Knowledge user survey and Delphi process to inform development of a new risk of bias tool to assess systematic reviews with network meta-analysis (RoB NMA tool)

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

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.

Methodological review of NMA bias concepts provides groundwork for the development of a list of concepts for potential inclusion in a new risk of bias tool for network meta-analysis (RoB NMA Tool)

INTRODUCTION

Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of the comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs.

METHODS AND ANALYSIS

We included papers that present items related to bias, reporting or methodological quality, papers assessing the quality of NMAs, or method papers. We searched MEDLINE, the Cochrane Library and unpublished literature (up to July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias and was included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions).

RESULTS

One hundred eighty-one articles were assessed in full text and 58 were included. Of these articles, 12 were tools, checklists or journal standards; 13 were guidance documents for NMAs; 27 were studies related to bias or NMA methods; and 6 were papers assessing the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The 22 items we included were reworded into concepts specific to bias in NMAs.

CONCLUSIONS

A list of 22 concepts was included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.