Risk of Bias Assessment for Diversity Groups in Community-Based Primary Health Care Artificial Intelligence Systems: A Rapid Review Protocol
Creators: name: Paquette, Jean-Sébastien
name: Després, Philippe
name: Gentelet, Karine
name: Gagnon, Marie-Pierre
name: Sasseville, Maxime
name: Ouellet, Steven
name: Couture, Vincent
name: Rhéaume, Caroline
name: Bergeron, Frédéric
name: Darmon, David
Publisher: name: Open Science Framework
Year: 2023
Subjects: subject: Social and Behavioral Sciences
subject: Health Information Technology
subject: Medicine and Health Sciences
subject: Diseases
subject: Sociology
subject: FOS: Sociology
subject: Computer Engineering
subject: Engineering
subject: Medical Sciences
subject: health equity; health disparity; algorithms; artificial intelligence; primary care; rapid review

description: This rapid review is the first step of the larger “Protecting and Engaging Vulnerable Populations in the Development of Predictive Models in Primary Health Care for Inclusive, Diverse and Equitable AI” (PREMIA) project. This work we will be presented results to key stakeholders and we will conduct qualitative data collection to identify and evaluate biases in one AI model conduct interviews with them. Abstract: Background: Current literature identifies several potential benefits of artificial intelligence in primary health care. However, there is a lack of understanding on how the risk of bias is considered in the development of primary health care algorithms and to what extend they perpetuate or introduce potential biases toward groups that could be considered as vulnerable in function of their characteristics (e.g., age, sex, gender identity, sexual orientation, race, ethnicity, religion, physical ability, socioeconomic status, etc.). To the best of our knowledge no reviews are currently available to identify the relevant methods to assess risk of bias in these algorithms. The primary question of this review is which strategies can assess risk of bias in community-based primary health care algorithms toward vulnerable or diversity groups? Objective: To identify 1) relevant methods (e.g., frameworks, tools, checklists, etc.) to assess the risk of bias toward diversity groups in the development and/or deployment of algorithms in community-based primary health care and 2) mitigation interventions deployed to promote and increase equity, diversity and inclusion. This review looks at what attempts to mitigate bias have been documented and which vulnerable or diversity groups have been considered. Methods: A rapid review of the scientific literature in four databases (PubMed, CINAHL, Web of Science and PsychInfo) in the last 5 years. Two reviewers will independently screen the titles and abstracts and the full text of the identified records on the Covidence systematic review software. We will include all studies on methods developed and/or tested to assess risk of bias in algorithms that can be relevant in community-based primary health care settings. Data of selected studies will be extracted using a validated grid and results will be presented using structured narrative summaries. Results: In November 2022 an information specialist developed a specific search strategy based on the main concepts of our primary review question in the most relevant databases (PubMed, CINAHL, Web of Science and PsychInfo) in the last 5 years. We completed the search strategy in December 2022 and 1022 sources were identified. We start the screening phase of titles and abstracts. Conclusions: This review will develop structured summaries of strategies to mitigate bias toward vulnerable populations and diversity groups in artificial intelligence models. This could be useful to researchers and other stakeholders to identify potential sources of bias in algorithms for eventually try to reduce or eliminate them.