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Going beyond the means: Exploring the role of bias from digital determinants of health in technologies.
Charpignon, Marie-Laure; Carrel, Adrien; Jiang, Yihang; Kwaga, Teddy; Cantada, Beatriz; Hyslop, Terry; Cox, Christopher E; Haines, Krista; Koomson, Valencia; Dumas, Guillaume; Morley, Michael; Dunn, Jessilyn; Ian Wong, An-Kwok.
Afiliação
  • Charpignon ML; Massachusetts Institute of Technology; Institute for Data, Systems, and Society; Laboratory for Information and Decision Systems, Boston, Massachusetts, United States of America.
  • Carrel A; CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France.
  • Jiang Y; Imperial College London, London, United Kingdom.
  • Kwaga T; Duke University, Pratt School of Engineering, Department of Biomedical Engineering, Durham, North Carolina, United States of America.
  • Cantada B; Mbarara University of Science and Technology, Department of Ophthalmology, Mbarara, Uganda.
  • Hyslop T; Massachusetts Institute of Technology; Institute Community and Equity Office, Boston, Massachusetts, United States of America.
  • Cox CE; Duke University, Department of Biostatistics and Bioinformatics, Durham, North Carolina, United States of America.
  • Haines K; Duke University, Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Durham, North Carolina, United States of America.
  • Koomson V; Duke University, Department of Surgery, Durham, North Carolina, United States of America.
  • Dumas G; Tufts University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States of America.
  • Morley M; CHU Sainte-Justine Research Center, Department of Psychiatry, Université de Montréal, Montréal, Quebec, Canada.
  • Dunn J; Mila-Quebec AI Institute, University of Montreal, Montréal, Quebec, Canada.
  • Ian Wong AK; Ophthalmic Consultants of Boston, Boston, Massachusetts, United States of America.
PLOS Digit Health ; 2(10): e0000244, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37824494
ABSTRACT

BACKGROUND:

In light of recent retrospective studies revealing evidence of disparities in access to medical technology and of bias in measurements, this narrative review assesses digital determinants of health (DDoH) in both technologies and medical formulae that demonstrate either evidence of bias or suboptimal performance, identifies potential mechanisms behind such bias, and proposes potential methods or avenues that can guide future efforts to address these disparities.

APPROACH:

Mechanisms are broadly grouped into physical and biological biases (e.g., pulse oximetry, non-contact infrared thermometry [NCIT]), interaction of human factors and cultural practices (e.g., electroencephalography [EEG]), and interpretation bias (e.g, pulmonary function tests [PFT], optical coherence tomography [OCT], and Humphrey visual field [HVF] testing). This review scope specifically excludes technologies incorporating artificial intelligence and machine learning. For each technology, we identify both clinical and research recommendations.

CONCLUSIONS:

Many of the DDoH mechanisms encountered in medical technologies and formulae result in lower accuracy or lower validity when applied to patients outside the initial scope of development or validation. Our clinical recommendations caution clinical users in completely trusting result validity and suggest correlating with other measurement modalities robust to the DDoH mechanism (e.g., arterial blood gas for pulse oximetry, core temperatures for NCIT). Our research recommendations suggest not only increasing diversity in development and validation, but also awareness in the modalities of diversity required (e.g., skin pigmentation for pulse oximetry but skin pigmentation and sex/hormonal variation for NCIT). By increasing diversity that better reflects patients in all scenarios of use, we can mitigate DDoH mechanisms and increase trust and validity in clinical practice and research.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: PLOS Digit Health Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: PLOS Digit Health Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos