The application of Johns Hopkins Adjusted Clinical Group Case-mix System in AFMS

Published: August 2, 2011
Category: Bibliography > Reports
Authors: Chao S
Countries: United States
Language: null
Types: Care Management
Settings: Government

In: Cato W, Roberts WC, ed. Proceedings of the 2011 AFMS Medical Research Symposium, Volume 4. Healthcare Informatics Track. August 2-4, 2011, National Harbor, MD, USA. Washington, DC, USA: US Air Force Medical Service:52-60.

US Air Force Medical Service, Washington, DC, USA

The Adjusted Clinical Group (ACG) Case-Mix System is a diagnosis- and medication-based risk-adjustment tool that has been adopted by more than 200 healthcare organizations in US and abroad and validated extensively in commercial and research settings over 15 years, but has only recently been implemented in AFMS. ACG offers a comprehensive family of measurements designed to help explain and predict how healthcare resources are delivered and consumed. Through its unique ‘person-focused’ approach, ACG captures the multidimensional nature of individual’s health and morbidity burden of patient population, and it also can be used to identify and predict health care resource needs, enhance equitable distribution of limited resources, improve accuracy in provider profiling, streamline healthcare delivery, evaluate population health risk, and provide actionable information. FY09-FY10 M2 data were used to demonstrate capabilities of ACG and to validate its predictive models in AFMS-enrolled population. Sensitivity of predictive models for high total healthcare cost, high pharmacy cost and hospitalization were 39%, 69% and 28%, respectively, whereas the corresponding specificity were 97%, 98% and 96%, respectively. The performance of ACG in AFMS was comparable to that found in commercial HMO populations where the sensitivity for high total healthcare costs and hospitalization were 37% and 33%, respectively. This suggests that ACG can be applied to AFMS even though it was originally developed using commercial HMO and state Medicaid populations. AFMS leadership should take advantage of the readily available measures generated by ACG and, with these unparalleled and comprehensive measures, in turn develop effective population health policies.

Outcome Measures,Process Measures,Predictive Risk Modeling,Overall Disease Burden,United States

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