Subgroups of High-Cost Medicare Advantage Patients: an Observational Study
There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations.
To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients.
We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups.
Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries.
Spending, utilization, and mortality.
High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+).
We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.
KEY WORDShigh-cost patients care management medicare advantage
This study was presented, in part, at AcademyHealth; June 25, 2018; Seattle, WA.
This study is supported by a grant from the Anthem Public Policy Institute and, in part, under a grant with the Pennsylvania Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations, or conclusions.
Compliance with Ethical Standards
This study was approved by the Institutional Review Board of the University of Pennsylvania.
Conflict of Interest
Dr. Navathe reports that he has received grant support from Hawaii Medical Service Association and Oscar Health; personal fees from Navvis and Co., Navigant Inc., Lynx Medical, Indegene Inc., and Sutherland Global Services; personal fees and equity from NavaHealth; serves on the board without compensation for Integrated Services, Inc., speaking fees from the Cleveland Clinic, and honoraria from Elsevier Press. Dr. Linn reports that she has received grant support from Hawaii Medical Service Association. Dr. Jain reports employment by Anthem, Inc.; stock ownership in Anthem, Inc., and honoraria from Elsevier Press. Ms. Kowalski reports employment by Anthem, Inc. and stock ownership in Anthem, Inc. and Amazon. Dr. Powers reports employment by Anthem, Inc. All other authors declare no conflicts of interest.
- 1.National Institute of Health Care Management. The Concentration of U.S. Health Care Spending. 2017; https://www.nihcm.org/topics/cost-quality/concentration-of-us-health-care-spending. Accessed October 19, 2018.
- 3.National Academy of Medicine. Effective care for high-need patients. Washington, DC: National Academy of Medicine; 2017.Google Scholar
- 8.Jacobson G, Damico A, Neuman T, Gold M. Medicare Advantage 2017 Spotlight: Enrollment Market Update. 2017; http://files.kff.org/attachment/Issue-Brief-Medicare-Advantage-2017-Spotlight-Enrollment-Market-Update. Accessed October 19, 2018.
- 9.Hong CS, Siegel AL, Ferris TG. Caring for High-Need, High-Cost Patients: What Makes for a Successful Care Management Program? 2014; https://www.commonwealthfund.org/sites/default/files/documents/___media_files_publications_issue_brief_2014_aug_1764_hong_caring_for_high_need_high_cost_patients_ccm_ib.pdf. Accessed October 19, 2018.
- 11.Hayes SL, Salzberg CA, McCarthy D, et al. High-Need, High-Cost Patients: Who Are They and How Do They Use Health Care—A Population-Based Comparison of Demographics, Health Care Use, and Expenditures. 2016; https://www.commonwealthfund.org/sites/default/files/documents/___media_files_publications_issue_brief_2016_aug_1897_hayes_who_are_high_need_high_cost_patients_v2.pdf. Accessed October 19, 2018.
- 12.Cohen S, Uberoi N. Differentials in the concentration in the level of health expenditures across population subgroups in the U.S., 2010. Statistical Brief #421 2013; https://meps.ahrq.gov/data_files/publications/st421/stat421.shtml. Accessed October 19, 2018.
- 13.Zodet M. Characteristics of Persons with High Health Care Expenditures in the U.S. Civilian Noninstitutionalized Population, 2014. Statistical Brief #496 2016; https://meps.ahrq.gov/data_files/publications/st496/stat496.pdf. Accessed October 19, 2018.
- 16.Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD-9-CM. 2017; https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed October 19, 2018.
- 17.Agency for Healthcare Research and Quality. Clinical Classifications Software for Services and Procedures. 2017; https://www.hcup-us.ahrq.gov/toolssoftware/ccs_svcsproc/ccssvcproc.jsp. Accessed October 19, 2018.
- 19.Agency for Healthcare Research and Quality. Prevention Quality Indicators Overview. 2017; http://www.qualityindicators.ahrq.gov/modules/pqi_resources.aspx. Accessed October 19, 2018.
- 20.New York University. Faculty & Research. 2017; https://wagner.nyu.edu/faculty/billings/nyued-background. Accessed October 19, 2018.
- 23.Yan J, Linn KA, Powers BW, et al. Applying Machine Learning Algorithms to Segment High-Cost Patient Populations. J Gen Intern Med. 2018.Google Scholar
- 24.Van Der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res. 2014;15(1):3221–3245.Google Scholar
- 25.Maaten Lvd, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579–2605.Google Scholar
- 26.Ester M, Kriegel H-P, Sander J, Xu X. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining; 1996; Portland, Oregon.Google Scholar
- 28.Congressional Budget Office. High-Cost Medicare Beneficiaries. Washington, DC: Congressional Budget Office;2005.Google Scholar
- 29.The Lewin Group. Individuals Living in the Community with Chronic Conditions and Functional Limitations: A Closer Look. 2010; https://aspe.hhs.gov/system/files/pdf/75961/closerlook.pdf. Accessed August 21, 2018.
- 31.Figueroa JF, Jha AK. Approach for achieving effective care for high-need patients. JAMA Intern Med. 2018.Google Scholar
- 34.Hostetter M, Klein S, McCarthy D. CareMore: Improving Outcomes and Controlling Health Care Spending for High-Needs Patients. 2017; https://www.commonwealthfund.org/sites/default/files/documents/___media_files_publications_case_study_2017_mar_1937_hostetter_caremore_case_study_v2.pdf. Accessed October 19, 2018.