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Developing an actionable patient taxonomy to understand and characterize high-cost Medicare patients
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Developing an actionable patient taxonomy to understand and characterize high-cost Medicare patients Yongkang Zhanga,∗, Zachary Grinspana,b,c, Dhruv Khullara,b,d, Mark Aaron Unruha, Elizabeth Shenkmane, Andrea Cohena, Rainu Kaushala,b,c,d a
Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA New York-Presbyterian Hospital, New York, NY, USA c Department of Pediatrics, Weill Cornell Medical College, New York, NY, USA d Department of Medicine, Weill Cornell Medical College, New York, NY, USA e Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA b
ARTICLE INFO
ABSTRACT
Keywords: Case management Health care delivery Implementation research Quality improvement Utilization
Background: Improving care for high-cost patients requires a better understanding of their characteristics and the ability to effectively target interventions. We developed an actionable taxonomy with clinically meaningful patient categories for high-cost Medicare patients—those in the top 10% of total costs. Methods: A cross-sectional study of a Medicare fee-for-service (FFS) patient cohort in the New York metropolitan area. We merged claims and neighborhood social determinants of health data to map patients into actionable categories. Results: Among 428,024 Medicare FFS patients, we mapped the 42,802 high-cost patients into ten overlapping categories, including: multiple chronic conditions, seriously ill, frail, serious mental illness, single condition with high pharmacy cost, chronic pain, end-stage renal disease (ESRD), single high-cost chronic condition, opioid use disorder, and socially vulnerable. Most high-cost patients had multiple chronic conditions (97.4%), followed by serious illness (53.7%) and frailty (48.9%). Patients with ESRD, who were seriously ill, and who were frail were more likely to be high-cost compared to patients in other categories. 72.7% of high-cost patients fell into multiple categories. Conclusions: High-cost patients are highly heterogeneous. A patient taxonomy incorporating medical, behavioral, and social characteristics may help providers better understand their characteristics and health needs. Implications: Mapping high-cost patients into clinically meaningful and actionable categories that incorporate medical, behavioral, and social factors could help health systems target interventions. Integrated approaches, including medical care, behavioral health, and social services may be needed to effectively and efficiently care for high-cost patients.
1. Introduction High-cost patients comprise a small group of individuals who account for a disproportionate share of healthcare spending and utilization.1 These patients are more likely to interact with the health system, incur preventable costs, and suffer quality and safety problems.2 The high concentration of spending among high-cost patients has motivated payers and providers to design new care models to better meet their needs, improve quality, and reduce unnecessary utilization.3,4 Research suggests that high-cost patients are not a homogenous group, but rather, have varied medical conditions, functional limitations, and social circumstances.3,5–7 Prior research suggested that ∗
programs created for subcategories of high-cost patients with shared characteristics and needs may effectively treat such patients.8 Developing actionable categories that capture the characteristics of high-cost patients may help health systems understand their patient population, allocate resources, and tailor interventions to fit the needs of each category of patients. Prior research has focused on developing mutually exclusive patient segments based on patients’ medical characteristics. Many patient segments have been derived from literature reviews, clinician input, and expert opinion. For example, Denver Health, a health system in Colorado, developed a segmentation framework with nine patient groups.9 Clough et al. identified eight distinct patient groups of high-
Corresponding author. 402 E 67th St, New York, NY, 10065, USA. E-mail address: [email protected] (Y. Zhang).
https://doi.org/10.1016/j.hjdsi.2019.100406 Received 19 July 2019; Received in revised form 17 December 2019; Accepted 22 December 2019 2213-0764/ © 2020 Elsevier Inc. All rights reserved.
Please cite this article as: Yongkang Zhang, et al., Healthcare, https://doi.org/10.1016/j.hjdsi.2019.100406
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cost Medicare patients, including patients with end-stage renal disease and those undergoing major surgery.10 A recent study by Joynt et al. divided Medicare beneficiaries into six segments: disabled < 65, frail, major complex chronic illness, minor complex chronic illness, simple chronic illness, and relatively healthy.11 This segmentation has been applied in other studies to understand the characteristics and healthcare utilization of high-cost patients.12 The National Academy of Medicine expanded this segmentation to overall patient population by including children.3 Other studies have used data driven methods, such as clustering analysis to develop patient subgroups. For example, Newcomer et al. identified ten clusters for complex patients, including chronic pain with mental health conditions and cardiac disease and obesity.13 More recently, Powers and colleagues developed ten subgroups for high-cost Medicare Advantage patients.14 Finally, Lee and colleagues developed patient segments based on healthcare utilization, such as number of admissions, surgeries, and ED visits to identify patient clusters.6 While these taxonomies provide important insights about high-cost patients, several practical challenges may limit the extent to which health systems can employ them to understand their patient populations and identify effective interventions. First, mutually exclusive segments map each patient to a single group. This approach may not fully capture the complexity of a patients’ medical status or the totality of their needs. Developing a taxonomy in which patients can fall into multiple, overlapping categories may help providers and researchers better understand the characteristics and needs of high-cost patients. Second, most studies have primarily focused on medical conditions, and only some have incorporated mental health conditions (e.g., diabetes with obesity and mental health). Considering mental health and other behavioral issues as independent category may be important given their prevalence in the Medicare population and the need for specialized interventions to address them. Third, prior taxonomies relied heavily on administrative data (usually Medicare fee-for-service claims) and do not robustly incorporate patient social conditions. A growing literature suggests that social determinants of health (SDH), including both individual-level and community-level social characteristics, are associated with higher utilization and costs.3 Incorporating SDH into a highcost patient taxonomy may be necessary to target services and reduce unnecessary spending. In this study, we developed a new taxonomy with ten overlapping patient categories to understand the medical, behavioral, and social complexity of high-cost Medicare patients. We conceptualized these categories based on a combination of qualitative and quantitative methods, including previously developed taxonomies.15,16 We operationalized these categories using a dataset that included Medicare claims and social risk factors. We hypothesized that 1) a set of categories could be developed to capture a majority of high-cost patients; 2) the probability of being a high-cost patient varies significantly by patient categories; and 3) a large proportion of high-cost patients would fall into multiple categories because of their medical, behavioral, and social complexity.
2.2. Data sources and study population The primary analysis included 428,024 Medicare FFS beneficiaries continuously enrolled in Medicare Parts A and B in 2013. Beneficiaries were excluded if they were 1) dually-eligible because their cost information was not completely captured by Medicare claims (we performed a sensitivity analysis for the dual-eligible patients); 2) had managed care participation in any month; or 3) died during the year, as limited months of enrollment may result in artificially low costs. Patients included in our sample represented approximately 18.9% of all Medicare FFS beneficiary in the New York metropolitan area. A chart illustrating the sample selection process is presented in Appendix Fig. 1. Data sources included Medicare FFS claims for Parts A and B, Part D claims, and community-level social determinants of health data from 2013. Specifically, we used the following files: Carrier, Outpatient, MedPAR for inpatient care, Skilled Nursing Facility (SNF), Home Health Agency (HHA), Hospice, Durable Medical Equipment, Part D Drug Event, and Master Beneficiary Summary. Neighborhood social determinants of health data at the 5-digit zip-code level were taken from the American Community Survey.17 2.3. Analysis 2.3.1. Development of patient taxonomy To develop a patient taxonomy for high-cost patients, we started with a literature review to identify other taxonomies in previous research. We also conducted more than 50 focus groups and interviews with patients, physicians, health system leaders, and health policy experts to collect their perspectives on novel high-cost categories as well as how clinically meaningful and potentially preventable these categories were. We then conducted preliminary analyses to (1) examine if a high-cost category could be measured using the combined claims and social determinants of health data and (2) examine if there was differential spending between high-cost and non-high-cost patients within a category. Additional details about taxonomy development are available in the Appendix (Methodology appendix). Our final taxonomy included ten patient categories: (1) multiple chronic conditions; (2) seriously ill; (3) frail; (4) serious mental illness; (5) single condition with high pharmacy cost; (6) chronic pain; (7) end-stage renal disease; (8) single high-cost chronic condition; (9) opioid use disorder; and (10) socially vulnerable. The first nine clinical categories were based on patients' medical or behavioral conditions, medical procedures they received (e.g., dialysis), and healthcare utilization (e.g., number of hospitalizations).18–26 Because individual-level social determinants of health (e.g., education and income) data are not available in Medicare claims, we used patients’ residential neighborhood conditions to measure their social needs. Previous studies have found that community-level social conditions are strongly associated with various health outcomes and healthcare utilization.27–31 For the socially vulnerable category, we created the zipcode level Area Deprivation Index (ADI).27,32,33 We defined socially vulnerable patients as those in the top 30% of the ADI score.34,35 Detailed descriptions of these patient categories are available in the Supplementary Appendix Tables 2 and 3
2. Methods 2.1. Overview of the study design
2.3.2. Primary analysis We calculated standardized total Medicare spending for each beneficiary in 2013.36 High-cost patients were defined as those with the highest 10% of total spending. We compared the demographic characteristics and comorbidities of high-cost and non-high-cost patients. We then mapped all patients into ten categories and calculated the percent of high-cost patients captured by each category, as well as the likelihood that a patient in any given category would be high-cost. Our taxonomy allowed a patient to belong to multiple categories. We examined high-cost patients in multiple categories and identified the dominant category pairs where high-cost patients were concentrated.
We performed a retrospective cohort study to identify and categorize high-cost Medicare beneficiaries into ten overlapping patient categories using Medicare claims and neighborhood social condition data based on a sample of Medicare fee-for-service (FFS) beneficiaries in the New York metropolitan area. We examined the percentage of high-cost patients captured by each category and the characteristics of patients within them. We then analyzed the likelihood that patients in a given category would be high-cost. 2
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Table 1 Patient characteristics of high-cost vs. non-high-cost patients.
Age, mean Male Race/Ethnicity
Original reason for Medicare enrollment Average number of chronic conditions Average 2013 Medicare spending in US dollars
Unknown White African American Other Asian Hispanic North American Native ESRD or disability Other
High-cost patients (N = 42,802)
Non-high-cost patients (N = 385,222)
p value
75.5 (69, 83) 20,878 (48.8%) 294 (0.7%) 37,216 (87.0%) 3697 (8.6%) 802 (1.9%) 377 (0.9%) 403 (0.9%) 13 (0.0%) 9461 (22.1%) 33,341 (77.9%) 8.3 (6, 10) $68,481 ($42,880, $78,569)
74.7 (69, 81) 166,222 (43.2%) 3966 (1.0%) 335,114 (87.0%) 28,716 (7.5%) 9008 (2.3%) 4310 (1.1%) 3994 (1.0%) 114 (0.0%) 50,112 (13.0%) 335,110 (86.7%) 5.1 (3, 7) $8234 ($2,789, $11,096)
p < 0.001 p < 0.001 p < 0.001
p < 0.001 p < 0.001 p < 0.001
Notes: Sample include Medicare fee-for-service patients continuously enrolled in Medicare Parts A&B in 2013. ESRD: end-stage renal disease; p values indicate the significance of the difference between the high-cost group and non-high cost group. Parentheses for age, average number of chronic conditions, and average 2013 Medicare spending are interquartile intervals.
2.4. Sensitivity analysis
who were seriously ill or frail were high-cost, and approximately 37% of patients in the chronic pain and opioid use disorder categories were high-cost. Patients in the remaining clinical categories had a relatively low probability of being high-cost patients.
To test the generalizability of the high-cost patient taxonomy, we conducted two sensitivity analyses to understand if our results varied with a dual-eligible patient population and with the exclusion of pharmaceutical costs. First, we identified the high-cost patients among dual-eligible patients and mapped them into patient categories. We repeated the primary analyses for this group of patients. As not all beneficiaries have Part D coverage for pharmaceutical costs, we redefined the high-cost patients among the original Medicare FFS patient cohort after dropping Part D costs and repeated the primary analyses. All analyses were performed using SAS 9.4 and STATA MP 14.0.
3.4. Overlap across categories Because more than 97% of high-cost patients had multiple chronic conditions, we excluded this category from the analysis and focused on high-cost patients falling into other categories. 72.7% of high-cost patients could be mapped into multiple categories (≥2 categories), with 30.8% in two and 41.9% in three or more patient categories (Fig. 1). These patients were most highly concentrated in three pairs of categories: frail and seriously ill (49.7%), frail and serious mental illness (27.0%), and seriously ill and serious mental illness (26.3%).
3. Results 3.1. Patient characteristics
3.5. Sensitivity analysis
A total of 42,802 high-cost patients were identified from 428,024 Medicare beneficiaries. Demographic characteristics differed significantly between high-cost and non-high-cost patents (Table 1). Compared to non-high-cost patients, high-cost patients were more likely to be older (75.5 vs. 74.7, p < 0.001), male (48.8% vs. 43.2% p < 0.001), African American (8.6% vs. 7.5%, p < 0.001), and have more chronic conditions (8.3 vs. 5.1, p < 0.001). High-cost patients were also more likely to have originally qualified for Medicare because of disability or ESRD. Average Medicare spending per beneficiary among high-cost patients was more than 8 times higher than for nonhigh-cost patients ($68,481 vs. $8,234, p < 0.001) (Table 1). The characteristics of high-cost patients in each category also differed from non-high-cost patients (Supplementary Appendix Table 4).
Over 99.7% high-cost dual-eligible patients were captured by nine clinical patient categories (Appendix Table 5). The likelihood of being a high-cost patient in each patient category was lower among dual-eligible patients than the FFS patient population in our primary that did not include dual-eligibles. More high-cost dual-eligible patients (84.5%) than FFS patients (72.7%) fell into multiple categories. Repeating the analysis without Part D costs did not significantly change our results (Appendix Table 6). The distribution of high-cost patients and the likelihood of being a high-cost patient across categories were consistent with our primary analysis after excluding Part D costs. 4. Discussion
3.2. Patient categories and high-cost patients
We developed a taxonomy with ten patient categories to identify and categorize high-cost Medicare FFS patients. We found that these categories captured over 99% of high-cost patients. High-cost patients were most likely to have multiple chronic conditions, serious mental illness, serious medical illness, or frailty. The likelihood of patients being high-cost varied significantly across categories. Patients with ESRD were most likely to be high-cost patients, followed by those who were seriously ill, frail, or who had opioid use disorder or chronic pain conditions. Our results support a growing understanding of the heterogeneity of high-cost patients and the need for a diverse set of interventions to care for them. Incorporating behavioral conditions in our taxonomy allowed us to identify that more than 30% of high-cost patients have serious mental illnesses. Although accounting for a small proportion of the patient population, patients with opioid use disorder have a high
Among all 42,802 high-cost patients, 97.4% had multiple chronic conditions, 53.7% were seriously ill, 48.9% were frail, 32.6% had serious mental health issues, 33.3% had social vulnerability, 13.6% had single condition with high pharmacy cost, 9.6% had chronic pain, 7.8% had ESRD, 3.4% had single high-cost chronic condition, and 1.9% had opioid use disorder (Table 2). The ten categories captured 99.3% of all high-cost patients. 3.3. Likelihood of being a high-cost patient The likelihood of being a high-cost patient varied considerably among categories (Table 2). For example, 78.8% of patients with ESRD were high-cost. By comparison, about half (44.5–46.6%) of patients 3
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Table 2 Patient categories and number of high-cost patients in each category. Patient categories
Number of high-cost patients that fall into each category
% of high-cost patients that fall into each category
Likelihood of being a high-cost patient in each category
Multiple chronic conditions Seriously ill Frail Socially vulnerable Serious mental illness Single condition with high pharmacy cost Chronic pain Patients with ESRD Single high-cost chronic condition Opioid use disorder Patients not in categories Total
41,670 22,991 20,921 14,262 13,968 5834 4106 3319 1435 801 292 42,802
97.4% 53.7% 48.9% 33.3% 32.6% 13.6% 9.6% 7.8% 3.4% 1.9% 0.7% 100.0%
11.5% 46.6% 44.5% 11.1% 24.2% 20.4% 36.8% 78.8% 25.1% 37.3% 0.7% –
Notes: Sample include Medicare fee-for-service patients continuously enrolled in Medicare Parts A&B in 2013. ESRD: end-stage renal disease.
likelihood of being a high-cost patient. Our findings suggest that behavioral conditions are highly prevalent among high-cost Medicare patients and that interventions are needed to better manage them. We also incorporated social determinants of health data to understand social risk factors associated with being a high-cost patient. A growing body of evidence suggests that socially disadvantaged individuals are at higher risk for high healthcare utilization.37–39 How best to measure social vulnerability, however, remains unclear. Researchers generally do not have access to detailed individual-level social data; as such, community-level social indices have often been used to measure patients’ social risk. In this study, we used the ADI at the zip-code level to measure social vulnerability and found a slightly higher proportion of high-cost patients had vulnerable social conditions relative to the general patient population. This is consistent with prior studies finding that neighborhood social conditions have significant but weak association with utilization. For example, one study found that patients with the greatest social disadvantage (i.e., top 10%) had only a 1.6-percentage-point higher risk of hospital readmission compared to those with the least disadvantaged social conditions (i.e., bottom 10%).40 More research is needed to understand the methods to quantify social vulnerability and how social risk factors are associated with healthcare utilization. We also found that more than 70% of high-cost patients fell into
multiple categories. Previous studies of high-cost patient segmentations have generally mapped patients into a single category. Because highcost patients have complex medical, behavioral, and social characteristics, we created a framework with categories that were not mutually exclusive. Health systems hoping to effectively care for high-cost patients may need to develop integrated care models that incorporate social and behavioral factors to reduce unnecessary healthcare utilization.3 Previous groupings of high-cost patients may not be sufficient to align care models with patient needs. Many studies, for example, have used multiple chronic conditions as a marker for high-cost patients, which may not provide enough clinical nuance to target care interventions.41 (We find that nearly all high-cost patients have multiple chronic conditions — as do many patients who are not high-cost). Ultimately, which high-cost patient taxonomy is most helpful to health systems may depend on their patient population (e.g., Medicare or Medicaid), as well as the goal of categorization (e.g., understanding the complexity of patient conditions or cost tiering), and the prevalence of various conditions among patients. Categorizing patients into more nuanced groups may help health systems understand patient characteristics and target interventions to meet patients’ needs. For example, our high-cost categories can be grouped into five domains based on their needs and potentially
Fig. 1. Number of patient categories that high-cost patients falls into, overall sample. Notes: we did not count multiple chronic conditions category as over 97% of high-cost patients were in this category. We only counted number of other eight clinical categories that each high-cost patient falling into. 4
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Fig. 2. High-cost patient categories and potentially effective care models.
effective interventions, including medical care, behavioral health services, social services, palliative care, and pharmaceutical pricing policies (Fig. 2). Patients who are seriously ill may benefit from early palliative care. Socially vulnerable patients likely require services from non-medical organizations, such as transportation and housing. Frail patients may need both social (e.g. programs to address loneliness) and medical interventions. Patients with opioid use disorder and serious mental illness need behavioral interventions. Patients with chronic pain may need both behavioral and medical treatments. ESRD, single highcost chronic condition, or multiple chronic conditions groups will need a care manager that could coordinate their intensive medical care service needs. Finally, pharmaceutical pricing policy that control medication prices will be needed for patients having a condition with high pharmacy cost. This study has several limitations. First, we used a convenience sample of Medicare FFS patients from health systems in New York metropolitan area, it may not be representative of other Medicare patients within this market or the overall Medicare population. The extent to which these categories apply to patients in other areas or to commercially-insured or Medicaid patients is unclear. Prior work, for example, has found that healthcare spending is more concentrated among high-cost Medicaid and commercially-insured patients, compared to Medicare patients.24 Patients covered by insurers other than Medicare may also have different drivers of high healthcare utilization. For example, behavioral and social conditions may be larger contributors to utilization among Medicaid patients, as compared to Medicare patients.24,42,43 Commercially-insured patients generally have fewer chronic conditions and the drivers of high utilization are more often due to acute conditions.42 Second, we excluded patients who died during the study period (as have many other studies on high-cost patients). Patients at the end of life are known to have high healthcare expenditures. However, these patients may appear to be lower cost if they do not contribute to spending throughout a given year. Separate studies are necessary to understand the utilization among high-cost decedents. Third, we used a cross-sectional study design based on one year of claims; it is not clear whether the observed high utilization persists over time. Finally, more research is need to explore how to incorporate clinical data, such as laboratory tests and vital signs, to better understand the contribution of clinical complexity markers to the characterization of high-cost patients.
5. Conclusion We developed a taxonomy with ten patient categories for high-cost Medicare patients. This taxonomy captured most high-cost patients and categorized them into clinically meaningful groups. Our framework has important implications for healthcare delivery and resource allocation by providing a nuanced stratification of high-cost patients based on medical, behavioral, and social factors. This patient grouping framework may help clinicians and health systems better understand their patient populations and improve care models targeted to their needs. Declaration of competing interest The authors report no conflicts of interest. Sponsors of this research played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Acknowledgments This study was funded by grant PCORI/HSD-1604-35187 (“Identifying and Predicting Patients with Preventable High Utilization”) from The Patient-Centered Outcomes Research Institute. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.hjdsi.2019.100406. References 1. Concentration of Health Expenditures in the US Civilian Noninstitutionalized Population, 2014. 2016; 2016https://meps.ahrq.gov/data_files/publications/st497/stat497. shtml, Accessed date: 31 May 2017. 2. Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for high-need, highcost patients - an urgent priority. N Engl J Med. 2016;375:909–911. 3. Long P, Abrams M, Milstein A, Anderson G, Apton KL, Dahlberg M. Effective Care for High-Need Patients. Washington, DC: National Academies Pr; 2017. 4. Figueroa JF, Jha AK. Approach for achieving effective care for high-need patients. JAMA Intern Med. 2018;178:845–846. 5. Hayes SL, Salzberg CA, McCarthy D, et al. High-need, high-cost patients: who are
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26. Watanabe JH, Chau DL, Hirsch JD. Federal and individual spending on the 10 costliest medications in Medicare Part D from 2011 to 2015. J Am Geriatr Soc. 2018;66:1621–1624. 27. Hu J, Kind AJH, Nerenz D. Area deprivation Index predicts readmission risk at an urban teaching hospital. Am J Med Qual. 2018;33:493–501. 28. Joynt Maddox KE, Reidhead M, Hu J, et al. Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program. Health Serv Res. 2019;54:327–336. 29. Kirby JB, Kaneda T. Neighborhood socioeconomic disadvantage and access to health care. J Health Soc Behav. 2005;46:15–31. 30. Robinette JW, Charles ST, Gruenewald TL. Neighborhood socioeconomic status and health: a longitudinal analysis. J Community Health. 2017;42:865–871. 31. Udell JA, Desai NR, Li S, et al. Neighborhood socioeconomic disadvantage and care after myocardial infarction in the national cardiovascular data registry. Circ Cardiovasc Qual Outcomes. 2018;11:e004054. 32. Kind AJ, Jencks S, Brock J, et al. Neighborhood socioeconomic disadvantage and 30day rehospitalization: a retrospective cohort study. Ann Intern Med. 2014;161:765–774. 33. Singh GK. Area deprivation and widening inequalities in US mortality, 1969–1998. Am J Public Health. 2003;93:1137–1143. 34. Whicher D, Rosengren K, Siddiqi S, Simpson L. The Future of Health Services Research: Advancing Health Systems Research and Practice in the United States. Washington, DC: National Academy of Medicin; 2018. 35. Kort D, van Rein N, van der Meer FJM, et al. Relationship between neighborhood socioeconomic status and venous thromboembolism: results from a population-based study. J Thromb Haemost. 2017;15:2352–2360. 36. Pesko MF, Ryan AM, Shortell SM, et al. Spending per Medicare Beneficiary Is Higher in Hospital‐Owned Small‐and Medium‐Sized Physician Practices. Health services research. 2017;53(2):2133–2146. 37. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communities–addressing social needs through Medicare and Medicaid. N Engl J Med. 2016;374:8–11. 38. Adler NE, Cutler DM, Jonathan J, et al. Addressing Social Determinants of Health and Health Disparities. National Academy of Medicine; 2016. 39. Meddings J, Reichert H, Smith SN, et al. The impact of disability and social determinants of health on condition-specific readmissions beyond Medicare risk adjustments: a cohort study. J Gen Intern Med. 2017;32:71–80. 40. Jencks SF, Schuster A, Dougherty GB, Gerovich S, Brock JE, Kind AJH. Safety-net hospitals, neighborhood disadvantage, and readmissions under Maryland’s all-payer program: an observational study. Ann Intern Med. 2019;171(2):91–98. 41. Koroukian SM, Schiltz N, Warner DF, et al. Combinations of chronic conditions, functional limitations, and geriatric syndromes that predict health outcomes. J Gen Intern Med. 2016;31:630–637. 42. Wammes JJG, van der Wees PJ, Tanke MAC, Westert GP, Jeurissen PPT. Systematic review of high-cost patients' characteristics and healthcare utilisation. BMJ Open. 2018;8:e023113. 43. Bodenheimer T. Strategies to reduce costs and improve care for high-utilizing Medicaid patients: reflections on pioneering programs. Available at https://www. chcs.org/media/HighUtilizerReport_102413_Final3.pdf; 2013, Accessed date: 11 November 2019.
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Healthcare journal homepage: www.elsevier.com/locate/healthcare
Developing an actionable patient taxonomy to understand and characterize high-cost Medicare patients Yongkang Zhanga,∗, Zachary Grinspana,b,c, Dhruv Khullara,b,d, Mark Aaron Unruha, Elizabeth Shenkmane, Andrea Cohena, Rainu Kaushala,b,c,d a
Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA New York-Presbyterian Hospital, New York, NY, USA c Department of Pediatrics, Weill Cornell Medical College, New York, NY, USA d Department of Medicine, Weill Cornell Medical College, New York, NY, USA e Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA b
ARTICLE INFO
ABSTRACT
Keywords: Case management Health care delivery Implementation research Quality improvement Utilization
Background: Improving care for high-cost patients requires a better understanding of their characteristics and the ability to effectively target interventions. We developed an actionable taxonomy with clinically meaningful patient categories for high-cost Medicare patients—those in the top 10% of total costs. Methods: A cross-sectional study of a Medicare fee-for-service (FFS) patient cohort in the New York metropolitan area. We merged claims and neighborhood social determinants of health data to map patients into actionable categories. Results: Among 428,024 Medicare FFS patients, we mapped the 42,802 high-cost patients into ten overlapping categories, including: multiple chronic conditions, seriously ill, frail, serious mental illness, single condition with high pharmacy cost, chronic pain, end-stage renal disease (ESRD), single high-cost chronic condition, opioid use disorder, and socially vulnerable. Most high-cost patients had multiple chronic conditions (97.4%), followed by serious illness (53.7%) and frailty (48.9%). Patients with ESRD, who were seriously ill, and who were frail were more likely to be high-cost compared to patients in other categories. 72.7% of high-cost patients fell into multiple categories. Conclusions: High-cost patients are highly heterogeneous. A patient taxonomy incorporating medical, behavioral, and social characteristics may help providers better understand their characteristics and health needs. Implications: Mapping high-cost patients into clinically meaningful and actionable categories that incorporate medical, behavioral, and social factors could help health systems target interventions. Integrated approaches, including medical care, behavioral health, and social services may be needed to effectively and efficiently care for high-cost patients.
1. Introduction High-cost patients comprise a small group of individuals who account for a disproportionate share of healthcare spending and utilization.1 These patients are more likely to interact with the health system, incur preventable costs, and suffer quality and safety problems.2 The high concentration of spending among high-cost patients has motivated payers and providers to design new care models to better meet their needs, improve quality, and reduce unnecessary utilization.3,4 Research suggests that high-cost patients are not a homogenous group, but rather, have varied medical conditions, functional limitations, and social circumstances.3,5–7 Prior research suggested that ∗
programs created for subcategories of high-cost patients with shared characteristics and needs may effectively treat such patients.8 Developing actionable categories that capture the characteristics of high-cost patients may help health systems understand their patient population, allocate resources, and tailor interventions to fit the needs of each category of patients. Prior research has focused on developing mutually exclusive patient segments based on patients’ medical characteristics. Many patient segments have been derived from literature reviews, clinician input, and expert opinion. For example, Denver Health, a health system in Colorado, developed a segmentation framework with nine patient groups.9 Clough et al. identified eight distinct patient groups of high-
Corresponding author. 402 E 67th St, New York, NY, 10065, USA. E-mail address: [email protected] (Y. Zhang).
https://doi.org/10.1016/j.hjdsi.2019.100406 Received 19 July 2019; Received in revised form 17 December 2019; Accepted 22 December 2019 2213-0764/ © 2020 Elsevier Inc. All rights reserved.
Please cite this article as: Yongkang Zhang, et al., Healthcare, https://doi.org/10.1016/j.hjdsi.2019.100406
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cost Medicare patients, including patients with end-stage renal disease and those undergoing major surgery.10 A recent study by Joynt et al. divided Medicare beneficiaries into six segments: disabled < 65, frail, major complex chronic illness, minor complex chronic illness, simple chronic illness, and relatively healthy.11 This segmentation has been applied in other studies to understand the characteristics and healthcare utilization of high-cost patients.12 The National Academy of Medicine expanded this segmentation to overall patient population by including children.3 Other studies have used data driven methods, such as clustering analysis to develop patient subgroups. For example, Newcomer et al. identified ten clusters for complex patients, including chronic pain with mental health conditions and cardiac disease and obesity.13 More recently, Powers and colleagues developed ten subgroups for high-cost Medicare Advantage patients.14 Finally, Lee and colleagues developed patient segments based on healthcare utilization, such as number of admissions, surgeries, and ED visits to identify patient clusters.6 While these taxonomies provide important insights about high-cost patients, several practical challenges may limit the extent to which health systems can employ them to understand their patient populations and identify effective interventions. First, mutually exclusive segments map each patient to a single group. This approach may not fully capture the complexity of a patients’ medical status or the totality of their needs. Developing a taxonomy in which patients can fall into multiple, overlapping categories may help providers and researchers better understand the characteristics and needs of high-cost patients. Second, most studies have primarily focused on medical conditions, and only some have incorporated mental health conditions (e.g., diabetes with obesity and mental health). Considering mental health and other behavioral issues as independent category may be important given their prevalence in the Medicare population and the need for specialized interventions to address them. Third, prior taxonomies relied heavily on administrative data (usually Medicare fee-for-service claims) and do not robustly incorporate patient social conditions. A growing literature suggests that social determinants of health (SDH), including both individual-level and community-level social characteristics, are associated with higher utilization and costs.3 Incorporating SDH into a highcost patient taxonomy may be necessary to target services and reduce unnecessary spending. In this study, we developed a new taxonomy with ten overlapping patient categories to understand the medical, behavioral, and social complexity of high-cost Medicare patients. We conceptualized these categories based on a combination of qualitative and quantitative methods, including previously developed taxonomies.15,16 We operationalized these categories using a dataset that included Medicare claims and social risk factors. We hypothesized that 1) a set of categories could be developed to capture a majority of high-cost patients; 2) the probability of being a high-cost patient varies significantly by patient categories; and 3) a large proportion of high-cost patients would fall into multiple categories because of their medical, behavioral, and social complexity.
2.2. Data sources and study population The primary analysis included 428,024 Medicare FFS beneficiaries continuously enrolled in Medicare Parts A and B in 2013. Beneficiaries were excluded if they were 1) dually-eligible because their cost information was not completely captured by Medicare claims (we performed a sensitivity analysis for the dual-eligible patients); 2) had managed care participation in any month; or 3) died during the year, as limited months of enrollment may result in artificially low costs. Patients included in our sample represented approximately 18.9% of all Medicare FFS beneficiary in the New York metropolitan area. A chart illustrating the sample selection process is presented in Appendix Fig. 1. Data sources included Medicare FFS claims for Parts A and B, Part D claims, and community-level social determinants of health data from 2013. Specifically, we used the following files: Carrier, Outpatient, MedPAR for inpatient care, Skilled Nursing Facility (SNF), Home Health Agency (HHA), Hospice, Durable Medical Equipment, Part D Drug Event, and Master Beneficiary Summary. Neighborhood social determinants of health data at the 5-digit zip-code level were taken from the American Community Survey.17 2.3. Analysis 2.3.1. Development of patient taxonomy To develop a patient taxonomy for high-cost patients, we started with a literature review to identify other taxonomies in previous research. We also conducted more than 50 focus groups and interviews with patients, physicians, health system leaders, and health policy experts to collect their perspectives on novel high-cost categories as well as how clinically meaningful and potentially preventable these categories were. We then conducted preliminary analyses to (1) examine if a high-cost category could be measured using the combined claims and social determinants of health data and (2) examine if there was differential spending between high-cost and non-high-cost patients within a category. Additional details about taxonomy development are available in the Appendix (Methodology appendix). Our final taxonomy included ten patient categories: (1) multiple chronic conditions; (2) seriously ill; (3) frail; (4) serious mental illness; (5) single condition with high pharmacy cost; (6) chronic pain; (7) end-stage renal disease; (8) single high-cost chronic condition; (9) opioid use disorder; and (10) socially vulnerable. The first nine clinical categories were based on patients' medical or behavioral conditions, medical procedures they received (e.g., dialysis), and healthcare utilization (e.g., number of hospitalizations).18–26 Because individual-level social determinants of health (e.g., education and income) data are not available in Medicare claims, we used patients’ residential neighborhood conditions to measure their social needs. Previous studies have found that community-level social conditions are strongly associated with various health outcomes and healthcare utilization.27–31 For the socially vulnerable category, we created the zipcode level Area Deprivation Index (ADI).27,32,33 We defined socially vulnerable patients as those in the top 30% of the ADI score.34,35 Detailed descriptions of these patient categories are available in the Supplementary Appendix Tables 2 and 3
2. Methods 2.1. Overview of the study design
2.3.2. Primary analysis We calculated standardized total Medicare spending for each beneficiary in 2013.36 High-cost patients were defined as those with the highest 10% of total spending. We compared the demographic characteristics and comorbidities of high-cost and non-high-cost patients. We then mapped all patients into ten categories and calculated the percent of high-cost patients captured by each category, as well as the likelihood that a patient in any given category would be high-cost. Our taxonomy allowed a patient to belong to multiple categories. We examined high-cost patients in multiple categories and identified the dominant category pairs where high-cost patients were concentrated.
We performed a retrospective cohort study to identify and categorize high-cost Medicare beneficiaries into ten overlapping patient categories using Medicare claims and neighborhood social condition data based on a sample of Medicare fee-for-service (FFS) beneficiaries in the New York metropolitan area. We examined the percentage of high-cost patients captured by each category and the characteristics of patients within them. We then analyzed the likelihood that patients in a given category would be high-cost. 2
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Table 1 Patient characteristics of high-cost vs. non-high-cost patients.
Age, mean Male Race/Ethnicity
Original reason for Medicare enrollment Average number of chronic conditions Average 2013 Medicare spending in US dollars
Unknown White African American Other Asian Hispanic North American Native ESRD or disability Other
High-cost patients (N = 42,802)
Non-high-cost patients (N = 385,222)
p value
75.5 (69, 83) 20,878 (48.8%) 294 (0.7%) 37,216 (87.0%) 3697 (8.6%) 802 (1.9%) 377 (0.9%) 403 (0.9%) 13 (0.0%) 9461 (22.1%) 33,341 (77.9%) 8.3 (6, 10) $68,481 ($42,880, $78,569)
74.7 (69, 81) 166,222 (43.2%) 3966 (1.0%) 335,114 (87.0%) 28,716 (7.5%) 9008 (2.3%) 4310 (1.1%) 3994 (1.0%) 114 (0.0%) 50,112 (13.0%) 335,110 (86.7%) 5.1 (3, 7) $8234 ($2,789, $11,096)
p < 0.001 p < 0.001 p < 0.001
p < 0.001 p < 0.001 p < 0.001
Notes: Sample include Medicare fee-for-service patients continuously enrolled in Medicare Parts A&B in 2013. ESRD: end-stage renal disease; p values indicate the significance of the difference between the high-cost group and non-high cost group. Parentheses for age, average number of chronic conditions, and average 2013 Medicare spending are interquartile intervals.
2.4. Sensitivity analysis
who were seriously ill or frail were high-cost, and approximately 37% of patients in the chronic pain and opioid use disorder categories were high-cost. Patients in the remaining clinical categories had a relatively low probability of being high-cost patients.
To test the generalizability of the high-cost patient taxonomy, we conducted two sensitivity analyses to understand if our results varied with a dual-eligible patient population and with the exclusion of pharmaceutical costs. First, we identified the high-cost patients among dual-eligible patients and mapped them into patient categories. We repeated the primary analyses for this group of patients. As not all beneficiaries have Part D coverage for pharmaceutical costs, we redefined the high-cost patients among the original Medicare FFS patient cohort after dropping Part D costs and repeated the primary analyses. All analyses were performed using SAS 9.4 and STATA MP 14.0.
3.4. Overlap across categories Because more than 97% of high-cost patients had multiple chronic conditions, we excluded this category from the analysis and focused on high-cost patients falling into other categories. 72.7% of high-cost patients could be mapped into multiple categories (≥2 categories), with 30.8% in two and 41.9% in three or more patient categories (Fig. 1). These patients were most highly concentrated in three pairs of categories: frail and seriously ill (49.7%), frail and serious mental illness (27.0%), and seriously ill and serious mental illness (26.3%).
3. Results 3.1. Patient characteristics
3.5. Sensitivity analysis
A total of 42,802 high-cost patients were identified from 428,024 Medicare beneficiaries. Demographic characteristics differed significantly between high-cost and non-high-cost patents (Table 1). Compared to non-high-cost patients, high-cost patients were more likely to be older (75.5 vs. 74.7, p < 0.001), male (48.8% vs. 43.2% p < 0.001), African American (8.6% vs. 7.5%, p < 0.001), and have more chronic conditions (8.3 vs. 5.1, p < 0.001). High-cost patients were also more likely to have originally qualified for Medicare because of disability or ESRD. Average Medicare spending per beneficiary among high-cost patients was more than 8 times higher than for nonhigh-cost patients ($68,481 vs. $8,234, p < 0.001) (Table 1). The characteristics of high-cost patients in each category also differed from non-high-cost patients (Supplementary Appendix Table 4).
Over 99.7% high-cost dual-eligible patients were captured by nine clinical patient categories (Appendix Table 5). The likelihood of being a high-cost patient in each patient category was lower among dual-eligible patients than the FFS patient population in our primary that did not include dual-eligibles. More high-cost dual-eligible patients (84.5%) than FFS patients (72.7%) fell into multiple categories. Repeating the analysis without Part D costs did not significantly change our results (Appendix Table 6). The distribution of high-cost patients and the likelihood of being a high-cost patient across categories were consistent with our primary analysis after excluding Part D costs. 4. Discussion
3.2. Patient categories and high-cost patients
We developed a taxonomy with ten patient categories to identify and categorize high-cost Medicare FFS patients. We found that these categories captured over 99% of high-cost patients. High-cost patients were most likely to have multiple chronic conditions, serious mental illness, serious medical illness, or frailty. The likelihood of patients being high-cost varied significantly across categories. Patients with ESRD were most likely to be high-cost patients, followed by those who were seriously ill, frail, or who had opioid use disorder or chronic pain conditions. Our results support a growing understanding of the heterogeneity of high-cost patients and the need for a diverse set of interventions to care for them. Incorporating behavioral conditions in our taxonomy allowed us to identify that more than 30% of high-cost patients have serious mental illnesses. Although accounting for a small proportion of the patient population, patients with opioid use disorder have a high
Among all 42,802 high-cost patients, 97.4% had multiple chronic conditions, 53.7% were seriously ill, 48.9% were frail, 32.6% had serious mental health issues, 33.3% had social vulnerability, 13.6% had single condition with high pharmacy cost, 9.6% had chronic pain, 7.8% had ESRD, 3.4% had single high-cost chronic condition, and 1.9% had opioid use disorder (Table 2). The ten categories captured 99.3% of all high-cost patients. 3.3. Likelihood of being a high-cost patient The likelihood of being a high-cost patient varied considerably among categories (Table 2). For example, 78.8% of patients with ESRD were high-cost. By comparison, about half (44.5–46.6%) of patients 3
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Table 2 Patient categories and number of high-cost patients in each category. Patient categories
Number of high-cost patients that fall into each category
% of high-cost patients that fall into each category
Likelihood of being a high-cost patient in each category
Multiple chronic conditions Seriously ill Frail Socially vulnerable Serious mental illness Single condition with high pharmacy cost Chronic pain Patients with ESRD Single high-cost chronic condition Opioid use disorder Patients not in categories Total
41,670 22,991 20,921 14,262 13,968 5834 4106 3319 1435 801 292 42,802
97.4% 53.7% 48.9% 33.3% 32.6% 13.6% 9.6% 7.8% 3.4% 1.9% 0.7% 100.0%
11.5% 46.6% 44.5% 11.1% 24.2% 20.4% 36.8% 78.8% 25.1% 37.3% 0.7% –
Notes: Sample include Medicare fee-for-service patients continuously enrolled in Medicare Parts A&B in 2013. ESRD: end-stage renal disease.
likelihood of being a high-cost patient. Our findings suggest that behavioral conditions are highly prevalent among high-cost Medicare patients and that interventions are needed to better manage them. We also incorporated social determinants of health data to understand social risk factors associated with being a high-cost patient. A growing body of evidence suggests that socially disadvantaged individuals are at higher risk for high healthcare utilization.37–39 How best to measure social vulnerability, however, remains unclear. Researchers generally do not have access to detailed individual-level social data; as such, community-level social indices have often been used to measure patients’ social risk. In this study, we used the ADI at the zip-code level to measure social vulnerability and found a slightly higher proportion of high-cost patients had vulnerable social conditions relative to the general patient population. This is consistent with prior studies finding that neighborhood social conditions have significant but weak association with utilization. For example, one study found that patients with the greatest social disadvantage (i.e., top 10%) had only a 1.6-percentage-point higher risk of hospital readmission compared to those with the least disadvantaged social conditions (i.e., bottom 10%).40 More research is needed to understand the methods to quantify social vulnerability and how social risk factors are associated with healthcare utilization. We also found that more than 70% of high-cost patients fell into
multiple categories. Previous studies of high-cost patient segmentations have generally mapped patients into a single category. Because highcost patients have complex medical, behavioral, and social characteristics, we created a framework with categories that were not mutually exclusive. Health systems hoping to effectively care for high-cost patients may need to develop integrated care models that incorporate social and behavioral factors to reduce unnecessary healthcare utilization.3 Previous groupings of high-cost patients may not be sufficient to align care models with patient needs. Many studies, for example, have used multiple chronic conditions as a marker for high-cost patients, which may not provide enough clinical nuance to target care interventions.41 (We find that nearly all high-cost patients have multiple chronic conditions — as do many patients who are not high-cost). Ultimately, which high-cost patient taxonomy is most helpful to health systems may depend on their patient population (e.g., Medicare or Medicaid), as well as the goal of categorization (e.g., understanding the complexity of patient conditions or cost tiering), and the prevalence of various conditions among patients. Categorizing patients into more nuanced groups may help health systems understand patient characteristics and target interventions to meet patients’ needs. For example, our high-cost categories can be grouped into five domains based on their needs and potentially
Fig. 1. Number of patient categories that high-cost patients falls into, overall sample. Notes: we did not count multiple chronic conditions category as over 97% of high-cost patients were in this category. We only counted number of other eight clinical categories that each high-cost patient falling into. 4
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Fig. 2. High-cost patient categories and potentially effective care models.
effective interventions, including medical care, behavioral health services, social services, palliative care, and pharmaceutical pricing policies (Fig. 2). Patients who are seriously ill may benefit from early palliative care. Socially vulnerable patients likely require services from non-medical organizations, such as transportation and housing. Frail patients may need both social (e.g. programs to address loneliness) and medical interventions. Patients with opioid use disorder and serious mental illness need behavioral interventions. Patients with chronic pain may need both behavioral and medical treatments. ESRD, single highcost chronic condition, or multiple chronic conditions groups will need a care manager that could coordinate their intensive medical care service needs. Finally, pharmaceutical pricing policy that control medication prices will be needed for patients having a condition with high pharmacy cost. This study has several limitations. First, we used a convenience sample of Medicare FFS patients from health systems in New York metropolitan area, it may not be representative of other Medicare patients within this market or the overall Medicare population. The extent to which these categories apply to patients in other areas or to commercially-insured or Medicaid patients is unclear. Prior work, for example, has found that healthcare spending is more concentrated among high-cost Medicaid and commercially-insured patients, compared to Medicare patients.24 Patients covered by insurers other than Medicare may also have different drivers of high healthcare utilization. For example, behavioral and social conditions may be larger contributors to utilization among Medicaid patients, as compared to Medicare patients.24,42,43 Commercially-insured patients generally have fewer chronic conditions and the drivers of high utilization are more often due to acute conditions.42 Second, we excluded patients who died during the study period (as have many other studies on high-cost patients). Patients at the end of life are known to have high healthcare expenditures. However, these patients may appear to be lower cost if they do not contribute to spending throughout a given year. Separate studies are necessary to understand the utilization among high-cost decedents. Third, we used a cross-sectional study design based on one year of claims; it is not clear whether the observed high utilization persists over time. Finally, more research is need to explore how to incorporate clinical data, such as laboratory tests and vital signs, to better understand the contribution of clinical complexity markers to the characterization of high-cost patients.
5. Conclusion We developed a taxonomy with ten patient categories for high-cost Medicare patients. This taxonomy captured most high-cost patients and categorized them into clinically meaningful groups. Our framework has important implications for healthcare delivery and resource allocation by providing a nuanced stratification of high-cost patients based on medical, behavioral, and social factors. This patient grouping framework may help clinicians and health systems better understand their patient populations and improve care models targeted to their needs. Declaration of competing interest The authors report no conflicts of interest. Sponsors of this research played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Acknowledgments This study was funded by grant PCORI/HSD-1604-35187 (“Identifying and Predicting Patients with Preventable High Utilization”) from The Patient-Centered Outcomes Research Institute. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.hjdsi.2019.100406. References 1. Concentration of Health Expenditures in the US Civilian Noninstitutionalized Population, 2014. 2016; 2016https://meps.ahrq.gov/data_files/publications/st497/stat497. shtml, Accessed date: 31 May 2017. 2. Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for high-need, highcost patients - an urgent priority. N Engl J Med. 2016;375:909–911. 3. Long P, Abrams M, Milstein A, Anderson G, Apton KL, Dahlberg M. Effective Care for High-Need Patients. Washington, DC: National Academies Pr; 2017. 4. Figueroa JF, Jha AK. Approach for achieving effective care for high-need patients. JAMA Intern Med. 2018;178:845–846. 5. Hayes SL, Salzberg CA, McCarthy D, et al. High-need, high-cost patients: who are
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