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.

Limitations

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.

CONCLUSIONS
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.

Acknowledgments

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: cvogeli@mgh.harvard.edu.
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