Development of quality indicators for type 2 diabetes, extractable from the electronic health record of the general physician. A rand-modified Delphi method. Issue 1 (February 2020) - Van den Bulck, Steve A.; Vankrunkelsven, Patrik; Goderis, Geert; Broekx, Lien; Dreesen, Kathleen; Ruijten, Laura; Mpoukouvalas, Dimitri; Hermens, Rosella
Highlights: A set of evidence based and EHR extractable quality indicators for diabetes care. This set of quality indicators is developed based on 10 international guidelines. These quality indicators cover various aspects of type 2 diabetes primary care. Aspects included in this set are screening, prevention, diagnosis and management. These indicators can be used to evaluate and improve type 2 diabetes primary care. Abstract: Aims: Type 2 diabetes mellitus is a worldwide cause of significant morbidity and mortality. The general physician (GP) plays an important role in managing this disease and the use of the electronic health record (EHR) can improve quality for type 2 diabetes care. We aimed to develop a set of evidence-based and EHR extractable quality indicators for type 2 diabetes, enabling an automated quality assessment. Methods: We used a Rand-modified Delphi method. Recommendations were selected from (inter)national guidelines using the 'SMART' principle and scored by a multidisciplinary expert panel. After analysis of the median score, prioritization and consensus, recommendations were discussed during a consensus meeting. A final validation round resulted in a core set of recommendations, which were transformed into quality indicators. Results: A total of 101 recommendations originating from 10 (inter)national guidelines were presented to the expert panel, which resulted in a core set of 50 recommendations that were merged and modified into 36 recommendations after the consensus meeting. The panel added 3 recommendations. This resulted in a final set of 39 quality indicators. Conclusions: Our study presents a set of 39 quality indicators for type 2 diabetes in primary care that are EHR extractable, enabling automated quality assessment.