High Users of Hospital Care

Despite signs of a slowing trend,[1, 2, 3] US healthcare costs continue to rise, and cost containment remains a major area of concern. Hospital costs are the largest single category of national healthcare expenditures,[4] and the burden of cost containment is increasingly being shifted to hospitals.[5] As such, hospitals are increasingly focusing on implementing interventions to reduce rates of hospitalizations and readmissions as a mechanism to reduce overall healthcare costs.[5, 6, 7, 8, 9]

Multiple factors potentially contribute to patients being high cost, including acute care utilization,[10, 11, 12] pharmaceuticals,[13, 14] procedures,[15] catastrophic illness,[16] and high‐risk chronic conditions.[11, 17, 18] However, many hospitals are implementing interventions focused on a single subset of these high cost patientshigh users of inpatient services. Care management interventions have received particular attention, due to their perceived potential to improve quality of care while reducing costs through the mechanism of reducing hospital admissions.[19, 20, 21] Despite their increasing prevalence, there is limited evidence demonstrating the effectiveness of these programs. Among interventions targeting high cost individuals, the Medicare Care Management for High Cost Beneficiaries showed no effect on hospital admissions.[19] Another high‐profile intervention, the Citywide Care Management System led by the Camden Coalition, showed promising preliminary results, but data from a systematic evaluation are lacking.[22] Conversely, interventions targeting individuals with frequent hospitalizations have similarly shown mixed results in reducing costs.[6, 7, 9, 23]

Taken together, these data suggest that the relationship between high costs and frequent hospital use is complicated, and the population of individuals with frequent hospitalizations may not represent the entire population of high cost individuals. Thus, focusing on reduction of hospitalizations alone may be inadequate to reduce costs. For these reasons, we sought to determine how many high cost individuals would be captured by focusing only on frequently hospitalized (high admit) individuals by examining the overlap between these populations. We also sought to describe the characteristics and distinctions between the resulting subgroups of high users to better inform the design of future interventions.

METHODS

We examined the cross‐sectional relationship between high cost and high admit populations among adult patients 18 years of age hospitalized at the University of California, San Francisco (UCSF) Medical Center, a 660‐bed urban academic medical center from July 1, 2010 to June 30, 2011.

This study was conducted as part of a quality improvement project to identify high user primary care patients for complex care management. Individuals were included in the study if: (1) they had an assigned UCSF primary care provider (PCP), and (2) they had at least 1 hospitalization at UCSF during the study period. PCP assignments were ascertained from panel lists maintained by clinics; lists included individuals with at least 1 visit at any of the 8 primary care clinics at UCSF in the 2 years prior to the end of the study period. Because individuals are dropped from PCP panels at death, we were unable to ascertain or include individuals who died during the study period.

From the initial study population, we defined the high cost group as those who were in the top decile of total hospital costs, and the high admit group as those who were in the top decile of total hospitalizations during the study period. We elected to use the top decile as a cutoff given that it is a common operational definition used to identify high users to target for intervention.[24]

To examine the relationship between high cost and high admits we defined 3 further subgroups: high costlow admits, high costhigh admits, and low costhigh admits (Figure 1). To explore the face validity of these descriptors and classification scheme, we subsequently examined the proportion of total hospital costs and total hospitalizations each subgroup accounted for with respect to the study population.

Figure 1

RESULTS

Of the 2566 unique individuals included in the analysis (Figure 1), 256 individuals were identified as high cost (top decile, $65,000). This group accounted for 45% of all costs and 22% of all hospitalizations (Figure 2). Two hundred fifty individuals were identified as high admits (top decile, 3 hospitalizations). This group accounted for 32% of all costs and 28% of all hospitalizations.

Figure 2

Only 48% of high cost individuals were also high admit ($65,000 and 3 hospitalizations; n=125, Figure 1). Among high users, we subsequently defined 3 subgroups based on the relationship between cost and hospitalizations (high costlow admits, high costhigh admits, and low costhigh admits). Each subgroup accounted for approximately 5% of the overall study population (Figure 2). The high costlow admits subgroup incurred a proportionate share of hospitalizations (6%) but a disproportionate share of costs (20%). The high costhigh admits subgroup had a disproportionate share of both costs (25%) and hospitalizations (16%). The low costhigh admits subgroup had a proportionate share of costs (7%) but a disproportionate share of hospitalizations (12%).

DISCUSSION

In this study, we found that only half of high cost individuals were also high admit. Further categorizing high users into high costlow admit versus high costhigh admit versus low costhigh admit identified distinct patterns between each group. High costhigh admit individuals were more likely to be hospitalized for medical conditions, whereas high costlow admit individuals were more likely to be hospitalized for surgical conditions. CMS core conditions accounted for a low proportion of overall hospitalizations across all groups.

Our findings suggest several distinct types of high users with different clinical characteristics, utilization, and cost patterns. From a hospital perspective, one implication is that a multifaceted approach to cost containment, rather than the one‐size‐fits‐all strategy of reducing hospitalizations, may be more effective in reducing costs. For example, our findings show that high costlow admit individuals have a disproportionate number of hospitalizations for surgical conditions, longer LOS, and more ICU days. Costs incurred by this subgroup may be more responsive to in‐hospital interventions aimed at reducing procedural costs, LOS, unnecessary use of the ICU, and minimizing postoperative infections and complications rather than to a care management approach.

In contrast, care management strategies such as improving postdischarge care and chronic disease management, which aim to achieve cost savings through reducing hospitalizations, may be more effective in reducing costs among high costhigh admit individuals, who have a high proportion of hospitalizations for medical conditions and the highest proportion of 30‐day readmissions. Such strategies may also be important in optimizing the quality of care for low costhigh admit individuals, who have the highest proportion of medical hospitalizations among all high users, though the potential for cost savings may be more limited in this subgroup.

Our results suggest that current hospital‐based approachesdriven by readmissions penalties for CMS core conditionsmay have less than the expected impact on costs. For example, although high costhigh admit individuals had the highest proportion of 30‐day readmissions, readmissions specifically for CMS core conditions accounted for <1% of costs in this subgroup. Thus, the potential return on an expensive investment in a care management intervention is unclear, given the small number of readmissions for these select conditions. From a broader perspective, the focus on readmissions for CMS core conditions, which overall contribute relatively little to high hospital costs, may not be a comprehensive enough strategy for cost containment. To date, there have been limited policies targeting factors contributing to high hospital costs outside of frequent medical hospitalizations. Medicare's nonpayment policy for treatment of preventable hospital conditions, including surgical site infections, translates prevention of these conditions into cost savings for hospitals.[30] However, this rule has been criticized for not going far enough to drive substantial savings.[31] A new CMS rule authorizes states to identify other provider‐preventable conditions for which Medicaid payment will be prohibited.[32] Future policy efforts should further emphasize a comprehensive, multipronged approach beyond readmissions penalties for select conditions if healthcare cost containment remains a policy priority.

Our results should be interpreted in light of several limitations. First, this was a single‐site study at an academic medical center; the generalizability of our results to other settings is unclear. Our cost data likely reflect local market factors, including the highest wage rates for skilled healthcare labor in the United States.[33] Although the explicit distribution of high user subgroups may be institution‐specific due to variations in our cost structure, we anticipate that the general classification paradigm will be similar in other health systems. Second, we captured utilization and costs only at a single hospital. However, our study population includes only individuals with PCPs at UCSF, and internal data from both Medicare and UCSF's largest private payer show that over 85% of hospitalizations among this population are to UCSF Medical Center. Third, we were able to capture only hospital costs rather than overall healthcare costs. Given that hospital costs account for the single largest category of total national health costs,[4] we expect that future studies examining total health costs will show similar findings. Fourth, our data did not include measures of health status, socioeconomic status, housing, or mental health comorbidities to permit an analysis of these factors, which have been previously related to frequent hospitalizations and high costs.[34, 35, 36, 37, 38, 39] Fifth, due to resource constraints, we were unable to conduct a longitudinal analysis to examine the extent to which individuals are consistently high users over time. Previous studies have described that 20% to 30% of individuals are consistently high users; the remainder have discrete periods of high utilization.[34, 40] This may be an important consideration in the design of future interventions.

Finally, our analysis was limited to individuals with a PCP to allow identification of an accessible cohort for care management. Thus, we did not capture individuals without a PCP and individuals who died during the study period, because these individuals no longer had an assigned PCP following death. Although this approach is consistent with that of many care management programs,[19] these populations are likely to incur higher than average utilization and healthcare costs, and represent important areas for future investigation.

In summary, our study identifies three types of high‐user populations that differ in the proportion of costs attributable to frequent hospitalizations, clinical conditions associated with hospital use, and frequency of 30‐day readmissions. Stratifying high users by both costs and hospitalizations may help identify tailored strategies to more effectively reduce costs and utilization.

Despite signs of a slowing trend,[1, 2, 3] US healthcare costs continue to rise, and cost containment remains a major area of concern. Hospital costs are the largest single category of national healthcare expenditures,[4] and the burden of cost containment is increasingly being shifted to hospitals.[5] As such, hospitals are increasingly focusing on implementing interventions to reduce rates of hospitalizations and readmissions as a mechanism to reduce overall healthcare costs.[5, 6, 7, 8, 9]

Multiple factors potentially contribute to patients being high cost, including acute care utilization,[10, 11, 12] pharmaceuticals,[13, 14] procedures,[15] catastrophic illness,[16] and high‐risk chronic conditions.[11, 17, 18] However, many hospitals are implementing interventions focused on a single subset of these high cost patientshigh users of inpatient services. Care management interventions have received particular attention, due to their perceived potential to improve quality of care while reducing costs through the mechanism of reducing hospital admissions.[19, 20, 21] Despite their increasing prevalence, there is limited evidence demonstrating the effectiveness of these programs. Among interventions targeting high cost individuals, the Medicare Care Management for High Cost Beneficiaries showed no effect on hospital admissions.[19] Another high‐profile intervention, the Citywide Care Management System led by the Camden Coalition, showed promising preliminary results, but data from a systematic evaluation are lacking.[22] Conversely, interventions targeting individuals with frequent hospitalizations have similarly shown mixed results in reducing costs.[6, 7, 9, 23]

Taken together, these data suggest that the relationship between high costs and frequent hospital use is complicated, and the population of individuals with frequent hospitalizations may not represent the entire population of high cost individuals. Thus, focusing on reduction of hospitalizations alone may be inadequate to reduce costs. For these reasons, we sought to determine how many high cost individuals would be captured by focusing only on frequently hospitalized (high admit) individuals by examining the overlap between these populations. We also sought to describe the characteristics and distinctions between the resulting subgroups of high users to better inform the design of future interventions.

METHODS

We examined the cross‐sectional relationship between high cost and high admit populations among adult patients 18 years of age hospitalized at the University of California, San Francisco (UCSF) Medical Center, a 660‐bed urban academic medical center from July 1, 2010 to June 30, 2011.

This study was conducted as part of a quality improvement project to identify high user primary care patients for complex care management. Individuals were included in the study if: (1) they had an assigned UCSF primary care provider (PCP), and (2) they had at least 1 hospitalization at UCSF during the study period. PCP assignments were ascertained from panel lists maintained by clinics; lists included individuals with at least 1 visit at any of the 8 primary care clinics at UCSF in the 2 years prior to the end of the study period. Because individuals are dropped from PCP panels at death, we were unable to ascertain or include individuals who died during the study period.

From the initial study population, we defined the high cost group as those who were in the top decile of total hospital costs, and the high admit group as those who were in the top decile of total hospitalizations during the study period. We elected to use the top decile as a cutoff given that it is a common operational definition used to identify high users to target for intervention.[24]

To examine the relationship between high cost and high admits we defined 3 further subgroups: high costlow admits, high costhigh admits, and low costhigh admits (Figure 1). To explore the face validity of these descriptors and classification scheme, we subsequently examined the proportion of total hospital costs and total hospitalizations each subgroup accounted for with respect to the study population.

Figure 1

RESULTS

Of the 2566 unique individuals included in the analysis (Figure 1), 256 individuals were identified as high cost (top decile, $65,000). This group accounted for 45% of all costs and 22% of all hospitalizations (Figure 2). Two hundred fifty individuals were identified as high admits (top decile, 3 hospitalizations). This group accounted for 32% of all costs and 28% of all hospitalizations.

Figure 2

Only 48% of high cost individuals were also high admit ($65,000 and 3 hospitalizations; n=125, Figure 1). Among high users, we subsequently defined 3 subgroups based on the relationship between cost and hospitalizations (high costlow admits, high costhigh admits, and low costhigh admits). Each subgroup accounted for approximately 5% of the overall study population (Figure 2). The high costlow admits subgroup incurred a proportionate share of hospitalizations (6%) but a disproportionate share of costs (20%). The high costhigh admits subgroup had a disproportionate share of both costs (25%) and hospitalizations (16%). The low costhigh admits subgroup had a proportionate share of costs (7%) but a disproportionate share of hospitalizations (12%).

DISCUSSION

In this study, we found that only half of high cost individuals were also high admit. Further categorizing high users into high costlow admit versus high costhigh admit versus low costhigh admit identified distinct patterns between each group. High costhigh admit individuals were more likely to be hospitalized for medical conditions, whereas high costlow admit individuals were more likely to be hospitalized for surgical conditions. CMS core conditions accounted for a low proportion of overall hospitalizations across all groups.

Our findings suggest several distinct types of high users with different clinical characteristics, utilization, and cost patterns. From a hospital perspective, one implication is that a multifaceted approach to cost containment, rather than the one‐size‐fits‐all strategy of reducing hospitalizations, may be more effective in reducing costs. For example, our findings show that high costlow admit individuals have a disproportionate number of hospitalizations for surgical conditions, longer LOS, and more ICU days. Costs incurred by this subgroup may be more responsive to in‐hospital interventions aimed at reducing procedural costs, LOS, unnecessary use of the ICU, and minimizing postoperative infections and complications rather than to a care management approach.

In contrast, care management strategies such as improving postdischarge care and chronic disease management, which aim to achieve cost savings through reducing hospitalizations, may be more effective in reducing costs among high costhigh admit individuals, who have a high proportion of hospitalizations for medical conditions and the highest proportion of 30‐day readmissions. Such strategies may also be important in optimizing the quality of care for low costhigh admit individuals, who have the highest proportion of medical hospitalizations among all high users, though the potential for cost savings may be more limited in this subgroup.

Our results suggest that current hospital‐based approachesdriven by readmissions penalties for CMS core conditionsmay have less than the expected impact on costs. For example, although high costhigh admit individuals had the highest proportion of 30‐day readmissions, readmissions specifically for CMS core conditions accounted for <1% of costs in this subgroup. Thus, the potential return on an expensive investment in a care management intervention is unclear, given the small number of readmissions for these select conditions. From a broader perspective, the focus on readmissions for CMS core conditions, which overall contribute relatively little to high hospital costs, may not be a comprehensive enough strategy for cost containment. To date, there have been limited policies targeting factors contributing to high hospital costs outside of frequent medical hospitalizations. Medicare's nonpayment policy for treatment of preventable hospital conditions, including surgical site infections, translates prevention of these conditions into cost savings for hospitals.[30] However, this rule has been criticized for not going far enough to drive substantial savings.[31] A new CMS rule authorizes states to identify other provider‐preventable conditions for which Medicaid payment will be prohibited.[32] Future policy efforts should further emphasize a comprehensive, multipronged approach beyond readmissions penalties for select conditions if healthcare cost containment remains a policy priority.

Our results should be interpreted in light of several limitations. First, this was a single‐site study at an academic medical center; the generalizability of our results to other settings is unclear. Our cost data likely reflect local market factors, including the highest wage rates for skilled healthcare labor in the United States.[33] Although the explicit distribution of high user subgroups may be institution‐specific due to variations in our cost structure, we anticipate that the general classification paradigm will be similar in other health systems. Second, we captured utilization and costs only at a single hospital. However, our study population includes only individuals with PCPs at UCSF, and internal data from both Medicare and UCSF's largest private payer show that over 85% of hospitalizations among this population are to UCSF Medical Center. Third, we were able to capture only hospital costs rather than overall healthcare costs. Given that hospital costs account for the single largest category of total national health costs,[4] we expect that future studies examining total health costs will show similar findings. Fourth, our data did not include measures of health status, socioeconomic status, housing, or mental health comorbidities to permit an analysis of these factors, which have been previously related to frequent hospitalizations and high costs.[34, 35, 36, 37, 38, 39] Fifth, due to resource constraints, we were unable to conduct a longitudinal analysis to examine the extent to which individuals are consistently high users over time. Previous studies have described that 20% to 30% of individuals are consistently high users; the remainder have discrete periods of high utilization.[34, 40] This may be an important consideration in the design of future interventions.

Finally, our analysis was limited to individuals with a PCP to allow identification of an accessible cohort for care management. Thus, we did not capture individuals without a PCP and individuals who died during the study period, because these individuals no longer had an assigned PCP following death. Although this approach is consistent with that of many care management programs,[19] these populations are likely to incur higher than average utilization and healthcare costs, and represent important areas for future investigation.

In summary, our study identifies three types of high‐user populations that differ in the proportion of costs attributable to frequent hospitalizations, clinical conditions associated with hospital use, and frequency of 30‐day readmissions. Stratifying high users by both costs and hospitalizations may help identify tailored strategies to more effectively reduce costs and utilization.

Despite signs of a slowing trend,[1, 2, 3] US healthcare costs continue to rise, and cost containment remains a major area of concern. Hospital costs are the largest single category of national healthcare expenditures,[4] and the burden of cost containment is increasingly being shifted to hospitals.[5] As such, hospitals are increasingly focusing on implementing interventions to reduce rates of hospitalizations and readmissions as a mechanism to reduce overall healthcare costs.[5, 6, 7, 8, 9]

Multiple factors potentially contribute to patients being high cost, including acute care utilization,[10, 11, 12] pharmaceuticals,[13, 14] procedures,[15] catastrophic illness,[16] and high‐risk chronic conditions.[11, 17, 18] However, many hospitals are implementing interventions focused on a single subset of these high cost patientshigh users of inpatient services. Care management interventions have received particular attention, due to their perceived potential to improve quality of care while reducing costs through the mechanism of reducing hospital admissions.[19, 20, 21] Despite their increasing prevalence, there is limited evidence demonstrating the effectiveness of these programs. Among interventions targeting high cost individuals, the Medicare Care Management for High Cost Beneficiaries showed no effect on hospital admissions.[19] Another high‐profile intervention, the Citywide Care Management System led by the Camden Coalition, showed promising preliminary results, but data from a systematic evaluation are lacking.[22] Conversely, interventions targeting individuals with frequent hospitalizations have similarly shown mixed results in reducing costs.[6, 7, 9, 23]

Taken together, these data suggest that the relationship between high costs and frequent hospital use is complicated, and the population of individuals with frequent hospitalizations may not represent the entire population of high cost individuals. Thus, focusing on reduction of hospitalizations alone may be inadequate to reduce costs. For these reasons, we sought to determine how many high cost individuals would be captured by focusing only on frequently hospitalized (high admit) individuals by examining the overlap between these populations. We also sought to describe the characteristics and distinctions between the resulting subgroups of high users to better inform the design of future interventions.

METHODS

We examined the cross‐sectional relationship between high cost and high admit populations among adult patients 18 years of age hospitalized at the University of California, San Francisco (UCSF) Medical Center, a 660‐bed urban academic medical center from July 1, 2010 to June 30, 2011.

This study was conducted as part of a quality improvement project to identify high user primary care patients for complex care management. Individuals were included in the study if: (1) they had an assigned UCSF primary care provider (PCP), and (2) they had at least 1 hospitalization at UCSF during the study period. PCP assignments were ascertained from panel lists maintained by clinics; lists included individuals with at least 1 visit at any of the 8 primary care clinics at UCSF in the 2 years prior to the end of the study period. Because individuals are dropped from PCP panels at death, we were unable to ascertain or include individuals who died during the study period.

From the initial study population, we defined the high cost group as those who were in the top decile of total hospital costs, and the high admit group as those who were in the top decile of total hospitalizations during the study period. We elected to use the top decile as a cutoff given that it is a common operational definition used to identify high users to target for intervention.[24]

To examine the relationship between high cost and high admits we defined 3 further subgroups: high costlow admits, high costhigh admits, and low costhigh admits (Figure 1). To explore the face validity of these descriptors and classification scheme, we subsequently examined the proportion of total hospital costs and total hospitalizations each subgroup accounted for with respect to the study population.

Figure 1

RESULTS

Of the 2566 unique individuals included in the analysis (Figure 1), 256 individuals were identified as high cost (top decile, $65,000). This group accounted for 45% of all costs and 22% of all hospitalizations (Figure 2). Two hundred fifty individuals were identified as high admits (top decile, 3 hospitalizations). This group accounted for 32% of all costs and 28% of all hospitalizations.

Figure 2

Only 48% of high cost individuals were also high admit ($65,000 and 3 hospitalizations; n=125, Figure 1). Among high users, we subsequently defined 3 subgroups based on the relationship between cost and hospitalizations (high costlow admits, high costhigh admits, and low costhigh admits). Each subgroup accounted for approximately 5% of the overall study population (Figure 2). The high costlow admits subgroup incurred a proportionate share of hospitalizations (6%) but a disproportionate share of costs (20%). The high costhigh admits subgroup had a disproportionate share of both costs (25%) and hospitalizations (16%). The low costhigh admits subgroup had a proportionate share of costs (7%) but a disproportionate share of hospitalizations (12%).

DISCUSSION

In this study, we found that only half of high cost individuals were also high admit. Further categorizing high users into high costlow admit versus high costhigh admit versus low costhigh admit identified distinct patterns between each group. High costhigh admit individuals were more likely to be hospitalized for medical conditions, whereas high costlow admit individuals were more likely to be hospitalized for surgical conditions. CMS core conditions accounted for a low proportion of overall hospitalizations across all groups.

Our findings suggest several distinct types of high users with different clinical characteristics, utilization, and cost patterns. From a hospital perspective, one implication is that a multifaceted approach to cost containment, rather than the one‐size‐fits‐all strategy of reducing hospitalizations, may be more effective in reducing costs. For example, our findings show that high costlow admit individuals have a disproportionate number of hospitalizations for surgical conditions, longer LOS, and more ICU days. Costs incurred by this subgroup may be more responsive to in‐hospital interventions aimed at reducing procedural costs, LOS, unnecessary use of the ICU, and minimizing postoperative infections and complications rather than to a care management approach.

In contrast, care management strategies such as improving postdischarge care and chronic disease management, which aim to achieve cost savings through reducing hospitalizations, may be more effective in reducing costs among high costhigh admit individuals, who have a high proportion of hospitalizations for medical conditions and the highest proportion of 30‐day readmissions. Such strategies may also be important in optimizing the quality of care for low costhigh admit individuals, who have the highest proportion of medical hospitalizations among all high users, though the potential for cost savings may be more limited in this subgroup.

Our results suggest that current hospital‐based approachesdriven by readmissions penalties for CMS core conditionsmay have less than the expected impact on costs. For example, although high costhigh admit individuals had the highest proportion of 30‐day readmissions, readmissions specifically for CMS core conditions accounted for <1% of costs in this subgroup. Thus, the potential return on an expensive investment in a care management intervention is unclear, given the small number of readmissions for these select conditions. From a broader perspective, the focus on readmissions for CMS core conditions, which overall contribute relatively little to high hospital costs, may not be a comprehensive enough strategy for cost containment. To date, there have been limited policies targeting factors contributing to high hospital costs outside of frequent medical hospitalizations. Medicare's nonpayment policy for treatment of preventable hospital conditions, including surgical site infections, translates prevention of these conditions into cost savings for hospitals.[30] However, this rule has been criticized for not going far enough to drive substantial savings.[31] A new CMS rule authorizes states to identify other provider‐preventable conditions for which Medicaid payment will be prohibited.[32] Future policy efforts should further emphasize a comprehensive, multipronged approach beyond readmissions penalties for select conditions if healthcare cost containment remains a policy priority.

Our results should be interpreted in light of several limitations. First, this was a single‐site study at an academic medical center; the generalizability of our results to other settings is unclear. Our cost data likely reflect local market factors, including the highest wage rates for skilled healthcare labor in the United States.[33] Although the explicit distribution of high user subgroups may be institution‐specific due to variations in our cost structure, we anticipate that the general classification paradigm will be similar in other health systems. Second, we captured utilization and costs only at a single hospital. However, our study population includes only individuals with PCPs at UCSF, and internal data from both Medicare and UCSF's largest private payer show that over 85% of hospitalizations among this population are to UCSF Medical Center. Third, we were able to capture only hospital costs rather than overall healthcare costs. Given that hospital costs account for the single largest category of total national health costs,[4] we expect that future studies examining total health costs will show similar findings. Fourth, our data did not include measures of health status, socioeconomic status, housing, or mental health comorbidities to permit an analysis of these factors, which have been previously related to frequent hospitalizations and high costs.[34, 35, 36, 37, 38, 39] Fifth, due to resource constraints, we were unable to conduct a longitudinal analysis to examine the extent to which individuals are consistently high users over time. Previous studies have described that 20% to 30% of individuals are consistently high users; the remainder have discrete periods of high utilization.[34, 40] This may be an important consideration in the design of future interventions.

Finally, our analysis was limited to individuals with a PCP to allow identification of an accessible cohort for care management. Thus, we did not capture individuals without a PCP and individuals who died during the study period, because these individuals no longer had an assigned PCP following death. Although this approach is consistent with that of many care management programs,[19] these populations are likely to incur higher than average utilization and healthcare costs, and represent important areas for future investigation.

In summary, our study identifies three types of high‐user populations that differ in the proportion of costs attributable to frequent hospitalizations, clinical conditions associated with hospital use, and frequency of 30‐day readmissions. Stratifying high users by both costs and hospitalizations may help identify tailored strategies to more effectively reduce costs and utilization.