Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices | Page 3

Hybrid approaches allow for clinician input into case finding for care management, but training and monitoring is required to protect against unintentional biases.
Published Online: May 17, 2016
Christine Vogeli, PhD; Jenna Spirt, MPH; Richard Brand, PhD; John Hsu, MD, MPH; Namita Mohta, MD; Clemens Hong, MD, MPH; Eric Weil, MD; and Timothy G. Ferris, MD, MPH
We found considerable variation in the overall adjusted proportions of patients identified as high risk across practices, as well as significant variations in practice-level identification rates for specific cohorts of patients, suggesting that practices may not use the same criteria to identify high-risk patients. Although practices were provided with central guidance on how to perform the clinical review, patient-by-patient decisions are left to the practices.  Some of this diversity in identification of high-risk patients may appropriately reflect unmeasured differences in practice resources and access to specialty care and services, such as behavioral health specialists and addiction counselors, for their patient population. Differences could also reflect variations in program implementation, such as nurse skill levels or comfort with different patient populations. Care management, by nature, is a diverse intervention for individuals with complex healthcare needs.5 The multifaceted nature of care management not only complicates the identification of patients, but may bias selection toward patients whose needs can be met by the skills of the care manager available to the practice. Training for care managers and feedback to practices regarding the benefits and limitations of CMP may help minimize unintentional disparities in patient selection.


We excluded 43 PCPs (763 patients) that did not appear to differentially classify Medicare ACO patients from this analysis; it is unclear whether these practices made appropriate decisions or elected to not fully participate in the review process. Training and feedback may be particularly important for practices without differential review decisions (eg, those practices where all or no patients were identified as high risk). Alternatively, lower-intensity versions of clinician review may be necessary to ensure that all practices are able to participate in a meaningful way. In addition, this study was conducted within a single large provider network, and therefore may not be generalizable to other systems.

Hybrid approaches that use quantitative methods to prospectively identify a list of patients for subsequent clinical review are commonly used to identify and select patients for care management.9 Although clinical review may enhance PCP buy-in and allow for the consideration of patient psychosocial factors and appropriateness for care management, it may also introduce biases in patient selection. This study found evidence of significant variation across practices, both in the identification and the selection of high-risk patients for care management. However, it is not yet clear if this variation reflects differences in patient need and existing clinical services or biases related to training and feedback. CMPs using hybrid approaches need to provide adequate training and feedback to primary care clinicians and practices; this additional support could ensure that unintentional biases do not impact decision making and that selection decisions evolve to target patients at the highest risk for future poor outcomes and high medical expense who can benefit most from care management. Finally, additional on-going research is necessary to identify the characteristics of patients most successful in primary care–based care management. The findings from this work will inform the design of care management interventions, particularly in relation to the initial selection of patients for CMPs.


Partners HealthCare Population Health Management funded this study but was not involved in the analysis and interpretation of the data. The authors would like to acknowledge their operational colleagues for their support with this research.

Author Affiliations: Mongan Institute for Health Policy (CV, JS, JH) and Division of General Medicine (EW), Massachusetts General Hospital, Boston, MA; Population Health Management, Partners HealthCare (CV, EW, NM, TGF), Boston, MA; Department of Medicine, Harvard Medical School (CV, JH, TGF), Boston, MA; Department of Epidemiology and Biostatistics, University of California at San Francisco (RB), San Francisco, CA; Los Angeles County Department of Health Services (CH), Los Angeles, CA.

Source of Funding: This study is based on a quality improvement investigation funded by Partners HealthCare.

Author Disclosures: Dr Brand was a biostatistical consultant for the team that prepared the manuscript. Drs Vogeli, Hsu, Weil, Mohta, and Ferris and Ms Sprit are employees of Partners HealthCare or one of its affiliated hospitals. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (CV, NM, CH, EW, TGF); acquisition of data (CV, JS, EW); analysis and interpretation of data (CV, JS, RB, JH, NM, CH, EW); drafting of the manuscript (CV); critical revision of the manuscript for important intellectual content (JS, RB, JH, NM, CH, EW, TGF); statistical analysis (CV, JS, RB); obtaining funding (CV); administrative, technical, or logistic support (NM); and supervision (CV, JH, TGF).

Address correspondence to: Christine Vogeli, PhD, Mongan Institute for Health Policy, Massachusetts General Hospital, 50 Staniford St, 9th Fl, Boston, MA 02114. E-mail:

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]. Agency for Healthcare Research and Quality website. Published September 2014. Accessed June 1, 2015.

2. Naessens JM, Stroebel RJ, Finnie DM, et al. Effect of multiple chronic conditions among working-age adults. Am J Manag Care. 2011;17(2):118-122.

3. 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.

4. Chen A, Brown R, Archibald N, Aliotta S, Fox PD; Mathematica Policy Research, Inc. Best practices in coordinated care. CMS website. Published March 22 2000. Accessed June 1, 2015

5. Bodenheimer T, Berry-Millett R. Care management of patients with complex health care needs. The Robert Wood Johnson Foundation website. Published December 2009. Accessed June 1, 2015.

6. Mullahy CM. Science versus art in case management. Case Manager. 2003;14(1):4.

7. 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. doi: 10.1001/jama.2009.126.

8. Wharam JF, Weiner JP. The promise and peril of healthcare forecasting. Am J Manag Care. 2012;18(3):e82-e85.

9. Hong CS, Hwang AS, Ferris TG. Finding a match: how successful complex care programs identify patients. California HealthCare Foundation website. Published March 2015. Accessed June 1, 2015.

10. Winkleman R, Mehmud S. A comparative analysis of claims-based tools for health risk assessment. Society of Actuaries website. Published April 20, 2007. Accessed June 1, 2015.

11. Bernstein RH. New arrows in the quiver for targeting care management: high-risk versus high-opportunity case identification. J Ambul Care Manage. 2007;30(1):39-51.

12. Cousins MS, Shickle LM, Bander JA. An introduction to predictive modeling for disease management risk stratification. Dis Manag. 2002;5(3):157-167.

13. Confidentiality of alcohol and drug abuse patient records (CFR Title 42: part 2). Government Publishing Office website.;node=42%3A1. Accessed June 1, 2015.

14. Shadmi E, Freund T. Targeting patients for multimorbid care management interventions: the case for equity in high-risk patient identification. Int J Equity Health. 2013;12(1):70. doi: 10.1186/1475-9276-12-70.

15. Berkowitz SA, Meigs JB, DeWalt D, et al. Material need insecurities, control of diabetes mellitus, and use of health care resources: results of the Measuring Economic Insecurity in Diabetes study. JAMA Intern Med. 2015;175(2):257-265. doi: 10.1001/jamainternmed.2014.6888.

16. Freund T, Gondan M, Rochon J, et al. Comparison of physician referral and insurance claims data-based risk prediction as approaches to identify patients for care management in primary care: an observational study. BMC Fam Pract. 2013;14:157. doi: 10.1186/1471-2296-14-157.

17. Lewis GH. “Impactibility models”: identifying the subgroup of high-risk patients most amenable to hospital-avoidance programs. Milbank Q. 2010;88(2):240-255. doi: 10.1111/j.1468-0009.2010.00597.x.

18. Haime V, Hong C, Mandel L, et al. Clinician considerations when selecting high-risk patients for care management. Am J Manag Care. 2015;21(10):e576-e582.

19. Grant RW, Ashburner JM, Hong CS, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician’s perspective: a cohort study. Ann Intern Med. 2011;155(12):797-804. doi: 10.7326/0003-4819-155-12-201112200-00001.

20. McCall N, Cromwell J, Urato C. Evaluation of Medicare Care Management for High Cost Beneficiaries (CMHCB) Demonstration: Massachusetts General Hospital and Massachusetts General Physicians Organization (MGH). Massachusetts General Hospital website. Published September 2010. Accessed April 2016.

21. 2012 American Community Survey: 5-year estimates. US Census Bureau website. Accessed June 1, 2015.

22. Pope GC, Kautter J, Ingber MJ, Freeman S, Sekar R, Newhart C. Evaluation of the CMS-HCC Risk Adjustment Model. CMS website. Published March 2011. Accessed June 1, 2015.

23. Starfield B. Primary Care: Concept, Evaluation, and Policy. New York, NY: Oxford University Press; 1992.

24. Freund T, Wensing M, Geissler S, et al. Primary care physicians’ experiences with case finding for practice-based care management. Am J Manag Care. 2012;18(4):e155-e161. 
Adult ADHD Compendium
COPD Compendium
Dermatology Compendium
Diabetes Compendium
Hematology Compendium
Immuno-oncology Compendium
Lipids Compendium
MACRA Compendium
Neutropenia Compendium
Oncology Compendium
Pain Compendium
Reimbursement Compendium
Rheumatoid Arthritis Compendium
Know Your News
HF Compendium
Managed Care PODCAST