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dc.contributor.authorCharpignon, Marie-Laure
dc.contributor.authorCarrel, Adrien
dc.contributor.authorJiang, Yihang
dc.contributor.authorKwaga, Teddy
dc.contributor.authorCantada, Beatriz
dc.contributor.authorHyslop, Terry
dc.contributor.authorCox, Christopher E.
dc.contributor.authorHaines, Krista
dc.contributor.authorKoomson, Valencia
dc.contributor.authorDumas, Guillaume
dc.contributor.authorMorley, Michael
dc.contributor.authorDunn, Jessilyn
dc.contributor.authorWong, An- Kwok Ian
dc.date.accessioned2023-10-18T13:11:36Z
dc.date.available2023-10-18T13:11:36Z
dc.date.issued2023
dc.identifier.citationCharpignon, M. L., Carrel, A., Jiang, Y., Kwaga, T., Cantada, B., Hyslop, T., ... & Ian Wong, A. K. (2023). Going beyond the means: Exploring the role of bias from digital determinants of health in technologies. PLOS Digital Health, 2(10), e0000244.en_US
dc.identifier.urihttp://ir.must.ac.ug/xmlui/handle/123456789/3190
dc.description.abstractBackground: 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.en_US
dc.description.sponsorshipDuke Clinical and Translational Science Institute by National Center for Advancing Translational Sciences of the NIH under UL1TR002553. GD is funded by the Institute for Data Valorization (Grant CF00137433), Montre´al, and the Fonds de Recherche du Que´bec (Grant 285289 & 295291).en_US
dc.language.isoen_USen_US
dc.publisherPLOS Digital Healthen_US
dc.subjectDigital technologiesen_US
dc.subjectDigital healthen_US
dc.subjectMedical technologyen_US
dc.titleGoing beyond the means: Exploring the role of bias from digital determinants of health in technologiesen_US
dc.typeArticleen_US


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