PDF(Pubmed)
Abstract：
BACKGROUND: Systematic 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.
METHODS: We 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.
RESULTS: Our 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.
CONCLUSIONS: Given 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.
METHODS: We 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.
RESULTS: Our 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.
CONCLUSIONS: Given 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.
摘要：
背景：系统综述（SRs）正在加速发布。决策者可能很难在同一主题的多个SR之间进行比较和选择。我们的目标是了解医疗保健决策者(例如，从业者,政策制定者，研究人员）使用SR为决策提供信息，并探索拟议的人工智能（AI）工具的潜在作用，以协助进行关键评估和选择SR。
方法：我们开发了一项包含21个开放式和封闭式问题的调查。我们遵循知识翻译计划，通过社交媒体和专业网络传播调查。
结果：我们的调查回复率低于预期（已分发电子邮件的7.9％）。在684名受访者中，58.2%被认定为研究人员，37.1%作为从业者，学生占19.2%，决策者占13.5%。受访者经常寻找SR（97.1％）作为决策的证据来源。他们经常（97.9％）在他们感兴趣的给定主题上发现多个SR。刚刚超过一半(50.8%)的人努力在多个人中选择最值得信赖的SR。这些困难与缺乏时间有关（55.2%），或由于SRs的方法学质量不同而难以比较(54.2%)，结果和结论的差异(49.7%)或纳入研究的变异(44.6%)。受访者根据与他们感兴趣的问题的相关性比较了SR，方法学质量，和最近的SR搜索。大多数受访者（87.0%）对AI工具感兴趣，以帮助评估和比较SR。
结论：鉴于使用SR证据的已识别障碍，一种人工智能工具，用于方便比较SR的相关性，搜索和方法学质量，可以帮助用户有效地在SR中进行选择并做出医疗保健决策。
方法：我们开发了一项包含21个开放式和封闭式问题的调查。我们遵循知识翻译计划，通过社交媒体和专业网络传播调查。
结果：我们的调查回复率低于预期（已分发电子邮件的7.9％）。在684名受访者中，58.2%被认定为研究人员，37.1%作为从业者，学生占19.2%，决策者占13.5%。受访者经常寻找SR（97.1％）作为决策的证据来源。他们经常（97.9％）在他们感兴趣的给定主题上发现多个SR。刚刚超过一半(50.8%)的人努力在多个人中选择最值得信赖的SR。这些困难与缺乏时间有关（55.2%），或由于SRs的方法学质量不同而难以比较(54.2%)，结果和结论的差异(49.7%)或纳入研究的变异(44.6%)。受访者根据与他们感兴趣的问题的相关性比较了SR，方法学质量，和最近的SR搜索。大多数受访者（87.0%）对AI工具感兴趣，以帮助评估和比较SR。
结论：鉴于使用SR证据的已识别障碍，一种人工智能工具，用于方便比较SR的相关性，搜索和方法学质量，可以帮助用户有效地在SR中进行选择并做出医疗保健决策。
