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Comparative Effectiveness of a Complex Care Program for High-Cost/High-Need Patients: a Retrospective Cohort Study



High-cost/high-need (HCHN) adults and the healthcare systems that provide their care may benefit from a new patient-centered model of care involving a dedicated physician and nurse team who coordinate both clinical and social services for a small patient panel.


Evaluate the impact of a Complex Care Program (CCP) on likelihood of patient survival and hospital admission in 180 days following empanelment to the CCP.


Retrospective cohort study using a quasi-experimental design with CCP patients propensity score matched to a concurrent control group of eligible but unempaneled patients.


Kaiser Permanente Mid-Atlantic States (KPMAS) during 2017–2018.


Nine hundred twenty-nine CCP patients empaneled January 2017–June 2018, 929 matched control patients for the same period.


The KPMAS CCP is a new program consisting of 8 teams each staffed by a physician and nurse who coordinate care across a continuum of specialty care, tertiary care, and community services for a panel of 200 patients with advanced clinical disease and recent hospitalizations.

Main Outcomes

Time to death and time to first hospital admission in the 180 days following empanelment or eligibility.


Compared to matched control patients, CCP patients had prolonged time to death (hazard ratio [HR]: 0.577, 95% CI: 0.474, 0.704), and CCP decedents had longer survival (median days 69.5 vs. 53.0, p=0.03). CCP patients had similar time to hospital admission (HR: 1.081, 95% CI: 0.930, 1.258), with similar results when adjusting for competing risk of death (HR: 1.062, 95% CI: 0.914, 1.084).


Non-randomized intervention; single healthcare system; patient eligibility limited to specific conditions.


The KPMAS CCP was associated with significantly reduced short-term mortality risk for eligible patients who volunteered to participate in this intervention.

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  1. 1.

    Cohen SB. The Concentration and Persistence in the Level of Health Expenditures over Time: Estimates for the U.S. Population, 2011-2012. Statistical Brief #449 (Medical Expenditure Panel Survey (US)) [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US).

  2. 2.

    Blumenthal D, Abrams MK. Tailoring complex care management for high-need, high-cost patients. JAMA. 2016;316(16):1657-1658.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for high-need, high-cost patients: An Urgent priority. New Engl J Med 2016;375(10):909-911.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: A 2012 Update. Prev Chronic Dis 2014;11.

  5. 5.

    Cross DA, Cohen GR, Lemak CH, Adler-Milstein J. Outcomes for high-needs patients: Practices with a higher proportion of these patients have an edge. Health Aff 2017;36(3):476-484.

    Google Scholar 

  6. 6.

    Jackson GL, Powers BJ, Chatterjee R, et al. Improving patient care. The Patient centered medical home: A Systematic review. Ann Intern Med 2013;158(3):169-178.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Shortell SM, Poon BY, Ramsay PP, et al. A Multilevel analysis of patient engagement and patient-reported outcomes in primary care practices of accountable care organizations. J Gen Intern Med 2017;32(6):640-647.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Kaufman BG, Spivack BS, Stearns SC, Song PH, O’Brien EC. Impact of accountable care organizations on utilization, care, and outcomes: A Systematic review. Med Care Res Rev. Med Care Res Rev 2019;76(3):255-290.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Lewis VA, Colla CH, Schoenherr KE, Shortell SM, Fisher ES. Innovation in the safety net: Integrating community health centers through accountable care. J Gen Intern Med 2014;29(11):1484-1490.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Korenstein D, Duan K, Diaz MJ, Ahn R, Keyhani S. Do health care delivery system reforms improve value? The Jury is still out. Med Care 2016;54(1):55-66.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hu R, Shi L, Sripipatana A, et al. The Association of patient-centered medical home designation with quality of care of HRSA-funded health centers: A Longitudinal analysis of 2012–2015. Med Care 2018;56(2):130-138.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Colla CH, Fisher ES. Moving forward with accountable care organizations: Some answers, more questions. JAMA Intern Med 2017;177(4):527-528.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Shortell SM, Sehgal NJ, Bibi S, et al. An Early assessment of accountable care organizations’ efforts to engage patients and their families. Med Care Res Rev 2015;72(5):580-564.

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Rosland A-M, Wong E, Maciejewski M, et al. Patient-centered medical home implementation and improved chronic disease quality: A Longitudinal observational study. Health Serv Res 2018;53(4):2503-2522.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Cuellar A, Helmchen LA, Gimm G, et al. The CareFirst patient-centered medical home program: Cost and utilization effects in its first three years. J Gen Intern Med 2016;31(11):1382-1388.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Hacker K, Walker DK. Achieving population health in accountable care organizations. Am J Public Health 2013;103(7):1163-1167.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 Randomized trials. JAMA. 2009;301(6):603-618.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Anderson GF, Ballreich J, Bleich S, et al. Attributes common to programs that successfully treat high-need, high-cost individuals. Am J Manag Care 2015;21(11):e597-600.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Brown RS, Peikes D, Peterson G, Schore J, Razafindrakoto CM. Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients. Health Aff 2012;31(6):1156-1166.

    Google Scholar 

  20. 20.

    Edwards ST, Peterson K, Chan B, Anderson J, Helfand M. Effectiveness of intensive primary care interventions: A Systematic review. J Gen Intern Med 2017;32(12):1377-1386.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Hochman M, Asch SM. Disruptive models in primary care: caring for high-needs, high-cost populations. J Gen Intern Med 2017;32(4):392-397.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Hong CS, Siegel AL, Ferris TG. Caring for high-need, high-cost patients: What makes for a successful care management program? Issue Brief (Commonw Fund) 2014;19:1-19.

    Google Scholar 

  23. 23.

    Meltzer DO, Ruhnke GW. 2014. Redesigning care for patients at increased hospitalization risk: The Comprehensive Care Physician Model. Health Aff 2014;33(5):770-777.

    Google Scholar 

  24. 24.

    United States Census 2000. Summary File 3.

  25. 25.

    Social Security Administration. SSA’s Death Information.

  26. 26.

    Charlson ME, Pompei P, Ales KL, MacKenzie CR. A New method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis, 1987;40(5):373-383.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45(6):613-619.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 1993;46(10):1075-1079.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994;47(11):1245-1251.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol 2000;53(12):1258-1267.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Austin SR, Wong YN, Uzzo RG, Beck JR, Egleston BL. Why summary comorbidity measures such as the Charlson comorbidity index and Elixhauser score work. Med Care 2015;53(9):e65–72.

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Sundararajan V, Henderson T, Perry C, et al. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004;57(12):1288-1294.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Blagev DP, Collingridge DS, Rea S, et al. The Laboratory-Based Intermountain Validated Exacerbation (LIVE) score identifies chronic obstructive pulmonary disease patients at high mortality risk. Front Med (Lausanne). 2018;5:173.

    Google Scholar 

  34. 34.

    Roblin DW. Validation of a neighborhood SES index in a managed care organization. Med Care 2013;51(1):e1-8.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    D’Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998;17(19):2265-2281.

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med 1997;127(8 pt 2):757-763.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Abadie A, Diamond A, Hainmueller J. Synthetic control methods for comparative case studies: Estimating the effect of California’s Tobacco Control Program. J Am Stat Assoc 2010;105(490):493-505.

    CAS  Google Scholar 

  38. 38.

    Dehejia RH, Wahba S. Propensity score-matching methods for nonexperimental causal studies. Rev Econ Stat 2002;84(1):151-161.

    Google Scholar 

  39. 39.

    DuGoff EH, Schuler M, Stuart EA. Generalizing observational study results: Applying propensity score methods to complex surveys. Health Serv Res 2014;49(1):284-303.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. New York: Wiley, 1980.

    Google Scholar 

  41. 41.

    Berry SD, Ngo L, Samelson EJ, Kiel DP. Competing risk of death: An Important consideration in studies of older adults. J Am Geriatr Soc 2010;58(4):783-787.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics. 1995;51(2):524-532.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Prentice RL, Kalbfleisch JD, Peterson AV Jr, et al. The Analysis of failure times in the presence of competing risks. Biometrics. 1978:34(4):541-554.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    So Y, Lin G, Johnston G. Using the PHREG procedure to analyze competing-risks data.

  45. 45.

    Garrido MM, Kelley AS, Paris J, et al. Methods for constructing and assessing propensity scores. Health Serv Res 2014;49(5):1701-1720.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Stokes J, Panagioti M, Alam R, et al. Effectiveness of case management for 'at risk' patients in primary care: A Systematic review and meta-analysis. PLoS One 2015;10(7):e0132340.

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Komaromy M, Bartlett J, Gonzales-van Horn SR, et al. A Novel Intervention for High-Need, High-Cost Medicaid Patients: A Study of ECHO Care. J Gen Intern Med 2020;35(1):21-27.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Komaromy M, Bartlett J, Zurawski A, etc. ECHO Care: Providing Multidisciplinary Specialty Expertise to Support the Care of Complex Patients. J Gen Intern Med 2020;35(1):326-330

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ekdahl AW, Wirehn AB, Alwin J, et al. Costs and effects of an ambulatory geriatric unit (the AGe-FIT Study): A Randomized controlled trial. J Am Med Dir Assoc 2015;16(6):497-503.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Saltvedt I, Opdahl Mo E-S, Fayers P, Kaasa S, Sletvold O. Reduced mortality in treating acutely sick, frail older patients in a geriatric evaluation and management unit: A Prospective randomized trial. J Am Geriatr Soc 2002;50(5):792-798.

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Finkelstein A, Zhou A, Taubman S, Doyle J. Health care hotspotting - A Randomized, controlled trial. N Engl J Med 2020;382(2):152-162.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Davis AC, Shen E, Shah NR, Glenn BA, Ponce N, Tleleca D, Gould MK, Needleman J. Segmentation of high-cost adults in an integrated healthcare system based on empirical clustering of acute and chronic conditions. J Gen Intern Med 2018;33(12):2171-2179.

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Zulman DM, Pal Chee C, Ezeji-Okoye SC, et al. Effect of an intensive outpatient program to augment primary care for high-need Veterans Affairs patients: A Randomized clinical trial. JAMA Intern Med 2017;177(2):166-175.

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Fireman B, Bartlett J, Selby J. Can disease management reduce health care costs by improving quality? Health Aff 2004;23(6):63-73.

    Google Scholar 

  55. 55.

    Goetzel RZ, Ozminkowski RJ, Villagra VG, Duffy J. Return on investment in disease management: A review. Health Care Finance Rev 2005;26(4):1-19.

    Google Scholar 

  56. 56.

    Leatherman S, Berwick D, Iles D, et al. The Business case for quality: Case studies and an analysis. Health Aff 2003;22(2):17-30.

    Google Scholar 

  57. 57.

    Luck J, Parkerton P, Hagigi F. What is the business case for improving care for patients with complex conditions? J Gen Intern Med 2007;22(suppl 3):396-402.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Short A, Mays G, Mittler J. Disease management: A Leap of faith to lower-cost, higher quality health care. Issue Brief Cent Stud Health Syst Change. 2003;69:1-4.

  59. 59.

    Vogeli C, Shields AE, Lee TA, et al. Multiple chronic conditions: Prevalence, health consequences and implications for quality, care management, and costs. J Gen Intern Med 2007;22(suppl 3):391-395.

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Cohen JT, Neumann PJ, Weinstein MC. Does preventive care save money? Health economics and the presidential candidates. New Engl J Med 2008;358(7):661-663.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Neumann PJ, Cohen JT. Cost savings and cost-effectiveness of clinical preventive care. Synth Proj Res Synth Rep 2009;(18).

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The authors would like to acknowledge the 2017–2018 KPMAS Complex Care Program physicians (Drs. F. Abdulsalam, S. Flagg, C. Freeman, F. Freisinger, L. Luo, K. Nagi, S. Nokuri, J. Swett, R. Yelamanchi), nurses (Ms. J. Garza, R. Leonard, E. McKinney), and administrative team (Ms. C. Campbell, Mr. C. Ma) who provided helpful suggestions during presentations of interim results at steering committee meetings.


The KPMAS Community Benefit Fund provided funds for the evaluation. It had no role in the design of the study; the collection, analysis, and interpretation of the data; and the decision to approve publication of the finished manuscript.

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Corresponding author

Correspondence to Douglas W. Roblin PhD.

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Conflict of Interest

The authors have no financial conflicts of interest to disclose. Drs. McCarthy, Mendiratta, and Roblin are affiliated with the medical group which designed and implemented the intervention.

Human Subjects

The evaluation protocol has been reviewed, approved, and monitored by the Institutional Review Board of Kaiser Permanente Mid-Atlantic States (IRB Protocol number MA-16-138).

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Key Points

Question: Does an intervention focused on the secondary and tertiary care coordination and palliative care needs of high-cost/high-need adults modify their short-term risk of repeat hospitalization and mortality?

Findings: The intervention implemented at Kaiser Permanente Mid-Atlantic States significantly reduced the 180-day mortality hazard ratio [HR] by 42.3% but did not significantly change the 180-day medical/surgical admission HR—compared to a matched patient cohort.

Meaning: An intervention designed to address the specific care requirements of high-cost/high-need adults achieved significantly reduced short-term mortality HR.

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Roblin, D.W., Segel, J.E., McCarthy, R.J. et al. Comparative Effectiveness of a Complex Care Program for High-Cost/High-Need Patients: a Retrospective Cohort Study. J GEN INTERN MED 36, 2021–2029 (2021).

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