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Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims.

Published: August 1, 2017
Category: Bibliography
Authors: Chang HY, Chi W, Gallagher JM, Kharrazi H, Knudson SM, Richards TM, Weiner JP
Country: United States
Type: Care Management
Setting: Hospital

Department of Health Policy and Management, Johns Hopkins School of Public Health, Center for Population Health Information Technology, Baltimore, MD HealthPartners, Health Informatics Division, Bloomington, MN.

BACKGROUND: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification.

METHODS: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined.

RESULTS: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims.

DISCUSSION: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR’s diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

Predictive Risk Modeling, Cost Burden Evaluation,Payment,United States,Adolescent,Adult,Gender,Hospital Administration,Age,Risk Assessment/methods

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