AI Article Synopsis
- The study compares early versus later adopters of the accountable care organization (ACO) model using a taxonomy that categorizes ACOs into larger integrated systems, smaller physician-led, and hybrid types.
- The data comes from the National Survey of ACOs, analyzing two waves of data with a cluster analysis approach to identify differences and patterns.
- Findings suggest that later adopter ACOs have a wider variety of provider types and a higher proportion claiming to be integrated systems, reinforcing a stable three-cluster taxonomy that can aid researchers and policymakers in evaluating ACOs.
Article Abstract
Objective: To compare early and later adopters of the accountable care organization (ACO) model, using the taxonomy of larger, integrated system; smaller, physician-led; and hybrid ACOs.
Data Sources: The National Survey of ACOs, Waves 1 and 2.
Study Design: Cluster analysis using the two-step clustering approach, validated using discriminant analysis. Wave 2 data analyzed separately to assess differences from Wave 1 and then data pooled across waves.
Findings: Compared to early ACOs, later adopter ACOs included a greater breadth of provider group types and a greater proportion self-reported as integrated delivery systems. When data from the two time periods were combined, a three-cluster solution similar to the original cluster solution emerged. Of the 251 ACOs, 31.1 percent were larger, integrated system ACOs; 45.0 percent were smaller physician-led ACOs; and 23.9 percent were hybrid ACOs-compared to 40.1 percent, 34.0 percent, and 25.9 percent from Wave 1 clusters, respectively.
Conclusions: While there are some differences between ACOs formed prior to August 2012 and those formed in the following year, the three-cluster taxonomy appears to best describe the types of ACOs in existence as of July 2013. The updated taxonomy can be used by researchers, policy makers, and health care organizations to support evaluation and continued development of ACOs.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134136 | PMC |
| http://dx.doi.org/10.1111/1475-6773.12473 | DOI Listing |
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