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Am J Manag Care. 2016 May;22(5):358-65.

Implementing a hybrid approach to select patients for care management: variations across practices.

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Mongan Institute for Health Policy, Massachusetts General Hospital, 50 Staniford St, 9th Fl, Boston, MA 02114. E-mail:



Appropriate selection of patients is key to the success of care management programs (CMPs). Hybrid patient selection approaches, in which large data assets are culled to develop a list of patients for more targeted clinical review, are increasingly common. We sought to describe the patient and practice characteristics associated with high-risk patient identification and selection for a CMP during clinical review, and to explore variation across primary care practices.


Retrospective cohort study.


Standardized estimates of Medicare beneficiaries identified as high risk for poor outcomes and high medical expense, and appropriate for a CMP within a large Pioneer Accountable Care Organization, were developed using mixed effects logistic models. Study subjects were 2685 Medicare beneficiaries aged over 18 (includes individuals eligible for Medicare due to a disability) aligned to 35 primary care practices in 2013.


Independent predictors of patient identification as high risk include older age; higher risk score; recent increases in medical conditions; higher numbers of medical hospitalizations, skilled nursing facility days, and primary care physician visits; and shorter relationships with the primary care physician. Older age, and lower income, but no prior hospice use were independently associated with patient selection for a CMP among the subset of patients identified as being high risk. Adjusted predicted percents of high-risk patients varied significantly across practices overall and for 5 of the 6 patient characteristics that were independently associated with identification as high risk.


Inconsistency in high-risk patient identification and selection for a CMP may reflect differences in practice resources, but also highlight the need for continual training and feedback in order to protect against unintentional biases.

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