Abstract
Although national measures of the quality of diabetes care delivery demonstrate improvement, progress has been slow. In 2008, the Minnesota legislature endorsed the patient-centered medical home (PCMH) as the preferred model for primary care redesign. In this work, we investigate the effect of PCMH-related clinic redesign and resources on diabetes outcomes from 2008 to 2012 among Minnesota clinics certified as PCMHs by 2011 by using a Bayesian framework for a continuous difference-in-differences model. Data from the Physician Practice Connections-Research Survey were used to assess a clinic’s maturity in primary care transformation, and diabetes outcomes were obtained from the MN Community Measurement program. These data have several characteristics that must be carefully considered from a modeling perspective, including the inability to match patients over time, the potential for dynamic confounding, and the hierarchical structure of clinics. An ad-hoc analysis suggests a significant correlation between PCMH-related clinic redesign and resources on diabetes outcomes; however, this effect is not detected after properly accounting for different sources of variability and confounding. Supplementary materials for this article are available online.
Highlights
Over the last 10 years, national “quality of care” measures have demonstrated that important gaps exist in the delivery of optimal diabetes care (HEDIS 2018)
Our goal is to investigate the effect of patient-centered medical home (PCMH)-associated clinical services and resources on diabetes outcomes among patients at Minnesota clinics certified as PCMHs by 2011
We offer a Bayesian approach for the continuous difference-in-difference (CDiD) model, which provides a natural framework to accommodate the hierarchical data structure and other sources of variation in the present application to PCMH effects on diabetes outcomes
Summary
Over the last 10 years, national “quality of care” measures have demonstrated that important gaps exist in the delivery of optimal diabetes care (HEDIS 2018). The primary predictor is a continuous measure of clinic services rather than a binary measure of treatment status, and so we instead focus on assessing the association between change in outcome and change in predictors We call this a continuous difference-in-difference (CDiD) model; as an example, Card (1992) considered the association between the change in minimum wage and the change in employment across several states. We offer a Bayesian approach for the CDiD model, which provides a natural framework to accommodate the hierarchical data structure and other sources of variation in the present application to PCMH effects on diabetes outcomes.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
