and throughout the year to reduce potential bias due to seasonal trends of diseases such as influenza. Patients were excluded if they died or were discharged within the first 24 hours of admission, were pregnant, jailed, or unable to give informed consent. All other patients were approached and invited to participate in a brief survey.

After informed consent was obtained, participants completed a bedside interview that included self‐identified ethnicity and the Berkman‐Syme Social Network Index,[14] a brief questionnaire quantifying social support from spouse or domestic partner, family, friends, and other religious or secular organizations. Baseline socioeconomic measures (eg, income, employment, home ownership, car ownership) and questions related to the last days of life were also included. Participants were asked the following question, If you were very sick, with an illness that could not be cured, and in bed most of the time, where would you spend the last days of your life if you could chose?

For each participant, we performed a detailed chart review to determine demographic data, presence of advance directives, and CARING criteria (Cancer, Admissions 2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, 2 Noncancer hospice Guidelines), a set of prognostic criteria identifying patients at an index hospitalization who have a high burden of illness and are at risk for death in the following year.[15] We then followed patients for 5 years. If participants died within the follow‐up period, we collected the date and location of death using medical records, death certificates, or in a few cases when official death records were unavailable, direct contact with the family. Participants were considered alive if they had a clinic visit or MD/RN phone contact within 3 months prior to the final collection point date.

RESULTS

Preferred Site of Death

When asked where they preferred to spend the last days of their life, 75% of patients (n=343) stated they would like to be at home. In the hospital was the preferred location for 10% of patients, whereas 6% stated a nursing home and 4% a hospice inpatient facility. Two percent stated they had no preference, and 3% refused to answer (Figure 1)

Figure 1

We found that in the univariable analysis the following factors were associated with preference for site of death at a significance level of P<0.25: unstable housing, hospital setting, income level, ethnicity, CARING criteria, presence of an advance directive, education level, married, primary care provider, and presence of public insurance. Results of the logit models (home vs nursing home or hospice facility, and hospital vs nursing home or hospice facility and home vs hospital) are presented in Table 2.

Table 2.Logistic Regression Modeling of Preference for Death at Home or Hospital

	Adjusted Odds Ratio (95% Confidence Interval)
	Home vs Nursing Home/Hospice Facility	Hospital vs Nursing Home/Hospice Facility	Home vs Hospital
NOTE: Abbreviations: CARING, Cancer, Admissions 2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, 2 Noncancer hospice Guidelines.
Low income	2.71 (1.305.67)	3.05 (1.019.24)	0.99 (0.422.37)
Married	2.44 (1.145.21)	2.40 (0.876.62)	0.82 (0.421.57)
CARING criteria	0.58 (0.301.14)	0.44 (0.181.09)	0.89 (0.471.66)

Patients with income <$30,000/year were more likely to prefer home (or hospital) over a nursing home or hospice facility. Being married was predictive of preferring home over nursing home or hospice facility. Patients meeting 1 of the CARING criteria trended toward being less likely (P=0.11 for home and P=0.08 for hospital) to prefer home (or hospital) vs nursing home or hospice facility. However, there were no significant predictors for a preference for home or hospital when directly comparing the 2 locations, as expected from observing similar effects of variables in the other 2 logit models.

DISCUSSION

We found, similarly to previous reports in the literature, the majority of patients preferred to die at home. We did not find a significant difference in preferences or location of death by ethnicity or illness severity. Lower‐income patients and married patients were more likely to prefer to be at home over a nursing home or a hospice facility in the last days of life. We found that the minority of patients died at their stated preferred site of death, and female gender was the only predictive variable we found to distinguish those patients who died in a place concordant with their wishes compared to those who did not.

In the literature, previous studies have reported concordance rates between preferred and actual site of death that range from 30% to 90%.[12, 13, 18, 19, 20, 21, 22, 23, 24] We found a concordance rate at the lowest end of this spectrum. In trying to understand our findings and place them in context, it is helpful to examine other studies. Many of these studies focused solely on cancer patients.[13, 18, 19, 20, 21, 22, 23] Cancer follows a more predictable trajectory of decline compared to other common life‐threatening illnesses, such as cardiac disease, emphysema, or liver failure, that often involve periods of acute deteriorations and plateaus throughout illness progression. The more predictable trajectory may explain the overall higher concordance rates found in the studies involving cancer patients.

The majority of studies in the literature examining concordance between preferred and actual site of death recruited the study sample from home health or home palliative care programs that were providing support and care to participants.[10, 12, 13, 18, 22, 25, 26, 27] The high concordance rates reported may be the result of the patients in the sample receiving services at home aimed at eliciting preferences and providing support at home. Our observational study is unique in that we elicited patient preferences from a diverse group of hospitalized adults. Patients had a broad range of medical illness and were at various stages in their disease trajectory. This allowed our findings to be more generalizable, a major strength of our study.

The only variable associated with concordance that we identified to predict concordance between preferred and actual site of death was female gender. Women have been shown to be more active in medical decision making, which may explain our findings.[28] Female gender and illness severity (as measured by the CARING criteria) were found to be associated with concordance when the preference is for death at home. For persons with more advanced medical illness, they may have had more opportunity to consider their preferences and talk about these preferences. It is even possible that our interview prompted some participants to have discussions with their families or providers.

Variables with high face validity, such as high social support, higher education, and completing an advance directive, did not demonstrate any effect on the outcome of concordance. Other studies have shown that low functional status, Caucasian ethnicity, home care, higher education, and social support have been associated with a greater likelihood for a home death.[3, 6, 9] However, although studies specifically examining concordance between preferred and actual site of death have looked at predictors for home death, we were unable to find predictors for concordance across all preferences in the literature. We can conclude from our findings that the factors that influence concordance of preferences for site of death are extremely complex and difficult to capture and measure. This is extremely unsatisfying in the face of the low concordance rate of 30% we identified.

Latino ethnicity showed a trend toward having a negative association with concordance between preferred and actual site of death. This trend persisted whether it was concordance overall or for concordance with those who preferred a death at home. In the literature, Latinos have been found to be less likely to complete advance directives, use hospice services at the end of life, and are more likely to experience a hospital death.[29, 30, 31, 32, 33] As care at the end of life continues to improve, careful attention should be paid to ensure that these kinds of gaps do not widen any further.

We interviewed patients at an index hospitalization. Patients had an acute medical illness or an exacerbation of a chronic medical illness and required at least 24 hours of hospitalization to be eligible for inclusion. Our bedside interview made use of an opportune time to question patients, a time when it may have been easier for patients to visualize severe illness at the end of life, rather than asking this question during a time of wellness. Although participants overwhelmingly stated they preferred to be at home, for those who died, decisions were made in their care that did not allow for this preference.

Our follow‐up after the initial bedside interview only included death records of where and when participants died. We do not have the details and narrative of the conversations that may have taken place that led to the care decisions that determined participants' actual place of death. We do not know if preferences were elicited or discussed, and care decisions then negotiated, to best meet the goals and preferences expressed at that time. We also do not know if the conversations did not occur and the default of medical intervention and cure‐focused care dictated how participants spent the last days of their life. There is evidence that when conversations about goals and preferences do occur, concordance between preferences and care received are high.[12, 21]

We were unable to identify any predictors beyond gender in this cohort of adults hospitalized with a broad spectrum of severe medical illness to predict concordance with stated preferences and actual site of death. We can conclude then, based on our findings and supported by the literature, that the default trends toward institutional end‐of‐life experiences. To shift to a more patient‐centered approach, away from the default, healthcare providers need to embrace a palliative approach and incorporate preferences and goals into the discussions about next steps of care to facilitate the peaceful death that the majority of patients imagine for themselves. Hospitalist physicians have a unique opportunity at an index hospitalization to start the conversation about preferences for care including where patients would want to spend the last days of their life.

Our study does have some limitations. We elicited preferences at a single point in time, at an index hospitalization. It is possible that participants' preferences changed over the course of their illness. However, in Agar et al.'s study of longitudinal patient preferences for site of death and place of care, most preferences remained stable over time.[18] We also did not have data that included palliative care involvement, homecare or hospice utilization, or cause of death. All of these variables may be important predictors of concordance. Issues of symptom management and lack of caregiver may also dictate place of death, even when goals and care are aligned. We do not have data to address these components of end‐of‐life decision making.

CONCLUSION

Patients continue to express a preference for death at home. However, the majority of patients experienced an institutional death. Furthermore, few participants achieved concordance with where they preferred to die and where they actually died. Female gender was the sole factor associated with concordance between preferred and actual site of death. Incorporating a palliative approach that elicits goals and helps match goals to care, may offer the best opportunity to help people die where they chose.

Disclosures: This research was supported by the Brookdale National Fellowship Award and the NIA/Beeson grant 5K23AG028957. All authors have seen and agree with the contents of the article. This submission was not under review by any other publication. The authors have no financial interest or other potential conflicts of interest.

At the turn of the 20th century, most deaths in the United States occurred at home. By the 1960s, over 70% of deaths occurred in an institutional setting, reflecting an evolution of medical technology.[1, 2, 3] With the birth of the hospice movement in the 1970s, dying patients had the opportunity to have both death at home and aggressive symptom control at the end of life. Although there has been a slow decline in the proportion of deaths that occur in the hospital over the past 2 decades,[3] the overwhelming majority of persons state that they would prefer to die at home. However, recent findings suggest that most people will die in an institutional setting.[3, 4, 5, 6]

Although good data exist describing population preferences for location of death, and we know, based on death records, where deaths occur in the United States, there are few studies that examine concordance between preferred and actual site of death at the individual patient level. Furthermore, although factors have been identified that predict death at home, factors predicting concordance between preferred and actual site of death are not well described.[3, 6, 7, 8, 9, 10, 11, 12, 13]

Regardless of where death ultimately occurs, most adults will experience multiple hospitalizations within the last years of their life. Understanding the preferences and subsequent experiences of this population is of particular relevance to hospitalist physicians who are in a unique position to elicit goals from seriously ill patients and help match patient preferences with their medical care. In this observational study, we sought to determine preferences for site of death in a cohort of adult patients admitted to the hospital for medical illness, and then follow those patients to determine where death occurred for those who died. We also sought to explore factors that may predict concordance between preferred and actual site of death. We hypothesized that ethnic diversity and lower socioeconomic status would be associated with a lower likelihood of concordance between preferred and actual site of death. We also hypothesized that advanced care planning would be associated with a higher likelihood of concordance. The Colorado Multi‐Institutional Review Board approved this study.

METHODS

Participants were recruited from 3 hospitals affiliated with the University of Colorado School of Medicine Internal Medicine Residency program, including the Denver Veterans' Administration Center (DVAMC), Denver Health Medical Center (DHMC), and University of Colorado Hospital (UCH). The DVAMC is a large urban Veterans Administration hospital, serving veterans from the Denver metro area, and is a tertiary referral center for veterans in rural Colorado, Wyoming, and parts of Montana. DHMC, the safety‐net hospital for the Denver area, serves over 25% of the residents in the city and county of Denver, including such special populations as the indigent, chronically mentally ill, and persons with polysubstance dependence. UCH had 350 licensed beds at the time of our study and serves as the Rocky Mountain region's only academic tertiary, specialty care, and referral center. At the time of this study, there was limited inpatient palliative care services at the DVAMC and UH, and no palliative care services at DHMC. Participants were screened on the first day following admission to the adult general medical service. Participants were recruited on 96 postadmission days between February 2004 and June 2006. Recruitment days varied from Monday through Friday, to include admissions from the weekend and throughout the year to reduce potential bias due to seasonal trends of diseases such as influenza. Patients were excluded if they died or were discharged within the first 24 hours of admission, were pregnant, jailed, or unable to give informed consent. All other patients were approached and invited to participate in a brief survey.

After informed consent was obtained, participants completed a bedside interview that included self‐identified ethnicity and the Berkman‐Syme Social Network Index,[14] a brief questionnaire quantifying social support from spouse or domestic partner, family, friends, and other religious or secular organizations. Baseline socioeconomic measures (eg, income, employment, home ownership, car ownership) and questions related to the last days of life were also included. Participants were asked the following question, If you were very sick, with an illness that could not be cured, and in bed most of the time, where would you spend the last days of your life if you could chose?

For each participant, we performed a detailed chart review to determine demographic data, presence of advance directives, and CARING criteria (Cancer, Admissions 2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, 2 Noncancer hospice Guidelines), a set of prognostic criteria identifying patients at an index hospitalization who have a high burden of illness and are at risk for death in the following year.[15] We then followed patients for 5 years. If participants died within the follow‐up period, we collected the date and location of death using medical records, death certificates, or in a few cases when official death records were unavailable, direct contact with the family. Participants were considered alive if they had a clinic visit or MD/RN phone contact within 3 months prior to the final collection point date.

RESULTS

Preferred Site of Death

When asked where they preferred to spend the last days of their life, 75% of patients (n=343) stated they would like to be at home. In the hospital was the preferred location for 10% of patients, whereas 6% stated a nursing home and 4% a hospice inpatient facility. Two percent stated they had no preference, and 3% refused to answer (Figure 1)

Figure 1

We found that in the univariable analysis the following factors were associated with preference for site of death at a significance level of P<0.25: unstable housing, hospital setting, income level, ethnicity, CARING criteria, presence of an advance directive, education level, married, primary care provider, and presence of public insurance. Results of the logit models (home vs nursing home or hospice facility, and hospital vs nursing home or hospice facility and home vs hospital) are presented in Table 2.

Table 2.Logistic Regression Modeling of Preference for Death at Home or Hospital

	Adjusted Odds Ratio (95% Confidence Interval)
	Home vs Nursing Home/Hospice Facility	Hospital vs Nursing Home/Hospice Facility	Home vs Hospital
NOTE: Abbreviations: CARING, Cancer, Admissions 2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, 2 Noncancer hospice Guidelines.
Low income	2.71 (1.305.67)	3.05 (1.019.24)	0.99 (0.422.37)
Married	2.44 (1.145.21)	2.40 (0.876.62)	0.82 (0.421.57)
CARING criteria	0.58 (0.301.14)	0.44 (0.181.09)	0.89 (0.471.66)

Patients with income <$30,000/year were more likely to prefer home (or hospital) over a nursing home or hospice facility. Being married was predictive of preferring home over nursing home or hospice facility. Patients meeting 1 of the CARING criteria trended toward being less likely (P=0.11 for home and P=0.08 for hospital) to prefer home (or hospital) vs nursing home or hospice facility. However, there were no significant predictors for a preference for home or hospital when directly comparing the 2 locations, as expected from observing similar effects of variables in the other 2 logit models.

DISCUSSION

We found, similarly to previous reports in the literature, the majority of patients preferred to die at home. We did not find a significant difference in preferences or location of death by ethnicity or illness severity. Lower‐income patients and married patients were more likely to prefer to be at home over a nursing home or a hospice facility in the last days of life. We found that the minority of patients died at their stated preferred site of death, and female gender was the only predictive variable we found to distinguish those patients who died in a place concordant with their wishes compared to those who did not.

In the literature, previous studies have reported concordance rates between preferred and actual site of death that range from 30% to 90%.[12, 13, 18, 19, 20, 21, 22, 23, 24] We found a concordance rate at the lowest end of this spectrum. In trying to understand our findings and place them in context, it is helpful to examine other studies. Many of these studies focused solely on cancer patients.[13, 18, 19, 20, 21, 22, 23] Cancer follows a more predictable trajectory of decline compared to other common life‐threatening illnesses, such as cardiac disease, emphysema, or liver failure, that often involve periods of acute deteriorations and plateaus throughout illness progression. The more predictable trajectory may explain the overall higher concordance rates found in the studies involving cancer patients.

The majority of studies in the literature examining concordance between preferred and actual site of death recruited the study sample from home health or home palliative care programs that were providing support and care to participants.[10, 12, 13, 18, 22, 25, 26, 27] The high concordance rates reported may be the result of the patients in the sample receiving services at home aimed at eliciting preferences and providing support at home. Our observational study is unique in that we elicited patient preferences from a diverse group of hospitalized adults. Patients had a broad range of medical illness and were at various stages in their disease trajectory. This allowed our findings to be more generalizable, a major strength of our study.

The only variable associated with concordance that we identified to predict concordance between preferred and actual site of death was female gender. Women have been shown to be more active in medical decision making, which may explain our findings.[28] Female gender and illness severity (as measured by the CARING criteria) were found to be associated with concordance when the preference is for death at home. For persons with more advanced medical illness, they may have had more opportunity to consider their preferences and talk about these preferences. It is even possible that our interview prompted some participants to have discussions with their families or providers.

Variables with high face validity, such as high social support, higher education, and completing an advance directive, did not demonstrate any effect on the outcome of concordance. Other studies have shown that low functional status, Caucasian ethnicity, home care, higher education, and social support have been associated with a greater likelihood for a home death.[3, 6, 9] However, although studies specifically examining concordance between preferred and actual site of death have looked at predictors for home death, we were unable to find predictors for concordance across all preferences in the literature. We can conclude from our findings that the factors that influence concordance of preferences for site of death are extremely complex and difficult to capture and measure. This is extremely unsatisfying in the face of the low concordance rate of 30% we identified.

Latino ethnicity showed a trend toward having a negative association with concordance between preferred and actual site of death. This trend persisted whether it was concordance overall or for concordance with those who preferred a death at home. In the literature, Latinos have been found to be less likely to complete advance directives, use hospice services at the end of life, and are more likely to experience a hospital death.[29, 30, 31, 32, 33] As care at the end of life continues to improve, careful attention should be paid to ensure that these kinds of gaps do not widen any further.

We interviewed patients at an index hospitalization. Patients had an acute medical illness or an exacerbation of a chronic medical illness and required at least 24 hours of hospitalization to be eligible for inclusion. Our bedside interview made use of an opportune time to question patients, a time when it may have been easier for patients to visualize severe illness at the end of life, rather than asking this question during a time of wellness. Although participants overwhelmingly stated they preferred to be at home, for those who died, decisions were made in their care that did not allow for this preference.

Our follow‐up after the initial bedside interview only included death records of where and when participants died. We do not have the details and narrative of the conversations that may have taken place that led to the care decisions that determined participants' actual place of death. We do not know if preferences were elicited or discussed, and care decisions then negotiated, to best meet the goals and preferences expressed at that time. We also do not know if the conversations did not occur and the default of medical intervention and cure‐focused care dictated how participants spent the last days of their life. There is evidence that when conversations about goals and preferences do occur, concordance between preferences and care received are high.[12, 21]

We were unable to identify any predictors beyond gender in this cohort of adults hospitalized with a broad spectrum of severe medical illness to predict concordance with stated preferences and actual site of death. We can conclude then, based on our findings and supported by the literature, that the default trends toward institutional end‐of‐life experiences. To shift to a more patient‐centered approach, away from the default, healthcare providers need to embrace a palliative approach and incorporate preferences and goals into the discussions about next steps of care to facilitate the peaceful death that the majority of patients imagine for themselves. Hospitalist physicians have a unique opportunity at an index hospitalization to start the conversation about preferences for care including where patients would want to spend the last days of their life.

Our study does have some limitations. We elicited preferences at a single point in time, at an index hospitalization. It is possible that participants' preferences changed over the course of their illness. However, in Agar et al.'s study of longitudinal patient preferences for site of death and place of care, most preferences remained stable over time.[18] We also did not have data that included palliative care involvement, homecare or hospice utilization, or cause of death. All of these variables may be important predictors of concordance. Issues of symptom management and lack of caregiver may also dictate place of death, even when goals and care are aligned. We do not have data to address these components of end‐of‐life decision making.

CONCLUSION

Patients continue to express a preference for death at home. However, the majority of patients experienced an institutional death. Furthermore, few participants achieved concordance with where they preferred to die and where they actually died. Female gender was the sole factor associated with concordance between preferred and actual site of death. Incorporating a palliative approach that elicits goals and helps match goals to care, may offer the best opportunity to help people die where they chose.

Disclosures: This research was supported by the Brookdale National Fellowship Award and the NIA/Beeson grant 5K23AG028957. All authors have seen and agree with the contents of the article. This submission was not under review by any other publication. The authors have no financial interest or other potential conflicts of interest.

At the turn of the 20th century, most deaths in the United States occurred at home. By the 1960s, over 70% of deaths occurred in an institutional setting, reflecting an evolution of medical technology.[1, 2, 3] With the birth of the hospice movement in the 1970s, dying patients had the opportunity to have both death at home and aggressive symptom control at the end of life. Although there has been a slow decline in the proportion of deaths that occur in the hospital over the past 2 decades,[3] the overwhelming majority of persons state that they would prefer to die at home. However, recent findings suggest that most people will die in an institutional setting.[3, 4, 5, 6]

Although good data exist describing population preferences for location of death, and we know, based on death records, where deaths occur in the United States, there are few studies that examine concordance between preferred and actual site of death at the individual patient level. Furthermore, although factors have been identified that predict death at home, factors predicting concordance between preferred and actual site of death are not well described.[3, 6, 7, 8, 9, 10, 11, 12, 13]

Regardless of where death ultimately occurs, most adults will experience multiple hospitalizations within the last years of their life. Understanding the preferences and subsequent experiences of this population is of particular relevance to hospitalist physicians who are in a unique position to elicit goals from seriously ill patients and help match patient preferences with their medical care. In this observational study, we sought to determine preferences for site of death in a cohort of adult patients admitted to the hospital for medical illness, and then follow those patients to determine where death occurred for those who died. We also sought to explore factors that may predict concordance between preferred and actual site of death. We hypothesized that ethnic diversity and lower socioeconomic status would be associated with a lower likelihood of concordance between preferred and actual site of death. We also hypothesized that advanced care planning would be associated with a higher likelihood of concordance. The Colorado Multi‐Institutional Review Board approved this study.

METHODS

Participants were recruited from 3 hospitals affiliated with the University of Colorado School of Medicine Internal Medicine Residency program, including the Denver Veterans' Administration Center (DVAMC), Denver Health Medical Center (DHMC), and University of Colorado Hospital (UCH). The DVAMC is a large urban Veterans Administration hospital, serving veterans from the Denver metro area, and is a tertiary referral center for veterans in rural Colorado, Wyoming, and parts of Montana. DHMC, the safety‐net hospital for the Denver area, serves over 25% of the residents in the city and county of Denver, including such special populations as the indigent, chronically mentally ill, and persons with polysubstance dependence. UCH had 350 licensed beds at the time of our study and serves as the Rocky Mountain region's only academic tertiary, specialty care, and referral center. At the time of this study, there was limited inpatient palliative care services at the DVAMC and UH, and no palliative care services at DHMC. Participants were screened on the first day following admission to the adult general medical service. Participants were recruited on 96 postadmission days between February 2004 and June 2006. Recruitment days varied from Monday through Friday, to include admissions from the weekend and throughout the year to reduce potential bias due to seasonal trends of diseases such as influenza. Patients were excluded if they died or were discharged within the first 24 hours of admission, were pregnant, jailed, or unable to give informed consent. All other patients were approached and invited to participate in a brief survey.

After informed consent was obtained, participants completed a bedside interview that included self‐identified ethnicity and the Berkman‐Syme Social Network Index,[14] a brief questionnaire quantifying social support from spouse or domestic partner, family, friends, and other religious or secular organizations. Baseline socioeconomic measures (eg, income, employment, home ownership, car ownership) and questions related to the last days of life were also included. Participants were asked the following question, If you were very sick, with an illness that could not be cured, and in bed most of the time, where would you spend the last days of your life if you could chose?

For each participant, we performed a detailed chart review to determine demographic data, presence of advance directives, and CARING criteria (Cancer, Admissions 2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, 2 Noncancer hospice Guidelines), a set of prognostic criteria identifying patients at an index hospitalization who have a high burden of illness and are at risk for death in the following year.[15] We then followed patients for 5 years. If participants died within the follow‐up period, we collected the date and location of death using medical records, death certificates, or in a few cases when official death records were unavailable, direct contact with the family. Participants were considered alive if they had a clinic visit or MD/RN phone contact within 3 months prior to the final collection point date.

RESULTS

Preferred Site of Death

When asked where they preferred to spend the last days of their life, 75% of patients (n=343) stated they would like to be at home. In the hospital was the preferred location for 10% of patients, whereas 6% stated a nursing home and 4% a hospice inpatient facility. Two percent stated they had no preference, and 3% refused to answer (Figure 1)

Figure 1

We found that in the univariable analysis the following factors were associated with preference for site of death at a significance level of P<0.25: unstable housing, hospital setting, income level, ethnicity, CARING criteria, presence of an advance directive, education level, married, primary care provider, and presence of public insurance. Results of the logit models (home vs nursing home or hospice facility, and hospital vs nursing home or hospice facility and home vs hospital) are presented in Table 2.

Table 2.Logistic Regression Modeling of Preference for Death at Home or Hospital

	Adjusted Odds Ratio (95% Confidence Interval)
	Home vs Nursing Home/Hospice Facility	Hospital vs Nursing Home/Hospice Facility	Home vs Hospital
NOTE: Abbreviations: CARING, Cancer, Admissions 2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, 2 Noncancer hospice Guidelines.
Low income	2.71 (1.305.67)	3.05 (1.019.24)	0.99 (0.422.37)
Married	2.44 (1.145.21)	2.40 (0.876.62)	0.82 (0.421.57)
CARING criteria	0.58 (0.301.14)	0.44 (0.181.09)	0.89 (0.471.66)

Patients with income <$30,000/year were more likely to prefer home (or hospital) over a nursing home or hospice facility. Being married was predictive of preferring home over nursing home or hospice facility. Patients meeting 1 of the CARING criteria trended toward being less likely (P=0.11 for home and P=0.08 for hospital) to prefer home (or hospital) vs nursing home or hospice facility. However, there were no significant predictors for a preference for home or hospital when directly comparing the 2 locations, as expected from observing similar effects of variables in the other 2 logit models.

DISCUSSION

We found, similarly to previous reports in the literature, the majority of patients preferred to die at home. We did not find a significant difference in preferences or location of death by ethnicity or illness severity. Lower‐income patients and married patients were more likely to prefer to be at home over a nursing home or a hospice facility in the last days of life. We found that the minority of patients died at their stated preferred site of death, and female gender was the only predictive variable we found to distinguish those patients who died in a place concordant with their wishes compared to those who did not.

In the literature, previous studies have reported concordance rates between preferred and actual site of death that range from 30% to 90%.[12, 13, 18, 19, 20, 21, 22, 23, 24] We found a concordance rate at the lowest end of this spectrum. In trying to understand our findings and place them in context, it is helpful to examine other studies. Many of these studies focused solely on cancer patients.[13, 18, 19, 20, 21, 22, 23] Cancer follows a more predictable trajectory of decline compared to other common life‐threatening illnesses, such as cardiac disease, emphysema, or liver failure, that often involve periods of acute deteriorations and plateaus throughout illness progression. The more predictable trajectory may explain the overall higher concordance rates found in the studies involving cancer patients.

The majority of studies in the literature examining concordance between preferred and actual site of death recruited the study sample from home health or home palliative care programs that were providing support and care to participants.[10, 12, 13, 18, 22, 25, 26, 27] The high concordance rates reported may be the result of the patients in the sample receiving services at home aimed at eliciting preferences and providing support at home. Our observational study is unique in that we elicited patient preferences from a diverse group of hospitalized adults. Patients had a broad range of medical illness and were at various stages in their disease trajectory. This allowed our findings to be more generalizable, a major strength of our study.

The only variable associated with concordance that we identified to predict concordance between preferred and actual site of death was female gender. Women have been shown to be more active in medical decision making, which may explain our findings.[28] Female gender and illness severity (as measured by the CARING criteria) were found to be associated with concordance when the preference is for death at home. For persons with more advanced medical illness, they may have had more opportunity to consider their preferences and talk about these preferences. It is even possible that our interview prompted some participants to have discussions with their families or providers.

Variables with high face validity, such as high social support, higher education, and completing an advance directive, did not demonstrate any effect on the outcome of concordance. Other studies have shown that low functional status, Caucasian ethnicity, home care, higher education, and social support have been associated with a greater likelihood for a home death.[3, 6, 9] However, although studies specifically examining concordance between preferred and actual site of death have looked at predictors for home death, we were unable to find predictors for concordance across all preferences in the literature. We can conclude from our findings that the factors that influence concordance of preferences for site of death are extremely complex and difficult to capture and measure. This is extremely unsatisfying in the face of the low concordance rate of 30% we identified.

Latino ethnicity showed a trend toward having a negative association with concordance between preferred and actual site of death. This trend persisted whether it was concordance overall or for concordance with those who preferred a death at home. In the literature, Latinos have been found to be less likely to complete advance directives, use hospice services at the end of life, and are more likely to experience a hospital death.[29, 30, 31, 32, 33] As care at the end of life continues to improve, careful attention should be paid to ensure that these kinds of gaps do not widen any further.

We interviewed patients at an index hospitalization. Patients had an acute medical illness or an exacerbation of a chronic medical illness and required at least 24 hours of hospitalization to be eligible for inclusion. Our bedside interview made use of an opportune time to question patients, a time when it may have been easier for patients to visualize severe illness at the end of life, rather than asking this question during a time of wellness. Although participants overwhelmingly stated they preferred to be at home, for those who died, decisions were made in their care that did not allow for this preference.

Our follow‐up after the initial bedside interview only included death records of where and when participants died. We do not have the details and narrative of the conversations that may have taken place that led to the care decisions that determined participants' actual place of death. We do not know if preferences were elicited or discussed, and care decisions then negotiated, to best meet the goals and preferences expressed at that time. We also do not know if the conversations did not occur and the default of medical intervention and cure‐focused care dictated how participants spent the last days of their life. There is evidence that when conversations about goals and preferences do occur, concordance between preferences and care received are high.[12, 21]

We were unable to identify any predictors beyond gender in this cohort of adults hospitalized with a broad spectrum of severe medical illness to predict concordance with stated preferences and actual site of death. We can conclude then, based on our findings and supported by the literature, that the default trends toward institutional end‐of‐life experiences. To shift to a more patient‐centered approach, away from the default, healthcare providers need to embrace a palliative approach and incorporate preferences and goals into the discussions about next steps of care to facilitate the peaceful death that the majority of patients imagine for themselves. Hospitalist physicians have a unique opportunity at an index hospitalization to start the conversation about preferences for care including where patients would want to spend the last days of their life.

Our study does have some limitations. We elicited preferences at a single point in time, at an index hospitalization. It is possible that participants' preferences changed over the course of their illness. However, in Agar et al.'s study of longitudinal patient preferences for site of death and place of care, most preferences remained stable over time.[18] We also did not have data that included palliative care involvement, homecare or hospice utilization, or cause of death. All of these variables may be important predictors of concordance. Issues of symptom management and lack of caregiver may also dictate place of death, even when goals and care are aligned. We do not have data to address these components of end‐of‐life decision making.

CONCLUSION

Patients continue to express a preference for death at home. However, the majority of patients experienced an institutional death. Furthermore, few participants achieved concordance with where they preferred to die and where they actually died. Female gender was the sole factor associated with concordance between preferred and actual site of death. Incorporating a palliative approach that elicits goals and helps match goals to care, may offer the best opportunity to help people die where they chose.

Disclosures: This research was supported by the Brookdale National Fellowship Award and the NIA/Beeson grant 5K23AG028957. All authors have seen and agree with the contents of the article. This submission was not under review by any other publication. The authors have no financial interest or other potential conflicts of interest.