Systematic review protocol: Examining the effects of introducing pay-for-performance for primary care physicians on diabetes outcomes in single-payer healthcare systems

Description

Background: Although pay-for-performance (P4P) for diabetes care is increasingly common across health organizations, evidence of its effectiveness in improving population health and service delivery is deficient. This information gap is attributable in part to the heterogeneity of healthcare financing, covered medical conditions, care settings, and provider remuneration arrangements within and across countries. Objective: This paper outlines a protocol for a systematic review examining the effects of introducing P4P for physicians in primary care and community settings to support guideline- based diabetes care. Our aim is to reduce the heterogeneity of evidence presented that has deterred conclusiveness of previous reviews by narrowing the focus to disease-specific P4P schemes in single-payer healthcare insurance systems. This approach enables us to minimize the risk of unintended consequences of P4P such as physicians’ gaming the payment system. Methods: Our review systematically searches, appraises, and synthesizes the literature concentrating on whether P4P for primary care physicians leads to better diabetes outcomes in single-payer health systems. We search 10 electronic databases and manually scan the reference lists of review articles and other global health literature. We include primary studies evaluating the effects of introducing P4P for diabetes care among primary care physicians in countries of universal health coverage. Outcomes of interest include patient morbidity, avoidable hospitalization, premature death, and healthcare costs. Results: We have received funding from Diabetes Canada and the New Brunswick Health Research Foundation to conduct policy-actionable diabetes health services research. Database searches were conducted and full-texts screened by two reviewers in 2017. We aim to submit the review for publication in 2018. Conclusions: We are narratively synthesizing the data. Because of the wide range of outcomes considered, we do not expect to perform a meta-analysis.