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Don’t forget about human factors: Lessons learned from COVID-19 point-of-care testing

Sarah Farmer, Victoria Razin, Amanda Foster Peagler, Samantha Strickler, W. Bradley Fain, Gregory L. Damhorst, Russell R. Kempker, Nira R. Pollock, Oliver Brand, Brooke Seitter, Stacy Heilman, Eric J. Nehl, Joshua M. Levy, David Gottfried, Greg S. Martin, Morgan Greenleaf, David N. Ku, Jesse J. Waggoner, Elizabeth Iffrig, Robert G. Mannino, Yun F. Wang, Eric A. Ortlund, Julie Sullivan, Paulina A. Rebolledo, Viviana Clavería, John D. Roback, MacArthur Benoit, Cheryl Stone, Annette Esper, Filipp Frank, Wilbur A. Lam

2022Cell Reports Methods22 citationsDOIOpen Access PDF

Abstract

During the COVID-19 pandemic, the development of point-of-care (POC) diagnostic testing accelerated in an unparalleled fashion. As a result, there has been an increased need for accurate, robust, and easy-to-use POC testing in a variety of non-traditional settings (i.e., pharmacies, drive-thru sites, schools). While stakeholders often express the desire for POC technologies that are "as simple as digital pregnancy tests," there is little discussion of what this means in regards to device design, development, and assessment. The design of POC technologies and systems should take into account the capabilities and limitations of the users and their environments. Such "human factors" are important tenets that can help technology developers create POC technologies that are effective for end-users in a multitude of settings. Here, we review the core principles of human factors and discuss lessons learned during the evaluation process of SARS-CoV-2 POC testing.

Topics & Concepts

Point-of-care testingVariety (cybernetics)Computer scienceCoronavirus disease 2019 (COVID-19)Process (computing)PandemicPoint (geometry)Point of careProcess managementRisk analysis (engineering)Data scienceEngineeringBusinessMedicineArtificial intelligenceNursingDiseaseImmunologyMathematicsGeometryInfectious disease (medical specialty)Operating systemPathologySARS-CoV-2 detection and testingMobile Health and mHealth ApplicationsMobile Crowdsensing and Crowdsourcing
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