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A Bayesian Difference-in-Difference Framework for the Impact of Primary Care Redesign on Diabetes Outcomes
James Normington1, Eric Lock1, Caroline Carlin2
1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN.
Statistics and Public Policy (Philadelphia, Pa.)
|August 23, 2019
Summary
Patient-centered medical home (PCMH) redesign shows no significant impact on diabetes care quality in Minnesota clinics. Rigorous analysis revealed that initial positive correlations were not sustained after accounting for confounding factors.
Area of Science:
- Health Services Research
- Primary Care
- Diabetes Management
Background:
- National diabetes care quality shows slow improvement.
- Minnesota endorsed the patient-centered medical home (PCMH) as a primary care redesign model in 2008.
- PCMH models aim to improve healthcare delivery and patient outcomes.
Purpose of the Study:
- To investigate the effect of PCMH-related clinic redesign and resources on diabetes outcomes.
- To analyze data from Minnesota clinics certified as PCMHs between 2008 and 2012.
- To address data complexities including patient non-matching, dynamic confounding, and clinic hierarchy.
Main Methods:
- Utilized a Bayesian framework for a continuous difference-in-differences model.
- Assessed clinic transformation maturity using the Physician Practice Connections-Research Survey.
- Obtained diabetes outcomes from the MN Community Measurement (MNCM) program.
Main Results:
- An ad-hoc analysis suggested a correlation between PCMH redesign/resources and diabetes outcomes.
- This correlation was not detected after accounting for variability and confounding.
- The study highlights challenges in measuring the impact of healthcare redesign initiatives.
Conclusions:
- PCMH-related clinic redesign and resources did not demonstrate a statistically significant effect on diabetes outcomes in Minnesota.
- Accounting for complex data structures and confounding factors is crucial for accurate impact assessment.
- Further research may be needed to refine PCMH models or measurement approaches.
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