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Case study

Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation

Authors:

Tracy L. Johnson ,

Denver Health
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Daniel Brewer,

Denver Health
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Raymond Estacio,

Denver Health
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Tara Vlasimsky,

Denver Health
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Michael J. Durfee,

Denver Health
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Kathy R. Thompson,

Denver Health
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Rachel M. Everhart,

Denver Health
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Deborath J. Rinehart,

Denver Health
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Holly Batal

Denver Health
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Abstract

Context: The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a “population health” approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner’s Chronic Care Model (CCM) to achieve the “Triple Aim”: improved health for populations, care to individuals, and lower per capita costs.

Case description: This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective.

Findings: In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables.

Conclusions: We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.

How to Cite: Johnson TL, Brewer D, Estacio R, Vlasimsky T, Durfee MJ, Thompson KR, et al.. Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2015;3(1):14. DOI: http://doi.org/10.13063/2327-9214.1181
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Published on 30 Jul 2015.
Peer Reviewed

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