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Subgroups of High-Cost Medicare Advantage Patients: an Observational Study

  • Brian W. Powers
  • Jiali Yan
  • Jingsan Zhu
  • Kristin A. Linn
  • Sachin H. Jain
  • Jennifer L. Kowalski
  • Amol S. Navathe
Original Research

Abstract

Background

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.

Objective

To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients.

Design

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.

Participants

Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries.

Main Measures

Spending, utilization, and mortality.

Key Results

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+).

Conclusions

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 WORDS

high-cost patients care management medicare advantage 

Notes

Prior Presentation(s)

This study was presented, in part, at AcademyHealth; June 25, 2018; Seattle, WA.

Funders

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.

Supplementary material

11606_2018_4759_MOESM1_ESM.docx (107 kb)
ESM 1 (DOCX 106 kb)

References

  1. 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.
  2. 2.
    Riley GF. Long-term trends in the concentration of Medicare spending. Health Aff (Millwood). 2007;26(3):808–816.CrossRefGoogle Scholar
  3. 3.
    National Academy of Medicine. Effective care for high-need patients. Washington, DC: National Academy of Medicine; 2017.Google Scholar
  4. 4.
    Hong CS, Abrams MK, Ferris TG. Toward increased adoption of complex care management. N Engl J Med. 2014;371(6):491–493.CrossRefGoogle Scholar
  5. 5.
    McWilliams JM. Cost Containment and the Tale of Care Coordination. N Engl J Med. 2016;375(23):2218–2220.CrossRefGoogle Scholar
  6. 6.
    Clough JD, Riley GF, Cohen M, et al. Patterns of care for clinically distinct segments of high cost Medicare beneficiaries. Healthc (Amst). 2016;4(3):160–165.CrossRefGoogle Scholar
  7. 7.
    Joynt KE, Figueroa JF, Beaulieu N, Wild RC, Orav EJ, Jha AK. Segmenting high-cost Medicare patients into potentially actionable cohorts. Healthc (Amst). 2017;5(1–2):62–67.CrossRefGoogle Scholar
  8. 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. 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.
  10. 10.
    Blumenthal D, Abrams MK. Tailoring complex care management for high-need, high-cost patients. JAMA. 2016;316(16):1657–1658.CrossRefGoogle Scholar
  11. 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. 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. 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.
  14. 14.
    Lynn J, Straube BM, Bell KM, Jencks SF, Kambic RT. Using population segmentation to provide better health care for all: the “Bridges to Health” model. Milbank Q. 2007;85(2):185–208; discussion 209-112.CrossRefGoogle Scholar
  15. 15.
    Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139.CrossRefGoogle Scholar
  16. 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. 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.
  18. 18.
    Choudhry NK, Shrank WH, Levin RL, et al. Measuring concurrent adherence to multiple related medications. Am J Manag Care. 2009;15(7):457–464.PubMedPubMedCentralGoogle Scholar
  19. 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. 20.
    New York University. Faculty & Research. 2017; https://wagner.nyu.edu/faculty/billings/nyued-background. Accessed October 19, 2018.
  21. 21.
    Joynt KE, Gawande AA, Orav EJ, Jha AK. Contribution of preventable acute care spending to total spending for high-cost Medicare patients. JAMA. 2013;309(24):2572–2578.CrossRefGoogle Scholar
  22. 22.
    Figueroa JF, Joynt Maddox KE, Beaulieu N, Wild RC, Jha AK. Concentration of Potentially Preventable Spending Among High-Cost Medicare Subpopulations: An Observational Study. Ann Intern Med. 2017;167(10):706–713.CrossRefGoogle Scholar
  23. 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. 24.
    Van Der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res. 2014;15(1):3221–3245.Google Scholar
  25. 25.
    Maaten Lvd, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579–2605.Google Scholar
  26. 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
  27. 27.
    Ankerst M, Breunig MM, Kriegel H-P, #246, Sander r. OPTICS: ordering points to identify the clustering structure. SIGMOD Rec. 1999;28(2):49–60.CrossRefGoogle Scholar
  28. 28.
    Congressional Budget Office. High-Cost Medicare Beneficiaries. Washington, DC: Congressional Budget Office;2005.Google Scholar
  29. 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.
  30. 30.
    Powers BW, Chaguturu SK, Ferris TG. Optimizing high-risk care management. JAMA. 2015;313(8):795–796.CrossRefGoogle Scholar
  31. 31.
    Figueroa JF, Jha AK. Approach for achieving effective care for high-need patients. JAMA Intern Med. 2018.Google Scholar
  32. 32.
    Newcomer SR, Steiner JF, Bayliss EA. Identifying subgroups of complex patients with cluster analysis. Am J Manag Care. 2011;17(8):e324–332.PubMedGoogle Scholar
  33. 33.
    Lee NS, Whitman N, Vakharia N, Ph DG, Rothberg MB. High-cost patients: hot-spotters don’t explain the half of it. J Gen Intern Med. 2017;32(1):28–34.CrossRefGoogle Scholar
  34. 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.

Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • Brian W. Powers
    • 1
    • 2
    • 3
    • 4
  • Jiali Yan
    • 5
  • Jingsan Zhu
    • 6
  • Kristin A. Linn
    • 7
  • Sachin H. Jain
    • 3
  • Jennifer L. Kowalski
    • 8
  • Amol S. Navathe
    • 6
    • 9
  1. 1.Department of MedicineBrigham and Women’s HospitalBostonUSA
  2. 2.Department of Population MedicineHarvard Medical School/Harvard Pilgrim Health Care InstituteBostonUSA
  3. 3.CareMore Health SystemCerritosUSA
  4. 4.Atrius HealthBostonUSA
  5. 5.Department of MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  6. 6.Department of Medical Ethics and Health Policy University of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  7. 7.Department of Biostatistics, Epidemiology and InformaticsUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  8. 8.Anthem Public Policy InstituteWashingtonUSA
  9. 9.Corporal Michael J. Cresencz VA Medical CenterPhiladelphiaUSA

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