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CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 228-235
DOI: 10.1055/s-0042-1742509
Section 9: Knowledge Representation and Management
Survey

Achieving Inclusivity by Design: Social and Contextual Information in Medical Knowledge

Janna Hastings
1   Department of Clinical, Educational and Health Psychology, University College London, UK
2   Institute for Intelligent Interacting Systems, Otto-von-Guericke University Magdeburg, Germany
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Summary

Objectives: To select, present, and summarize the most relevant papers published in 2020 and 2021 in the field of Knowledge Representation and Knowledge Management, Medical Vocabularies and Ontologies, with a particular focus on health inclusivity and bias.

Methods: A broad search of the medical literature indexed in PubMed was conducted. The search terms 'ontology'/'ontologies' or 'medical knowledge management' for the dates 2020-2021 (search conducted November 26, 2021) returned 9,608 records. These were pre-screened based on a review of the titles for relevance to health inclusivity, bias, social and contextual factors, and health behaviours. Among these, 109 papers were selected for in-depth reviewing based on full text, from which 22 were selected for inclusion in this survey.

Results: Selected papers were grouped into three themes, each addressing one aspect of the overall challenge for medical knowledge management. The first theme addressed the development of ontologies for social and contextual factors broadening the scope of health information. The second theme addressed the need for synthesis and translation of knowledge across historical disciplinary boundaries to address inequities and bias. The third theme encompassed a growing interest in the semantics of datasets used to train medical artificial intelligence systems and on how to ensure they are free of bias.

Conclusions: Medical knowledge management and semantic resources have much to offer efforts to tackle bias and enhance health inclusivity. Tackling inequities and biases requires relevant, semantically rich data, which needs to be captured and exchanged.

Keywords

Medical knowledge management - ontologies - vocabularies - health inequities - bias


Publication History

Article published online:
02 June 2022

© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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