%0 Journal Article
%T Development of quality indicators for hypertension, extractable from the electronic health record of the general practitioner: a rand-modified Delphi method.
%A Danhieux K
%A Hollevoet M
%A Lismont S
%A Taveirne P
%A Van Vaerenbergh L
%A Vaes B
%A Van den Bulck S
%J BMC Prim Care
%V 25
%N 1
%D 2024 Aug 15
%M 39148044
暂无%R 10.1186/s12875-024-02543-w
%X <B>BACKGROUND: </B>Hypertension, a chronic medical condition affecting millions of people worldwide, is a leading cause of cardiovascular diseases. A multidisciplinary approach is needed to reduce the burden of the disease, with general practitioners playing a vital role. Therefore, it is crucial that GPs provide high-quality care that is standardized and based on the most recent European guidelines. Quality indicators (QIs) can be used to assess the performance, outcomes, or processes of healthcare delivery and are critical in helping healthcare professionals identify areas of improvement and measure progress towards achieving desired health outcomes. However, QIs to evaluate the care of patients with hypertension in general practice have been studied to a limited extent. The aim of our study is to define quality indicators for hypertension in general practice that are extractable from the electronic health record (EHR) and can be used to evaluate and improve the quality of care for hypertensive patients in the general practice setting.<BR><B>METHODS: </B>We used a Rand-modified Delphi procedure. We extracted recommendations from European guidelines and assembled them into an online questionnaire. An initial scoring based on the SMART principle and extractability from the EHR was performed by panel members, these results were analyzed using a Median Likert score, prioritization and degree of consensus. A consensus meeting was set up in which all the recommendations were discussed, followed by a final validation round.<BR><B>RESULTS: </B>Our study extracted 115 recommendations. After analysis of the online questionnaire round and a consensus meeting round, 37 recommendations were accepted and 75 were excluded. Of these 37 recommendations, 9 were slightly modified and 4 were combined into 2 recommendations, resulting in a list of 35 recommendations. All recommendations of the final set were translated to QIs, made up of 7 QIs on screening, 6 QIs on diagnosis, 11 QIs on treatment, 5 QIs on outcome and 6 QIs on follow-up.<BR><B>CONCLUSIONS: </B>Our study resulted in a set of 35 QIs for hypertension in general practice. These QIs, tailored to the Belgian EHR, provide a robust foundation for automated audit and feedback and could substantially benefit other countries if adapted to their systems.