A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

Abstract

Objective A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time. Materials and Methods PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles’ references. The details of design, implementation and evaluation of included CDSSs were extracted. Results Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing. Conclusions We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

Trends in management of hip and knee osteoarthritis in general practice in Australia over an 11-year window: a nationwide cross-sectional survey

“…The effectiveness and utility of electronic decision support tools/systems are still unclear. 33 , 34 At a system level, there is interest in alternative models of health care delivery to optimise quality and outcomes of care while improving the allocation of finite healthcare resources, 35 , 36 although there is limited research in OA specifically. Clinical trials investigating a GP OA model consultation that included an electronic template in the UK 37 and a structured primary care OA model integrating GP care with group-based physiotherapist-led education and exercise and optional healthy eating program in Norway 38 have shown increased uptake of core treatment recommendations by GPs.…”

Pediatric Provider Utilization of a Clinical Decision Support Alert and Association with HIV Pre-exposure Prophylaxis Prescription Rates

“…This study adds to a body of previous literature that demonstrated mixed outcomes regarding the effectiveness of electronic alerts to impact provider behavior. 14 15 16 17 18 19 20 21 22 This is also the first study to evaluate use of a CDS alert for HIV PrEP in a pediatric setting.…”

“…Previous studies evaluating the effect of an EHR-based clinical decision support (CDS) alert on provider practices have demonstrated mixed results. 14 15 16 17 18 19 20 21 22 23 24 Studies have previously shown both evidence of electronic alerts impacting clinician practice, 16 17 18 19 20 23 24 as well as unsuccessfully changing provider behavior. 15 21 Specific to HIV screening, EHR-based CDS systems have been shown to successfully increase rates of HIV screening in various settings including primary care and emergency medicine.…”

“…14 15 16 17 18 19 20 21 22 23 24 Studies have previously shown both evidence of electronic alerts impacting clinician practice, 16 17 18 19 20 23 24 as well as unsuccessfully changing provider behavior. 15 21 Specific to HIV screening, EHR-based CDS systems have been shown to successfully increase rates of HIV screening in various settings including primary care and emergency medicine. 25 26 27 Qualitative data evaluating primary care provider preferences for PrEP-specific support demonstrated that PrEP-inexperienced providers were particularly interested in CDS systems and access to PrEP-experienced providers for expert advice or referrals.…”

BAIT: A New Medical Decision Support Technology Based on Discrete Choice Theory

“…As a consequence, a variety of medical decision support systems have been developed. [1][2][3][4][5] These can be classified into knowledge-based and non-knowledge-based systems. 6 The former require that experts perform the very difficult task of explicating their tacit knowledge into deterministic rules; furthermore, such rule-based systems struggle with capturing the subtleties that are present in real-life contexts.…”