Exploring decision-makers’ challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare

• Published in: BMJ open Vol. 14; no. 7; p. e084124
• Main Authors: Lunny, Carole, Whitelaw, Sera, Reid, Emma K, Chi, Yuan, Ferri, Nicola, Zhang, Jia He (Janet), Pieper, Dawid, Kanji, Salmaan, Veroniki, Areti-Angeliki, Shea, Beverley, Dourka, Jasmeen, Ardern, Clare, Pham, Ba, Bagheri, Ebrahim, Tricco, Andrea C
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 05.07.2024; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: Artificial Intelligence; Bias; Bibliometrics; Cohort analysis; COVID-19; cross-sectional studies; Decision Making; Decision Support Techniques; Delivery of Health Care; Epidemiology; Humans; implementation science; Intervention; Meta-analysis; natural language processing; Public health; quality in health care; Response rates; Social networks; Statistical analysis; Surveys and Questionnaires; Systematic review; Systematic Reviews as Topic - methods; natural language processing; systematic review; cross-sectional studies; quality in health care; decision making; implementation science
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2024-084124

BackgroundSystematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare decision-makers (eg, practitioners, policymakers, researchers) use SRs to inform decision-making and to explore the potential role of a proposed artificial intelligence (AI) tool to assist in critical appraisal and choosing among SRs.MethodsWe developed a survey with 21 open and closed questions. We followed a knowledge translation plan to disseminate the survey through social media and professional networks.ResultsOur survey response rate was lower than expected (7.9% of distributed emails). Of the 684 respondents, 58.2% identified as researchers, 37.1% as practitioners, 19.2% as students and 13.5% as policymakers. Respondents frequently sought out SRs (97.1%) as a source of evidence to inform decision-making. They frequently (97.9%) found more than one SR on a given topic of interest to them. Just over half (50.8%) struggled to choose the most trustworthy SR among multiple. These difficulties related to lack of time (55.2%), or difficulties comparing due to varying methodological quality of SRs (54.2%), differences in results and conclusions (49.7%) or variation in the included studies (44.6%). Respondents compared SRs based on the relevance to their question of interest, methodological quality, and recency of the SR search. Most respondents (87.0%) were interested in an AI tool to help appraise and compare SRs.ConclusionsGiven the identified barriers of using SR evidence, an AI tool to facilitate comparison of the relevance of SRs, the search and methodological quality, could help users efficiently choose among SRs and make healthcare decisions.