Hospital readmissions that occur within 30 days of discharge are an important measure for assessing performance of the healthcare system and the quality of patient care.[1, 2] According to the Healthcare Cost and Utilization Project (HCUP), there were approximately 3.3 million adults with all‐cause 30‐day readmissions in the United States in 2011, incurring nearly $41.3 billion in hospital costs.[3] Reducing 30‐day readmissions has become a priority for payers, providers, and policymakers seeking to achieve improved quality of care at lower costs.

The implementation of the Affordable Care Act (ACA) provided the Centers for Medicare & Medicaid Services (CMS) statutory authority under the Hospital Readmissions Reduction Program to reduce payments for certain hospital readmissions that it deemed avoidable.[4] Although initial focus was on Medicare readmissions related to heart failure, myocardial infarction, and pneumonia, CMS is now considering expanding the list beyond the 3 conditions covered by the program.[4, 5] Therefore, it is important to understand major risk factors for readmissions in beneficiaries with chronic conditions.

Medicaid consists of the largest number of beneficiaries among all payers in the United States, with approximately 62 million beneficiaries in 2013.[5] The Medicaid population is further expected to increase with the coverage expansions under the ACA. In addition, the state Medicaid programs incur an estimated $374 billion in healthcare expenditures and provide healthcare services to the vulnerable, indigent, and disabled. It is estimated that 61% of adult Medicaid beneficiaries have chronic or disabling conditions that place them at an increased risk of hospitalization.[6] A series of HCUP statistical briefs reported several findings. First, Medicaid all‐cause readmission rates were comparable with Medicare but double the rate of private insurance.[7] Second, for readmissions following nonsurgical hospitalizations, 30‐day Medicaid readmission rates were higher than Medicare and private insurance for both acute and chronic conditions.[1] The effects of such costly utilization patterns, for this large and growing population necessitates heightened attention under healthcare reform.

The balance between hospital efficiency and quality of care is another crucial aspect for our healthcare system. However, length of stay (LOS), a proxy marker for efficiency, may conflict with hospital readmission rates, an indicator of quality. Further, CMS plans to bundle 30‐day readmission rates to reimbursement for the index hospitalization.[8]

The effect of LOS on readmission rates is complex, and previous studies have provided conflicting data regarding the relationship between LOS and subsequent readmission risk. Some indicate that shorter LOS is associated with a higher risk of readmission,[8, 9] whereas others suggest that extended LOS is associated with a higher risk of readmission.[10, 11, 12] However, most research on readmissions has focused on Medicare beneficiaries.[11, 13, 14] The readmission patterns of Medicaid beneficiaries differ from those of the geriatric Medicare beneficiaries, from a clinical and socioeconomic perspective. Considering the importance of 30‐day readmission for payers and policy makers, there is a need to understand the role of LOS and implications for treatment and management strategies.

Our study examined the association between index hospitalization characteristics (LOS and reason for admission) and all‐cause 30‐day readmission risk in fee‐for‐service high‐risk Medicaid beneficiaries. The study is limited to patients with selected chronic conditions and examines the differentiating factors within this high‐risk population. For the purpose of our study, variables were selected based on a priori knowledge and Andersen's behavioral model of health service utilization. This model suggests that potential health service use is determined by interactions among predisposing (demographics, index hospitalization characteristics), enabling (county level [eg, socioeconomic status]), and need (health status) characteristics of individuals and also the healthcare systems in the communities where they reside.[15]

METHODS

Study Population

The study population consisted of nonelderly (2164 years old) fee‐for‐service Medicaid‐only beneficiaries with selected chronic conditions and continuous enrollment during baseline and follow‐up period (Figure 1). Analyses were restricted to those who had at least 1 inpatient admission in 2007 and were conducted at the person‐level.

Figure 1

For the purpose of this study, Medicaid beneficiaries with 19 chronic conditions were selected: asthma, arthritis, cardiac arrhythmias, coronary artery disease, cancer, congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, dementia, diabetes, hypertension, hyperlipidemia, hepatitis, human immunodeficiency virus osteoporosis, stroke, depression, schizophrenia, and substance use disorders. These conditions were identified based on the strategic framework developed and adopted by the Department of Health and Human Services for research, policy, program, and practice.[16]

RESULTS

After the exclusion criteria, there were 15,806 Medicaid beneficiaries with selected chronic conditions and at least 1 inpatient encounter in 2007. Overall, 16.7% experienced all‐cause 30‐day readmissions. A description of the study population and unadjusted associations between independent variables and all‐cause 30‐day readmission are presented in Table 1.

Multilevel logistic regressions of all‐cause 30‐day readmissions are summarized in Table 2. Beneficiaries with longer LOS had significantly higher odds of 30‐day readmission. In addition, presence of cancer, cardiovascular conditions, diabetes, and mental health conditions at index hospitalization significantly increased the odds of readmission. In addition, beneficiaries with cash or medical need eligibility had significantly higher odds of 30‐day readmission.

DISCUSSION

To the best of our knowledge, this is the first study examining patient‐level and county‐level characteristics associated with all‐cause 30‐day readmission in Medicaid beneficiaries with chronic conditions. In addition, our findings add to the nascent literature on readmissions among Medicaid beneficiaries, with findings discussed below.

LOS has been reported as a risk factor for readmission both in elderly and nonelderly populations.[11] Our findings indicate that longer LOS is associated with increased odds of 30‐day readmission, which could be attributed to severity of illness at index hospitalization.[10] This finding could be related to unmeasured clinical severity (our models account for some comorbidities) and socioeconomic issues (as noted in the introduction). This may have implications for discharge planning efforts and focusing on chronic disease management, which has previously shown to be effective in reducing readmissions.[17] Our findings suggest 30‐day readmissions can be predicted using variables that are readily available, few in number, and simple to incorporate in discharge planning. Comprehensive discharge planning which takes into account chronic conditions and index hospitalization characteristics may help organize postdischarge services, including coordination of care with physicians, medication reconciliation, follow‐up care, and appropriate self‐management for chronic conditions.

Our findings of increased risk of 30‐day hospital readmissions as well as longer LOS among Medicaid beneficiaries with cancer, cardiovascular conditions, diabetes, and mental health conditions at index hospitalization suggests that patient complexity/poor health status increases the risk of readmission. A more focused approach in treatment of these diseases can help reduce readmissions. Integrated care management interventions after hospital discharge have been shown to reduce readmissions among those with heart disease; a coordinated care team including cardiologists, specialized nurses, and primary care physicians, and provision of integrated care following hospitalizations have shown benefit.[18, 19] Emerging models of delivery such as accountable care organizations and patient‐centered medical homes, which offer comprehensive, well‐coordinated primary care services, may be needed to reduce readmission among Medicaid beneficiaries with chronic health conditions. In this respect, 3 of the 4 states represented (California, New York, and Texas) are CMS Innovation Model partner states and are presently awardees of Medicaid Incentives for the Prevention of Chronic Disease state grants.[20] It remains to be seen whether such programs can reduce the high prevalence of readmissions in a Medicaid population.

Although our findings may have implications in reducing readmission risk, these results need to be interpreted in the light of study limitations. Our study was based on beneficiaries from only 4 states and cannot be generalized to the entire US Medicaid population. We also excluded individuals who were not enrolled in Medicaid health maintenance organizations. Given that less than one‐third of the population receives fee‐for‐service care in Medicaid, our study may have selection bias. Our study design utilized a retrospective cohort approach and cannot be used to establish causal relationships. Further, our study did not include adjustment for variables related to discharge planning or care coordination other than a primary care visit 14 days post discharge, which might influence the readmission risk of complex patients. Our study utilized data from administrative claims files.

Overall, our analyses revealed that patient complexities increased the risk of all‐cause 30‐day readmission for high‐risk Medicaid beneficiaries with chronic conditions, thus warranting the need for comprehensive care for those with chronic conditions. Programs designed to reduce the risk of 30‐day readmissions may need to focus on appropriate disease management and better coordinated care post hospitalization.

Hospital readmissions that occur within 30 days of discharge are an important measure for assessing performance of the healthcare system and the quality of patient care.[1, 2] According to the Healthcare Cost and Utilization Project (HCUP), there were approximately 3.3 million adults with all‐cause 30‐day readmissions in the United States in 2011, incurring nearly $41.3 billion in hospital costs.[3] Reducing 30‐day readmissions has become a priority for payers, providers, and policymakers seeking to achieve improved quality of care at lower costs.

The implementation of the Affordable Care Act (ACA) provided the Centers for Medicare & Medicaid Services (CMS) statutory authority under the Hospital Readmissions Reduction Program to reduce payments for certain hospital readmissions that it deemed avoidable.[4] Although initial focus was on Medicare readmissions related to heart failure, myocardial infarction, and pneumonia, CMS is now considering expanding the list beyond the 3 conditions covered by the program.[4, 5] Therefore, it is important to understand major risk factors for readmissions in beneficiaries with chronic conditions.

Medicaid consists of the largest number of beneficiaries among all payers in the United States, with approximately 62 million beneficiaries in 2013.[5] The Medicaid population is further expected to increase with the coverage expansions under the ACA. In addition, the state Medicaid programs incur an estimated $374 billion in healthcare expenditures and provide healthcare services to the vulnerable, indigent, and disabled. It is estimated that 61% of adult Medicaid beneficiaries have chronic or disabling conditions that place them at an increased risk of hospitalization.[6] A series of HCUP statistical briefs reported several findings. First, Medicaid all‐cause readmission rates were comparable with Medicare but double the rate of private insurance.[7] Second, for readmissions following nonsurgical hospitalizations, 30‐day Medicaid readmission rates were higher than Medicare and private insurance for both acute and chronic conditions.[1] The effects of such costly utilization patterns, for this large and growing population necessitates heightened attention under healthcare reform.

The balance between hospital efficiency and quality of care is another crucial aspect for our healthcare system. However, length of stay (LOS), a proxy marker for efficiency, may conflict with hospital readmission rates, an indicator of quality. Further, CMS plans to bundle 30‐day readmission rates to reimbursement for the index hospitalization.[8]

The effect of LOS on readmission rates is complex, and previous studies have provided conflicting data regarding the relationship between LOS and subsequent readmission risk. Some indicate that shorter LOS is associated with a higher risk of readmission,[8, 9] whereas others suggest that extended LOS is associated with a higher risk of readmission.[10, 11, 12] However, most research on readmissions has focused on Medicare beneficiaries.[11, 13, 14] The readmission patterns of Medicaid beneficiaries differ from those of the geriatric Medicare beneficiaries, from a clinical and socioeconomic perspective. Considering the importance of 30‐day readmission for payers and policy makers, there is a need to understand the role of LOS and implications for treatment and management strategies.

Our study examined the association between index hospitalization characteristics (LOS and reason for admission) and all‐cause 30‐day readmission risk in fee‐for‐service high‐risk Medicaid beneficiaries. The study is limited to patients with selected chronic conditions and examines the differentiating factors within this high‐risk population. For the purpose of our study, variables were selected based on a priori knowledge and Andersen's behavioral model of health service utilization. This model suggests that potential health service use is determined by interactions among predisposing (demographics, index hospitalization characteristics), enabling (county level [eg, socioeconomic status]), and need (health status) characteristics of individuals and also the healthcare systems in the communities where they reside.[15]

METHODS

Study Population

The study population consisted of nonelderly (2164 years old) fee‐for‐service Medicaid‐only beneficiaries with selected chronic conditions and continuous enrollment during baseline and follow‐up period (Figure 1). Analyses were restricted to those who had at least 1 inpatient admission in 2007 and were conducted at the person‐level.

Figure 1

For the purpose of this study, Medicaid beneficiaries with 19 chronic conditions were selected: asthma, arthritis, cardiac arrhythmias, coronary artery disease, cancer, congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, dementia, diabetes, hypertension, hyperlipidemia, hepatitis, human immunodeficiency virus osteoporosis, stroke, depression, schizophrenia, and substance use disorders. These conditions were identified based on the strategic framework developed and adopted by the Department of Health and Human Services for research, policy, program, and practice.[16]

RESULTS

After the exclusion criteria, there were 15,806 Medicaid beneficiaries with selected chronic conditions and at least 1 inpatient encounter in 2007. Overall, 16.7% experienced all‐cause 30‐day readmissions. A description of the study population and unadjusted associations between independent variables and all‐cause 30‐day readmission are presented in Table 1.

Multilevel logistic regressions of all‐cause 30‐day readmissions are summarized in Table 2. Beneficiaries with longer LOS had significantly higher odds of 30‐day readmission. In addition, presence of cancer, cardiovascular conditions, diabetes, and mental health conditions at index hospitalization significantly increased the odds of readmission. In addition, beneficiaries with cash or medical need eligibility had significantly higher odds of 30‐day readmission.

DISCUSSION

To the best of our knowledge, this is the first study examining patient‐level and county‐level characteristics associated with all‐cause 30‐day readmission in Medicaid beneficiaries with chronic conditions. In addition, our findings add to the nascent literature on readmissions among Medicaid beneficiaries, with findings discussed below.

LOS has been reported as a risk factor for readmission both in elderly and nonelderly populations.[11] Our findings indicate that longer LOS is associated with increased odds of 30‐day readmission, which could be attributed to severity of illness at index hospitalization.[10] This finding could be related to unmeasured clinical severity (our models account for some comorbidities) and socioeconomic issues (as noted in the introduction). This may have implications for discharge planning efforts and focusing on chronic disease management, which has previously shown to be effective in reducing readmissions.[17] Our findings suggest 30‐day readmissions can be predicted using variables that are readily available, few in number, and simple to incorporate in discharge planning. Comprehensive discharge planning which takes into account chronic conditions and index hospitalization characteristics may help organize postdischarge services, including coordination of care with physicians, medication reconciliation, follow‐up care, and appropriate self‐management for chronic conditions.

Our findings of increased risk of 30‐day hospital readmissions as well as longer LOS among Medicaid beneficiaries with cancer, cardiovascular conditions, diabetes, and mental health conditions at index hospitalization suggests that patient complexity/poor health status increases the risk of readmission. A more focused approach in treatment of these diseases can help reduce readmissions. Integrated care management interventions after hospital discharge have been shown to reduce readmissions among those with heart disease; a coordinated care team including cardiologists, specialized nurses, and primary care physicians, and provision of integrated care following hospitalizations have shown benefit.[18, 19] Emerging models of delivery such as accountable care organizations and patient‐centered medical homes, which offer comprehensive, well‐coordinated primary care services, may be needed to reduce readmission among Medicaid beneficiaries with chronic health conditions. In this respect, 3 of the 4 states represented (California, New York, and Texas) are CMS Innovation Model partner states and are presently awardees of Medicaid Incentives for the Prevention of Chronic Disease state grants.[20] It remains to be seen whether such programs can reduce the high prevalence of readmissions in a Medicaid population.

Although our findings may have implications in reducing readmission risk, these results need to be interpreted in the light of study limitations. Our study was based on beneficiaries from only 4 states and cannot be generalized to the entire US Medicaid population. We also excluded individuals who were not enrolled in Medicaid health maintenance organizations. Given that less than one‐third of the population receives fee‐for‐service care in Medicaid, our study may have selection bias. Our study design utilized a retrospective cohort approach and cannot be used to establish causal relationships. Further, our study did not include adjustment for variables related to discharge planning or care coordination other than a primary care visit 14 days post discharge, which might influence the readmission risk of complex patients. Our study utilized data from administrative claims files.

Overall, our analyses revealed that patient complexities increased the risk of all‐cause 30‐day readmission for high‐risk Medicaid beneficiaries with chronic conditions, thus warranting the need for comprehensive care for those with chronic conditions. Programs designed to reduce the risk of 30‐day readmissions may need to focus on appropriate disease management and better coordinated care post hospitalization.

Hospital readmissions that occur within 30 days of discharge are an important measure for assessing performance of the healthcare system and the quality of patient care.[1, 2] According to the Healthcare Cost and Utilization Project (HCUP), there were approximately 3.3 million adults with all‐cause 30‐day readmissions in the United States in 2011, incurring nearly $41.3 billion in hospital costs.[3] Reducing 30‐day readmissions has become a priority for payers, providers, and policymakers seeking to achieve improved quality of care at lower costs.

The implementation of the Affordable Care Act (ACA) provided the Centers for Medicare & Medicaid Services (CMS) statutory authority under the Hospital Readmissions Reduction Program to reduce payments for certain hospital readmissions that it deemed avoidable.[4] Although initial focus was on Medicare readmissions related to heart failure, myocardial infarction, and pneumonia, CMS is now considering expanding the list beyond the 3 conditions covered by the program.[4, 5] Therefore, it is important to understand major risk factors for readmissions in beneficiaries with chronic conditions.

Medicaid consists of the largest number of beneficiaries among all payers in the United States, with approximately 62 million beneficiaries in 2013.[5] The Medicaid population is further expected to increase with the coverage expansions under the ACA. In addition, the state Medicaid programs incur an estimated $374 billion in healthcare expenditures and provide healthcare services to the vulnerable, indigent, and disabled. It is estimated that 61% of adult Medicaid beneficiaries have chronic or disabling conditions that place them at an increased risk of hospitalization.[6] A series of HCUP statistical briefs reported several findings. First, Medicaid all‐cause readmission rates were comparable with Medicare but double the rate of private insurance.[7] Second, for readmissions following nonsurgical hospitalizations, 30‐day Medicaid readmission rates were higher than Medicare and private insurance for both acute and chronic conditions.[1] The effects of such costly utilization patterns, for this large and growing population necessitates heightened attention under healthcare reform.

The balance between hospital efficiency and quality of care is another crucial aspect for our healthcare system. However, length of stay (LOS), a proxy marker for efficiency, may conflict with hospital readmission rates, an indicator of quality. Further, CMS plans to bundle 30‐day readmission rates to reimbursement for the index hospitalization.[8]

The effect of LOS on readmission rates is complex, and previous studies have provided conflicting data regarding the relationship between LOS and subsequent readmission risk. Some indicate that shorter LOS is associated with a higher risk of readmission,[8, 9] whereas others suggest that extended LOS is associated with a higher risk of readmission.[10, 11, 12] However, most research on readmissions has focused on Medicare beneficiaries.[11, 13, 14] The readmission patterns of Medicaid beneficiaries differ from those of the geriatric Medicare beneficiaries, from a clinical and socioeconomic perspective. Considering the importance of 30‐day readmission for payers and policy makers, there is a need to understand the role of LOS and implications for treatment and management strategies.

Our study examined the association between index hospitalization characteristics (LOS and reason for admission) and all‐cause 30‐day readmission risk in fee‐for‐service high‐risk Medicaid beneficiaries. The study is limited to patients with selected chronic conditions and examines the differentiating factors within this high‐risk population. For the purpose of our study, variables were selected based on a priori knowledge and Andersen's behavioral model of health service utilization. This model suggests that potential health service use is determined by interactions among predisposing (demographics, index hospitalization characteristics), enabling (county level [eg, socioeconomic status]), and need (health status) characteristics of individuals and also the healthcare systems in the communities where they reside.[15]

METHODS

Study Population

The study population consisted of nonelderly (2164 years old) fee‐for‐service Medicaid‐only beneficiaries with selected chronic conditions and continuous enrollment during baseline and follow‐up period (Figure 1). Analyses were restricted to those who had at least 1 inpatient admission in 2007 and were conducted at the person‐level.

Figure 1

For the purpose of this study, Medicaid beneficiaries with 19 chronic conditions were selected: asthma, arthritis, cardiac arrhythmias, coronary artery disease, cancer, congestive heart failure, chronic kidney disease, chronic obstructive pulmonary disease, dementia, diabetes, hypertension, hyperlipidemia, hepatitis, human immunodeficiency virus osteoporosis, stroke, depression, schizophrenia, and substance use disorders. These conditions were identified based on the strategic framework developed and adopted by the Department of Health and Human Services for research, policy, program, and practice.[16]

RESULTS

After the exclusion criteria, there were 15,806 Medicaid beneficiaries with selected chronic conditions and at least 1 inpatient encounter in 2007. Overall, 16.7% experienced all‐cause 30‐day readmissions. A description of the study population and unadjusted associations between independent variables and all‐cause 30‐day readmission are presented in Table 1.

Multilevel logistic regressions of all‐cause 30‐day readmissions are summarized in Table 2. Beneficiaries with longer LOS had significantly higher odds of 30‐day readmission. In addition, presence of cancer, cardiovascular conditions, diabetes, and mental health conditions at index hospitalization significantly increased the odds of readmission. In addition, beneficiaries with cash or medical need eligibility had significantly higher odds of 30‐day readmission.

DISCUSSION

To the best of our knowledge, this is the first study examining patient‐level and county‐level characteristics associated with all‐cause 30‐day readmission in Medicaid beneficiaries with chronic conditions. In addition, our findings add to the nascent literature on readmissions among Medicaid beneficiaries, with findings discussed below.

LOS has been reported as a risk factor for readmission both in elderly and nonelderly populations.[11] Our findings indicate that longer LOS is associated with increased odds of 30‐day readmission, which could be attributed to severity of illness at index hospitalization.[10] This finding could be related to unmeasured clinical severity (our models account for some comorbidities) and socioeconomic issues (as noted in the introduction). This may have implications for discharge planning efforts and focusing on chronic disease management, which has previously shown to be effective in reducing readmissions.[17] Our findings suggest 30‐day readmissions can be predicted using variables that are readily available, few in number, and simple to incorporate in discharge planning. Comprehensive discharge planning which takes into account chronic conditions and index hospitalization characteristics may help organize postdischarge services, including coordination of care with physicians, medication reconciliation, follow‐up care, and appropriate self‐management for chronic conditions.

Our findings of increased risk of 30‐day hospital readmissions as well as longer LOS among Medicaid beneficiaries with cancer, cardiovascular conditions, diabetes, and mental health conditions at index hospitalization suggests that patient complexity/poor health status increases the risk of readmission. A more focused approach in treatment of these diseases can help reduce readmissions. Integrated care management interventions after hospital discharge have been shown to reduce readmissions among those with heart disease; a coordinated care team including cardiologists, specialized nurses, and primary care physicians, and provision of integrated care following hospitalizations have shown benefit.[18, 19] Emerging models of delivery such as accountable care organizations and patient‐centered medical homes, which offer comprehensive, well‐coordinated primary care services, may be needed to reduce readmission among Medicaid beneficiaries with chronic health conditions. In this respect, 3 of the 4 states represented (California, New York, and Texas) are CMS Innovation Model partner states and are presently awardees of Medicaid Incentives for the Prevention of Chronic Disease state grants.[20] It remains to be seen whether such programs can reduce the high prevalence of readmissions in a Medicaid population.

Although our findings may have implications in reducing readmission risk, these results need to be interpreted in the light of study limitations. Our study was based on beneficiaries from only 4 states and cannot be generalized to the entire US Medicaid population. We also excluded individuals who were not enrolled in Medicaid health maintenance organizations. Given that less than one‐third of the population receives fee‐for‐service care in Medicaid, our study may have selection bias. Our study design utilized a retrospective cohort approach and cannot be used to establish causal relationships. Further, our study did not include adjustment for variables related to discharge planning or care coordination other than a primary care visit 14 days post discharge, which might influence the readmission risk of complex patients. Our study utilized data from administrative claims files.

Overall, our analyses revealed that patient complexities increased the risk of all‐cause 30‐day readmission for high‐risk Medicaid beneficiaries with chronic conditions, thus warranting the need for comprehensive care for those with chronic conditions. Programs designed to reduce the risk of 30‐day readmissions may need to focus on appropriate disease management and better coordinated care post hospitalization.

Payers, providers, and policymakers are testing several major approaches to reducing US healthcare spending without harming quality. One strategy is to bundle payments for a longitudinal episode of care, as in Medicare's popular Bundled Payments for Care Improvement initiative.[1] A second approach is to decrease rates of inappropriate care, through programs such as Choosing Wisely, that discourage use of low‐value services.[2] Finally, a third approach adopted by the Medicare Shared Savings Program strives to reduce both episode costs and rates of inappropriate care, by incorporating annual per capita Medicare spending into performance benchmarks.[3] Given these ongoing efforts, it would be important to compare the potential impact of reducing episode payments versus rates of care on total costs of care.

METHODS

For 3 common surgical procedures, we compared the relative influence of procedure rates versus episode payments (among those with procedures) on total Medicare expenditures.

We used complete Part A and B Medicare claims data for: coronary artery bypass grafting (CABG), prostatectomy, and hip replacement. We used International Classification of Diseases, Ninth Revision codes to identify the procedures (CABG: 361.0, 361.1, 361.2, 361.3, 361.4, 361.5, 361.6, 361.7, 361.9, 36.2; prostatectomy: 60.4, 60.5, 60.62 with a prostate cancer diagnosis code of 185 or 233.4; and hip replacement: 81.51, 81.52 excluding hip fracture codes 820.0, 820.1, 820.2, 820.3, 820.8, 820.9).

For each procedure, we estimated age‐ and sex‐adjusted episode rates for each hospital referral region (HRR). The numerator was the number of admissions to an acute care hospital for CABG (total n = 118,185), prostatectomy (total n = 18,328), or hip replacement (total n = 178,982) from January 2009 to June 2010. The denominator was fee‐for‐service Medicare beneficiaries age 65 years or older. We excluded those without continuous Part A and B enrollment (total denominator n = 23,403,051). Females were also excluded from the prostatectomy cohort.

For each of the 306 HRRs, we next calculated average HRR‐level episode payments. Using CABG as an example, we aggregated up the risk‐adjusted (age, sex, race, admission type, Elixhauser[4] comorbidities), price‐standardized[5] episode payments for all CABG patients residing in an HRR, and divided this by the number of CABG patients living in that HRR.

Finally, we obtained baseline per capita spending by multiplying the age‐ and sex‐adjusted CABG episode rate by the average CABG episode payment in that HRR. All payments were standardized to 2010 dollars using the Consumer Price Index.

We simulated changes in per capita Medicare spending for CABG across all HRRs under 2 scenarios: (1) reducing HRR‐level rates to the median versus (2) reducing HRR‐level episode payments to the median. We repeated this for prostatectomy and hip replacement.

RESULTS

Age‐ and sex‐adjusted rates of CABG varied more than risk‐adjusted, price‐standardized episode payments (90th:10th percentile of 2.0 for rates vs 1.2 for payments) (see Supporting Information, Appendix, in the online version of this article). Reducing rates of CABG to the 50th percentile decreased per capita episode payments by 11.1%. In contrast, reducing CABG episode payments to the 50th percentile decreased per capita episode payments by 3.6%. The absolute difference between the 2 simulations was 7.5% (95% confidence interval [CI]: 5.6%‐9.4%) (Figure 1). Results were similar for prostatectomy and hip replacement. In sensitivity analyses, reducing hospital‐level episode payments (rather than HRR‐level episode payments) produced similar findings. Employing the 90th percentile as a cutoff (instead of the median) also produced qualitatively similar results.

Figure 1

DISCUSSION

For 3 common surgical procedures, reducing procedure rates lowers total Medicare spending substantially more than reducing episode payments. These findings are attributable to a much greater variation in procedure rates compared to episode‐based payments. Prior research has documented wide variation in rates of surgical procedures.[6] This may be due to a number of factors, including physician beliefs about indications for surgery, as well as the degree to which patient preferences are incorporated into decision making.[6]

Our findings suggest that it would be important to incorporate population‐based episode rates into efforts aimed at incentivizing higher value care. Incentives tied to population‐based episode rates are difficult to design well. They may need to be paired with appropriateness criteria to avoid stinting on care. Attribution of a population to a hospital (including those who are not admitted to a hospital) is also complex.[7] Finally, hospitals are not solely responsible for rates of care, because the decision to admit a patient is sometimes made in the emergency department (eg, for chronic medical conditions), but at other times is made in the outpatient arena (eg, for elective surgery). Nevertheless, a narrow focus on per episode spending limits the potential impact of efforts to control Medicare spending.

Payers, providers, and policymakers are testing several major approaches to reducing US healthcare spending without harming quality. One strategy is to bundle payments for a longitudinal episode of care, as in Medicare's popular Bundled Payments for Care Improvement initiative.[1] A second approach is to decrease rates of inappropriate care, through programs such as Choosing Wisely, that discourage use of low‐value services.[2] Finally, a third approach adopted by the Medicare Shared Savings Program strives to reduce both episode costs and rates of inappropriate care, by incorporating annual per capita Medicare spending into performance benchmarks.[3] Given these ongoing efforts, it would be important to compare the potential impact of reducing episode payments versus rates of care on total costs of care.

METHODS

For 3 common surgical procedures, we compared the relative influence of procedure rates versus episode payments (among those with procedures) on total Medicare expenditures.

We used complete Part A and B Medicare claims data for: coronary artery bypass grafting (CABG), prostatectomy, and hip replacement. We used International Classification of Diseases, Ninth Revision codes to identify the procedures (CABG: 361.0, 361.1, 361.2, 361.3, 361.4, 361.5, 361.6, 361.7, 361.9, 36.2; prostatectomy: 60.4, 60.5, 60.62 with a prostate cancer diagnosis code of 185 or 233.4; and hip replacement: 81.51, 81.52 excluding hip fracture codes 820.0, 820.1, 820.2, 820.3, 820.8, 820.9).

For each procedure, we estimated age‐ and sex‐adjusted episode rates for each hospital referral region (HRR). The numerator was the number of admissions to an acute care hospital for CABG (total n = 118,185), prostatectomy (total n = 18,328), or hip replacement (total n = 178,982) from January 2009 to June 2010. The denominator was fee‐for‐service Medicare beneficiaries age 65 years or older. We excluded those without continuous Part A and B enrollment (total denominator n = 23,403,051). Females were also excluded from the prostatectomy cohort.

For each of the 306 HRRs, we next calculated average HRR‐level episode payments. Using CABG as an example, we aggregated up the risk‐adjusted (age, sex, race, admission type, Elixhauser[4] comorbidities), price‐standardized[5] episode payments for all CABG patients residing in an HRR, and divided this by the number of CABG patients living in that HRR.

Finally, we obtained baseline per capita spending by multiplying the age‐ and sex‐adjusted CABG episode rate by the average CABG episode payment in that HRR. All payments were standardized to 2010 dollars using the Consumer Price Index.

We simulated changes in per capita Medicare spending for CABG across all HRRs under 2 scenarios: (1) reducing HRR‐level rates to the median versus (2) reducing HRR‐level episode payments to the median. We repeated this for prostatectomy and hip replacement.

RESULTS

Age‐ and sex‐adjusted rates of CABG varied more than risk‐adjusted, price‐standardized episode payments (90th:10th percentile of 2.0 for rates vs 1.2 for payments) (see Supporting Information, Appendix, in the online version of this article). Reducing rates of CABG to the 50th percentile decreased per capita episode payments by 11.1%. In contrast, reducing CABG episode payments to the 50th percentile decreased per capita episode payments by 3.6%. The absolute difference between the 2 simulations was 7.5% (95% confidence interval [CI]: 5.6%‐9.4%) (Figure 1). Results were similar for prostatectomy and hip replacement. In sensitivity analyses, reducing hospital‐level episode payments (rather than HRR‐level episode payments) produced similar findings. Employing the 90th percentile as a cutoff (instead of the median) also produced qualitatively similar results.

Figure 1

DISCUSSION

For 3 common surgical procedures, reducing procedure rates lowers total Medicare spending substantially more than reducing episode payments. These findings are attributable to a much greater variation in procedure rates compared to episode‐based payments. Prior research has documented wide variation in rates of surgical procedures.[6] This may be due to a number of factors, including physician beliefs about indications for surgery, as well as the degree to which patient preferences are incorporated into decision making.[6]

Our findings suggest that it would be important to incorporate population‐based episode rates into efforts aimed at incentivizing higher value care. Incentives tied to population‐based episode rates are difficult to design well. They may need to be paired with appropriateness criteria to avoid stinting on care. Attribution of a population to a hospital (including those who are not admitted to a hospital) is also complex.[7] Finally, hospitals are not solely responsible for rates of care, because the decision to admit a patient is sometimes made in the emergency department (eg, for chronic medical conditions), but at other times is made in the outpatient arena (eg, for elective surgery). Nevertheless, a narrow focus on per episode spending limits the potential impact of efforts to control Medicare spending.

Payers, providers, and policymakers are testing several major approaches to reducing US healthcare spending without harming quality. One strategy is to bundle payments for a longitudinal episode of care, as in Medicare's popular Bundled Payments for Care Improvement initiative.[1] A second approach is to decrease rates of inappropriate care, through programs such as Choosing Wisely, that discourage use of low‐value services.[2] Finally, a third approach adopted by the Medicare Shared Savings Program strives to reduce both episode costs and rates of inappropriate care, by incorporating annual per capita Medicare spending into performance benchmarks.[3] Given these ongoing efforts, it would be important to compare the potential impact of reducing episode payments versus rates of care on total costs of care.

METHODS

For 3 common surgical procedures, we compared the relative influence of procedure rates versus episode payments (among those with procedures) on total Medicare expenditures.

We used complete Part A and B Medicare claims data for: coronary artery bypass grafting (CABG), prostatectomy, and hip replacement. We used International Classification of Diseases, Ninth Revision codes to identify the procedures (CABG: 361.0, 361.1, 361.2, 361.3, 361.4, 361.5, 361.6, 361.7, 361.9, 36.2; prostatectomy: 60.4, 60.5, 60.62 with a prostate cancer diagnosis code of 185 or 233.4; and hip replacement: 81.51, 81.52 excluding hip fracture codes 820.0, 820.1, 820.2, 820.3, 820.8, 820.9).

For each procedure, we estimated age‐ and sex‐adjusted episode rates for each hospital referral region (HRR). The numerator was the number of admissions to an acute care hospital for CABG (total n = 118,185), prostatectomy (total n = 18,328), or hip replacement (total n = 178,982) from January 2009 to June 2010. The denominator was fee‐for‐service Medicare beneficiaries age 65 years or older. We excluded those without continuous Part A and B enrollment (total denominator n = 23,403,051). Females were also excluded from the prostatectomy cohort.

For each of the 306 HRRs, we next calculated average HRR‐level episode payments. Using CABG as an example, we aggregated up the risk‐adjusted (age, sex, race, admission type, Elixhauser[4] comorbidities), price‐standardized[5] episode payments for all CABG patients residing in an HRR, and divided this by the number of CABG patients living in that HRR.

Finally, we obtained baseline per capita spending by multiplying the age‐ and sex‐adjusted CABG episode rate by the average CABG episode payment in that HRR. All payments were standardized to 2010 dollars using the Consumer Price Index.

We simulated changes in per capita Medicare spending for CABG across all HRRs under 2 scenarios: (1) reducing HRR‐level rates to the median versus (2) reducing HRR‐level episode payments to the median. We repeated this for prostatectomy and hip replacement.

RESULTS

Age‐ and sex‐adjusted rates of CABG varied more than risk‐adjusted, price‐standardized episode payments (90th:10th percentile of 2.0 for rates vs 1.2 for payments) (see Supporting Information, Appendix, in the online version of this article). Reducing rates of CABG to the 50th percentile decreased per capita episode payments by 11.1%. In contrast, reducing CABG episode payments to the 50th percentile decreased per capita episode payments by 3.6%. The absolute difference between the 2 simulations was 7.5% (95% confidence interval [CI]: 5.6%‐9.4%) (Figure 1). Results were similar for prostatectomy and hip replacement. In sensitivity analyses, reducing hospital‐level episode payments (rather than HRR‐level episode payments) produced similar findings. Employing the 90th percentile as a cutoff (instead of the median) also produced qualitatively similar results.

Figure 1

DISCUSSION

For 3 common surgical procedures, reducing procedure rates lowers total Medicare spending substantially more than reducing episode payments. These findings are attributable to a much greater variation in procedure rates compared to episode‐based payments. Prior research has documented wide variation in rates of surgical procedures.[6] This may be due to a number of factors, including physician beliefs about indications for surgery, as well as the degree to which patient preferences are incorporated into decision making.[6]

Our findings suggest that it would be important to incorporate population‐based episode rates into efforts aimed at incentivizing higher value care. Incentives tied to population‐based episode rates are difficult to design well. They may need to be paired with appropriateness criteria to avoid stinting on care. Attribution of a population to a hospital (including those who are not admitted to a hospital) is also complex.[7] Finally, hospitals are not solely responsible for rates of care, because the decision to admit a patient is sometimes made in the emergency department (eg, for chronic medical conditions), but at other times is made in the outpatient arena (eg, for elective surgery). Nevertheless, a narrow focus on per episode spending limits the potential impact of efforts to control Medicare spending.