Identification of Factors Associated with Hospital Readmission and Development of a Predictive Model Janet M. Corrigan and James B. Martin Multiple hospital admissions, especially those related to the chronically ill, represent a particular challenge to both the acute and long-term care sectors to identify effective methods of resource management. This study analyzes the multiple admission patterns associated with a cohort of 4,219 adult medical-surgical patients discharged alive from a community teaching hospital in Michigan. The sample was divided into two groups: 3,818 patients who survived and 392 who expired during the one-yearfollow-up period. For the surviving subsample, the characteristics found to be directly associated with the likelihood of readmission included increased age, advanced stage of disease, greater index-episode length of stay, discharge by an internist rather than a surgeon, Medicare as expected source of payment, decreased physician age, discharge to a community setting, and increased number ofprior hospital episodes. For the subsample who died, only one explanatory variable was significantly associated with an increased likelihood of readmission - discharge to a community setting (with or without home care) rather than a nursing home. The article includes illustrations of the importance of decisions regarding posthospital, long-term care services on the likelihood of
rehospitalization. It has been recognized for some time that a small proportion of the population accounts for a disproportionate share of the consumption of This research was supported in part by a doctoral student award from the Michigan Health Care Education and Research Foundation, and through the generosity of an anonymous hospital. Address correspondence and requests for reprints toJanet M. Corrigan, Ph.D., Director, Planning and Development, National Committee for Quality Assurance, 1730 Rhode Island Avenue, N.W., Suite 307, Washington, DC 20036. James B. Martin, Ph.D., is Associate Professor of Health Services Management and Policy at the School of Public Health, University of Michigan, Ann Arbor. This article, submitted to Health Services Research on September 5, 1990, was revised and accepted for publication on May 16, 1991.
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health care resources (McCall and Wai 1983; Roos and Shapiro 1981). Approximately one out of every six or seven patients admitted to a hospital experiences a second admission within less than one year, and for Medicare beneficiaries the readmission rate may be as high as 22 to 27 percent within 60 days of discharge (Anderson and Steinberg 1984; Holloway, Thomas, and Shapiro 1988). Multiple admissions particularly concern providers, patients, and payers as possible indications of poor quality or inefficiency in providing patient care. To the extent that multiple admissions are attributable to care of the chronically ill, they present a particular challenge to both the acute and long-term care sectors to identify effective methods of management. The objectives of this analysis are to identify patient and health system characteristics associated with readmission, and to construct statistical models that can be used to estimate the probability of readmission for a given patient at some future point in time.
LITERATURE REVIEW Numerous studies have been conducted to identify the relationship of various patient and health system characteristics to the likelihood of readmission (Graham and Livesley 1983; Anderson and Steinberg 1984; Victor and Vetter 1985; Roos et al. 1986b; Fethke, Smith, and Johnson 1986; Roos et al. 1986a; Holloway, Thomas, and Shapiro 1988; Holloway, Medendorp, and Bromberg 1990). Patient characteristics include demographic-social, clinical, and resource use characteristics. Health care provider and system characteristics include: physician specialty, age, experience, practice setting and caseload; hospital teaching status; and community supply of health resources. Age has been found to be positively associated with the likelihood of readmission until old age is reached; at that time there appears to be no significant relationship between age and readmission (Holloway, Medendorp, and Bromberg 1990; Holloway, Thomas, and Shapiro 1988; Fethke, Smith, and Johnson 1986). Males have higher readmission rates than females in studies employing shorter time windows (e.g., less than three months), while studies with longer time windows (e.g., one year) found no significant gender difference (Graham and Livesley 1983; Anderson and Steinberg 1984; Fethke, Smith, and Johnson 1986). Research has produced conflicting results regarding the effect of marital status on readmission, especially when various living arrange-
Readmission and a Predictive Model
83
ments and socioeconomic status are controlled for (Fethke, Smith, and Johnson 1986; Holloway, Medendorp, and Bromberg 1990). Several studies have examined the relationship between "living alone" and the likelihood of readmission and have reported insignificant findings (Graham and Livesley 1983; Victor and Vetter 1985; Fethke, Smith, and Johnson 1986; Holloway, Thomas, and Shapiro 1988). One study that included measures of income found no significant differences in readmission rates among individuals of various income levels (Fethke, Smith, and Johnson 1986), while another that sampled only from Medicare beneficiaries found that individuals having both Medicare and Medicaid coverage showed significantly higher rates of readmission than those with only Medicare coverage (Anderson and Steinberg 1984). Some evidence indicates that rural residence or increased distance from hospitals, or both, are associated with higher readmission rates (Anderson and Steinberg 1984; Holloway, Medendorp, and Bromberg 1990). Variations in readmission rates across diagnostic groups and surgical procedures have been well documented, with patients having certain chronic conditions or undergoing certain surgical procedures exhibiting higher readmission rates (Riley and Lubitz 1986; Zook, Savickis, and Moore 1980; Victor and Vetter 1985; Holloway, Medendorp, and Bromberg 1990). Some studies have also documented an increased risk of readmission associated with the presence of multiple chronic conditions and the performance of multiple surgical procedures (Fethke, Smith, and Johnson 1986; Roos et al. 1986a; Holloway, Thomas, and Shapiro 1988; Holloway, Medendorp, and Bromberg 1990). Higher readmission rates have also been found to be associated with poorer health status and impairments of sight or hearing, motor disabilities, and incontinence (Holloway, Thomas, and Shapiro 1988; Anderson and Steinberg 1984; Victor and Vetter 1985). Two studies have reported a direct relationship between prior hospitalization patterns and the probability of readmission (Fethke, Smith, and Johnson 1986; Roos et al. 1986a). No significant association has been found between the length of stay for the index episode and the likelihood of readmission (Victor and Vetter 1985; Holloway, Medendorp, and Bromberg 1990). Very little information is available regarding the use of nonhospital services subsequent to hospital discharge and its effect on readmission. Holloway, Mendendorp, and Bromberg (1990) found no association between readmission and the use of nursing homes or other subacute institutional services subsequent to hospital discharge, but they did find that discharge from an intermediate care ward in a
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Department of Veterans Affairs medical center was highly predictive of early readmission. In an analysis of the hospital utilization patterns of primary physicians, Roos and colleagues (1986b) found that, after adjusting for differences in case mix, increasing physician age was generally associated with (1) a lower percentage of patients in the practice who were hospitalized; (2) lower readmission rates; (3) an increased average length of stay; and (4) a lower mean number of hospital days per patient in the practice per year. Physicians with appointments in teaching hospitals averaged significantly fewer days of hospitalization per patient in their practices, and rural physicians exhibited higher hospital utilization rates in the form of higher percentages of patients hospitalized, but lower average lengths of stay. Roos et al. found no significant differences in the readmission rates between physicians in solo and in group practice, but they did find that increased bed supply relative to the population in an area was directly related to higher readmission rates (Roos et al. 1986b). This study contributes to the current knowledge base regarding readmissions by validating some of the results of earlier studies, and by incorporating better measures of case mix, severity of illness, and longterm care utilization.
METHODOLOGY The data used in the analysis pertain to adult (age over 14 years) medical or surgical patients, who received services at a 500-bed community-based, teaching hospital ("Community Hospital") located in the metropolitan Detroit area of southeastern Michigan. Nearly two-thirds of the Community Hospital patients are drawn from its primary service area, consisting of a single county and several adjacent townships, and the remaining one-third from surrounding rural areas. In addition to Community Hospital are a university hospital and three small private hospitals whose service areas overlap in part with the service area of Community Hospital. In 1984, active medical staff members at Community Hospital numbered 245, of which approximately 48 percent and 30 percent were credentialed within the departments of medicine and surgery, respectively. The study sample was drawn from patients hospitalized at Community Hospital during calendar year 1985. For each patient, the earliest 1985 discharge was designated the "index episode." The num-
Readmission and a Predictive Model
85
ber of discharges during the 365-day period prior to the patient's index episode represented the "number of prior episodes." The "number of readmissions" was the total number of admissions during the 365-day period subsequent to the index episode. For an accurate determination of the number of prior episodes and the occurrence and number of readmissions, knowledge of the patient's complete pattern of hospitalization was necessary. Since data available for use in this study pertained only to patient hospitalizations at Community Hospital, not other area hospitals, the possibility existed that counts of prior episodes and readmissions would be underestimated, and in some instances that patients experiencing recurrent hospitalization might be misclassified as single-episode patients. In order to minimize the deleterious effects of incomplete data, the sample was selected to exclude both patients who very probably would use another hospital, and those who were known to have been hospitalized at another acute care facility. Specifically, the sample included only patients whose discharging attending physician at the time of the focal episode practiced exclusively at Community Hospital,' and patients who resided in the 16 zip code areas proximate to the hospital where the hospital's market penetration was greatest.2 In addition, a small number of patients (N = 98) known to have been admitted from or discharged to another hospital at the time of their index episode were excluded. After exclusions, the sample consisted of 4,219 patients, representing approximately 33 percent of all adult medical-surgical patients discharged alive from Community Hospital in 1985. To ascertain whether the selective sampling process had effectively excluded most patients who used other hospitals, aggregate information (patient-specific data were not available due to confidentiality restrictions) was obtained from the Michigan Peer Review Organization regarding the utilization of other hospitals in Michigan by the Medicare patients who constituted nearly 41 percent of the sample. Approximately 7.5 percent of the Medicare patients in the sample were readmitted to another hospital during the 365-day period subsequent to their index discharge. It is reasonable to assume that Medicare patients are generally at greater risk of readmission than are nonMedicare patients, since Medicare is the primary payer for the elderly and severely disabled. Consequently, for the sample as a whole, the percentage of patients readmitted to other hospitals was probably somewhat lower. The selective sampling process appears to have been successful in excluding most patients who used other hospitals. There were five major sources of data: Community Hospital's patient discharge abstract and accounts receivable data; physician data
86
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sets containing descriptive information on the medical staff, such as respective specialty, age, and caseload; information from the hospital's social service records on the posthospital placement of patients in nursing homes or with home health agencies; information regarding the occurrence and timing of fatalities from the death registry of the Michigan Department of Vital Statistics; and estimates of the median income associated with various zip code areas derived from the 1980 United States Census (Donnelley Demographics 1986). Prior to the regression analyses, the patients in the sample were assigned to one of two groups-readmitted and not readmitted. The readmitted subsample consisted of those patients having at least one "unplanned" readmission within 365 days of their index episode.3 Table 1 provides a list of the predictor and dependent variables used in the study. The dependent variable was "the occurrence of at least one unplanned readmission." There were three groups of predictor variables: patient demographic, socioeconomic, and health status characteristics; measures of the discharging physician's level of experience and performance; and measures of the patient's acute and longterm care service use before, during, and after the index episode. The Cox proportional hazards regression model was used because the dependent variable was dichotomous and asymmetric, and also because this model provided a graphic depiction of the "time-toresponse" (Dixon 1985).
RESULTS The average and median ages for the sample were 56 and 58 years, respectively, and approximately 53 percent were female. Nearly 62 percent of the sample patients were discharged by physicians from the internal medicine service with the remaining 38 percent from the surgical service. Medicare and Blue Cross were the principal payers for about 41 percent and 35 percent of patients, respectively. The average length of stay was 6.72 days. About 66 percent of the study patients had no Community Hospital discharges during the 365-day period subsequent to their index discharge, while 21 percent had one readmission, about 8 percent hadQtwo, and a little over 5 percent had three or more. In the first phase of the analysis, the sample was divided into two groups-those who survived (N = 3,823) and those who died during the 365-day follow-up period subsequent to their index hospital episode (N = 396). Surviving and expired patients were analyzed separately
Readmission and a Predictive Model
87
Table 1: List of Variables PREDICTOR VARIABLES Patient Socio/Demographic Age Gender Income: median income of area of residence (zip code) Payer: Medicare, Medicaid, Blue Cross, other Patient Clinical MDC: Major diagnostic category (DRG Support Group Ltd. 1983) Stage: Stage of illness on an increasing scale of 1 through 4 (see Systemetrics, Inc. 1984) Blind* Deaf* Paralysis* Mental: Mental deficiency* Patient Resource Use Prior EPI: prior hospital episodes during previous one year LOS: Index episode length of stay Nursing Home: Discharge to nursing home after index episode Home Health: Discharge with home health services after index episode Physician Quality and Efficiency MD-Age: Physician age (
rehospitalization. It has been recognized for some time that a small proportion of the population accounts for a disproportionate share of the consumption of This research was supported in part by a doctoral student award from the Michigan Health Care Education and Research Foundation, and through the generosity of an anonymous hospital. Address correspondence and requests for reprints toJanet M. Corrigan, Ph.D., Director, Planning and Development, National Committee for Quality Assurance, 1730 Rhode Island Avenue, N.W., Suite 307, Washington, DC 20036. James B. Martin, Ph.D., is Associate Professor of Health Services Management and Policy at the School of Public Health, University of Michigan, Ann Arbor. This article, submitted to Health Services Research on September 5, 1990, was revised and accepted for publication on May 16, 1991.
82
HSR: Health Services Research 27:1 (April 1992)
health care resources (McCall and Wai 1983; Roos and Shapiro 1981). Approximately one out of every six or seven patients admitted to a hospital experiences a second admission within less than one year, and for Medicare beneficiaries the readmission rate may be as high as 22 to 27 percent within 60 days of discharge (Anderson and Steinberg 1984; Holloway, Thomas, and Shapiro 1988). Multiple admissions particularly concern providers, patients, and payers as possible indications of poor quality or inefficiency in providing patient care. To the extent that multiple admissions are attributable to care of the chronically ill, they present a particular challenge to both the acute and long-term care sectors to identify effective methods of management. The objectives of this analysis are to identify patient and health system characteristics associated with readmission, and to construct statistical models that can be used to estimate the probability of readmission for a given patient at some future point in time.
LITERATURE REVIEW Numerous studies have been conducted to identify the relationship of various patient and health system characteristics to the likelihood of readmission (Graham and Livesley 1983; Anderson and Steinberg 1984; Victor and Vetter 1985; Roos et al. 1986b; Fethke, Smith, and Johnson 1986; Roos et al. 1986a; Holloway, Thomas, and Shapiro 1988; Holloway, Medendorp, and Bromberg 1990). Patient characteristics include demographic-social, clinical, and resource use characteristics. Health care provider and system characteristics include: physician specialty, age, experience, practice setting and caseload; hospital teaching status; and community supply of health resources. Age has been found to be positively associated with the likelihood of readmission until old age is reached; at that time there appears to be no significant relationship between age and readmission (Holloway, Medendorp, and Bromberg 1990; Holloway, Thomas, and Shapiro 1988; Fethke, Smith, and Johnson 1986). Males have higher readmission rates than females in studies employing shorter time windows (e.g., less than three months), while studies with longer time windows (e.g., one year) found no significant gender difference (Graham and Livesley 1983; Anderson and Steinberg 1984; Fethke, Smith, and Johnson 1986). Research has produced conflicting results regarding the effect of marital status on readmission, especially when various living arrange-
Readmission and a Predictive Model
83
ments and socioeconomic status are controlled for (Fethke, Smith, and Johnson 1986; Holloway, Medendorp, and Bromberg 1990). Several studies have examined the relationship between "living alone" and the likelihood of readmission and have reported insignificant findings (Graham and Livesley 1983; Victor and Vetter 1985; Fethke, Smith, and Johnson 1986; Holloway, Thomas, and Shapiro 1988). One study that included measures of income found no significant differences in readmission rates among individuals of various income levels (Fethke, Smith, and Johnson 1986), while another that sampled only from Medicare beneficiaries found that individuals having both Medicare and Medicaid coverage showed significantly higher rates of readmission than those with only Medicare coverage (Anderson and Steinberg 1984). Some evidence indicates that rural residence or increased distance from hospitals, or both, are associated with higher readmission rates (Anderson and Steinberg 1984; Holloway, Medendorp, and Bromberg 1990). Variations in readmission rates across diagnostic groups and surgical procedures have been well documented, with patients having certain chronic conditions or undergoing certain surgical procedures exhibiting higher readmission rates (Riley and Lubitz 1986; Zook, Savickis, and Moore 1980; Victor and Vetter 1985; Holloway, Medendorp, and Bromberg 1990). Some studies have also documented an increased risk of readmission associated with the presence of multiple chronic conditions and the performance of multiple surgical procedures (Fethke, Smith, and Johnson 1986; Roos et al. 1986a; Holloway, Thomas, and Shapiro 1988; Holloway, Medendorp, and Bromberg 1990). Higher readmission rates have also been found to be associated with poorer health status and impairments of sight or hearing, motor disabilities, and incontinence (Holloway, Thomas, and Shapiro 1988; Anderson and Steinberg 1984; Victor and Vetter 1985). Two studies have reported a direct relationship between prior hospitalization patterns and the probability of readmission (Fethke, Smith, and Johnson 1986; Roos et al. 1986a). No significant association has been found between the length of stay for the index episode and the likelihood of readmission (Victor and Vetter 1985; Holloway, Medendorp, and Bromberg 1990). Very little information is available regarding the use of nonhospital services subsequent to hospital discharge and its effect on readmission. Holloway, Mendendorp, and Bromberg (1990) found no association between readmission and the use of nursing homes or other subacute institutional services subsequent to hospital discharge, but they did find that discharge from an intermediate care ward in a
84
HSR: Health Services Research 27:1 (April 1992)
Department of Veterans Affairs medical center was highly predictive of early readmission. In an analysis of the hospital utilization patterns of primary physicians, Roos and colleagues (1986b) found that, after adjusting for differences in case mix, increasing physician age was generally associated with (1) a lower percentage of patients in the practice who were hospitalized; (2) lower readmission rates; (3) an increased average length of stay; and (4) a lower mean number of hospital days per patient in the practice per year. Physicians with appointments in teaching hospitals averaged significantly fewer days of hospitalization per patient in their practices, and rural physicians exhibited higher hospital utilization rates in the form of higher percentages of patients hospitalized, but lower average lengths of stay. Roos et al. found no significant differences in the readmission rates between physicians in solo and in group practice, but they did find that increased bed supply relative to the population in an area was directly related to higher readmission rates (Roos et al. 1986b). This study contributes to the current knowledge base regarding readmissions by validating some of the results of earlier studies, and by incorporating better measures of case mix, severity of illness, and longterm care utilization.
METHODOLOGY The data used in the analysis pertain to adult (age over 14 years) medical or surgical patients, who received services at a 500-bed community-based, teaching hospital ("Community Hospital") located in the metropolitan Detroit area of southeastern Michigan. Nearly two-thirds of the Community Hospital patients are drawn from its primary service area, consisting of a single county and several adjacent townships, and the remaining one-third from surrounding rural areas. In addition to Community Hospital are a university hospital and three small private hospitals whose service areas overlap in part with the service area of Community Hospital. In 1984, active medical staff members at Community Hospital numbered 245, of which approximately 48 percent and 30 percent were credentialed within the departments of medicine and surgery, respectively. The study sample was drawn from patients hospitalized at Community Hospital during calendar year 1985. For each patient, the earliest 1985 discharge was designated the "index episode." The num-
Readmission and a Predictive Model
85
ber of discharges during the 365-day period prior to the patient's index episode represented the "number of prior episodes." The "number of readmissions" was the total number of admissions during the 365-day period subsequent to the index episode. For an accurate determination of the number of prior episodes and the occurrence and number of readmissions, knowledge of the patient's complete pattern of hospitalization was necessary. Since data available for use in this study pertained only to patient hospitalizations at Community Hospital, not other area hospitals, the possibility existed that counts of prior episodes and readmissions would be underestimated, and in some instances that patients experiencing recurrent hospitalization might be misclassified as single-episode patients. In order to minimize the deleterious effects of incomplete data, the sample was selected to exclude both patients who very probably would use another hospital, and those who were known to have been hospitalized at another acute care facility. Specifically, the sample included only patients whose discharging attending physician at the time of the focal episode practiced exclusively at Community Hospital,' and patients who resided in the 16 zip code areas proximate to the hospital where the hospital's market penetration was greatest.2 In addition, a small number of patients (N = 98) known to have been admitted from or discharged to another hospital at the time of their index episode were excluded. After exclusions, the sample consisted of 4,219 patients, representing approximately 33 percent of all adult medical-surgical patients discharged alive from Community Hospital in 1985. To ascertain whether the selective sampling process had effectively excluded most patients who used other hospitals, aggregate information (patient-specific data were not available due to confidentiality restrictions) was obtained from the Michigan Peer Review Organization regarding the utilization of other hospitals in Michigan by the Medicare patients who constituted nearly 41 percent of the sample. Approximately 7.5 percent of the Medicare patients in the sample were readmitted to another hospital during the 365-day period subsequent to their index discharge. It is reasonable to assume that Medicare patients are generally at greater risk of readmission than are nonMedicare patients, since Medicare is the primary payer for the elderly and severely disabled. Consequently, for the sample as a whole, the percentage of patients readmitted to other hospitals was probably somewhat lower. The selective sampling process appears to have been successful in excluding most patients who used other hospitals. There were five major sources of data: Community Hospital's patient discharge abstract and accounts receivable data; physician data
86
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sets containing descriptive information on the medical staff, such as respective specialty, age, and caseload; information from the hospital's social service records on the posthospital placement of patients in nursing homes or with home health agencies; information regarding the occurrence and timing of fatalities from the death registry of the Michigan Department of Vital Statistics; and estimates of the median income associated with various zip code areas derived from the 1980 United States Census (Donnelley Demographics 1986). Prior to the regression analyses, the patients in the sample were assigned to one of two groups-readmitted and not readmitted. The readmitted subsample consisted of those patients having at least one "unplanned" readmission within 365 days of their index episode.3 Table 1 provides a list of the predictor and dependent variables used in the study. The dependent variable was "the occurrence of at least one unplanned readmission." There were three groups of predictor variables: patient demographic, socioeconomic, and health status characteristics; measures of the discharging physician's level of experience and performance; and measures of the patient's acute and longterm care service use before, during, and after the index episode. The Cox proportional hazards regression model was used because the dependent variable was dichotomous and asymmetric, and also because this model provided a graphic depiction of the "time-toresponse" (Dixon 1985).
RESULTS The average and median ages for the sample were 56 and 58 years, respectively, and approximately 53 percent were female. Nearly 62 percent of the sample patients were discharged by physicians from the internal medicine service with the remaining 38 percent from the surgical service. Medicare and Blue Cross were the principal payers for about 41 percent and 35 percent of patients, respectively. The average length of stay was 6.72 days. About 66 percent of the study patients had no Community Hospital discharges during the 365-day period subsequent to their index discharge, while 21 percent had one readmission, about 8 percent hadQtwo, and a little over 5 percent had three or more. In the first phase of the analysis, the sample was divided into two groups-those who survived (N = 3,823) and those who died during the 365-day follow-up period subsequent to their index hospital episode (N = 396). Surviving and expired patients were analyzed separately
Readmission and a Predictive Model
87
Table 1: List of Variables PREDICTOR VARIABLES Patient Socio/Demographic Age Gender Income: median income of area of residence (zip code) Payer: Medicare, Medicaid, Blue Cross, other Patient Clinical MDC: Major diagnostic category (DRG Support Group Ltd. 1983) Stage: Stage of illness on an increasing scale of 1 through 4 (see Systemetrics, Inc. 1984) Blind* Deaf* Paralysis* Mental: Mental deficiency* Patient Resource Use Prior EPI: prior hospital episodes during previous one year LOS: Index episode length of stay Nursing Home: Discharge to nursing home after index episode Home Health: Discharge with home health services after index episode Physician Quality and Efficiency MD-Age: Physician age (
