From assessment to improvement of elderly care in general practice using decision support to increase adherence to ACOVE quality indicators: study protocol for randomized control trial
1 Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
2 Pharmaceutical Research Center, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
3 Department of General Practice, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
4 Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
5 Department of Geriatrics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
Trials 2014, 15:81 doi:10.1186/1745-6215-15-81Published: 19 March 2014
Previous efforts such as Assessing Care of Vulnerable Elders (ACOVE) provide quality indicators for assessing the care of elderly patients, but thus far little has been done to leverage this knowledge to improve care for these patients. We describe a clinical decision support system to improve general practitioner (GP) adherence to ACOVE quality indicators and a protocol for investigating impact on GPs’ adherence to the rules.
We propose two randomized controlled trials among a group of Dutch GP teams on adherence to ACOVE quality indicators. In both trials a clinical decision support system provides un-intrusive feedback appearing as a color-coded, dynamically updated, list of items needing attention. The first trial pertains to real-time automatically verifiable rules. The second trial concerns non-automatically verifiable rules (adherence cannot be established by the clinical decision support system itself, but the GPs report whether they will adhere to the rules). In both trials we will randomize teams of GPs caring for the same patients into two groups, A and B. For the automatically verifiable rules, group A GPs receive support only for a specific inter-related subset of rules, and group B GPs receive support only for the remainder of the rules. For non-automatically verifiable rules, group A GPs receive feedback framed as actions with positive consequences, and group B GPs receive feedback framed as inaction with negative consequences. GPs indicate whether they adhere to non-automatically verifiable rules. In both trials, the main outcome measure is mean adherence, automatically derived or self-reported, to the rules.
We relied on active end-user involvement in selecting the rules to support, and on a model for providing feedback displayed as color-coded real-time messages concerning the patient visiting the GP at that time, without interrupting the GP’s workflow with pop-ups. While these aspects are believed to increase clinical decision support system acceptance and its impact on adherence to the selected clinical rules, systems with these properties have not yet been evaluated.
Controlled Trials NTR3566