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Risk-Stratification Methods for Identifying Patients for Care Coordination
Lindsey R. Haas, MPH; Paul Y. Takahashi, MD; Nilay D. Shah, PhD; Robert J. Stroebel, MD; Matthew E. Bernard, MD; Dawn M. Finnie, MPA; and James M. Naessens, ScD
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Risk-Stratification Methods for Identifying Patients for Care Coordination

Lindsey R. Haas, MPH; Paul Y. Takahashi, MD; Nilay D. Shah, PhD; Robert J. Stroebel, MD; Matthew E. Bernard, MD; Dawn M. Finnie, MPA; and James M. Naessens, ScD
Identifying which patients are likely to benefit from care coordination is important. We evaluated the performance of 6 risk-screening instruments in predicting healthcare utilization.
Each of these risk-stratification methods has potential advantages and disadvantages. There is considerable overlap across the risk-stratification methods because each system relies on the number of comorbid illnesses that a patient has. In fact, at least 40% of our clinically identified patients (based on combination of MN Tiering and ERA) were among the highest risk patients using any of the risk-stratification methods. However, none of the models explained more than half of the variability in outcomes—a clear limitation suggesting that other factors could enable better identification of patients who need care coordination in order to reduce the need for hospitalization and other high-cost healthcare services. Future research is needed to determine whether incorporating additional factors (eg, living situation, high-risk medications, lifestyle, patient preferences) would improve prediction of high-risk individuals or might suggest that certain risks suggest certain interventions.


We found good concordance among all 6 different risk screening instruments for predicting hospitalization. Use of any of the tools may provide some support for providers and health plans who undertake case management. Focusing care coordination efforts within the medical home on patients likely to benefit most requires appropriate identification of the highest risk, highest utilizing patients. Use of risk screening tools is a promising method.

Author Affiliations: From Department of Health Sciences (LRH, NDS, DMF, JMN), Division of Health Care Policy and Research, Department of Internal Medicine (RJS, PYT), Division of Primary Care Internal Medicine, Department of Family Medicine (MEB), Mayo Clinic, Rochester, MN
Funding Source: None.

Author Disclosures: The authors (LRS, PYT, NDS, RJS, MEB, DMF, JMN) 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 (LRS, PYT, NDS, RJS, MEB, DMF, JMN); acquisition of data (LRS, JMN); analysis and interpretation of data (LRS, NDS, RJS, MEB, DMF, JMN); drafting of the manuscript (LRS, PYT, MEB, JMN); critical revision of the manuscript for important intellectual content (LRS, PYT, NDS, RJS, MEB, JMN); statistical analysis (LRS, JMN); obtaining funding (JMN); and administrative, technical, or logistic support (DMF).

Address correspondence to: Lindsey R. Haas, MPH, Health Care Policy and Research, Mayo Clinic, 200 First St Southwest, Rochester, MN 55905. E-mail:
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