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Implementing a hybrid approach to select patients for care management: variations across practices
Christine Vogeli1, Jenna Spirt, Richard Brand
1Mongan Institute for Health Policy, Massachusetts General Hospital, 50 Staniford St, 9th Fl, Boston, MA 02114.
Objectives:
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.
Study Design:
Retrospective cohort study.
Methods:
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.
Results:
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.
Conclusions:
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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