Confronting complexity: toward equitable healthcare technology design and evaluation for socioeconomically marginalized patients
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Le résumé fourni par la source
Abstract Objectives Investigate how patients at a Federally-Qualified Health Center (FQHC) perform with, and perceive, complex telehealth tasks. Determine which complexity dimensions most affect patients’ performance and perceptions, and compare this to researcher-evaluators’ complexity walkthrough results. Materials and Methods A novel complexity walkthrough inspection method was implemented by researcher-evaluators (n = 8) to identify theory-informed complexity dimensions in 6 FQHC-required telehealth tasks. Remote user testing (n = 24) where FQHC patients were observed performing tasks, then completed newly-developed complexity-focused interviews and surveys. Descriptive statistics regarding complexity dimension presence, task performance, cognitive load, and perceived difficulty were integrated with qualitative data analyzed using inductive and deductive coding. Results Patients completed 33.9% of the required subtasks without issues. Cognitive load and perceived difficulty were high for 2 tasks. Complexity dimensions of ambiguity (unclear inputs/processes; new concepts/words) and relationship (context switching; deep navigational hierarchies) most affected patient-perceived difficulty. Patients spent twice as long as walkthrough evaluators on tasks, and encountered broader complexity dimensions: new concepts/words, and errors. Many patients ended tasks early, asserting that they would abandon them outside of a study or had previously done so. Discussion Technology-mediated task complexity may explain some telehealth uptake inequities. The complexity dimensions that challenge patients extend known usability heuristics by enhancing their equity sensitivity. Complexity walkthroughs surface design patterns that challenge patients, but complexity-focused user testing with patients reveals additional difficulties. Findings support complexity reduction of tasks via structuring, familiar concepts/words, feature integration, and shallow/broad navigation. Conclusion This paper’s novel, theoretically-grounded complexity-focused methods and findings may inform future equitable design and evaluation of technology-mediated tasks for socioeconomically marginalized patients.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Confronting complexity: toward equitable healthcare technology design and evaluation for socioeconomically marginalized patients
- Date Crossref
- 07/07/2026
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
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University of Michigan pays non établi dans la noticeUniversité ou école supérieure
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University of Virginia pays non établi dans la noticeUniversité ou école supérieure
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Michigan Medicine pays non établi dans la noticeÉtablissement de santé
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Mohamed bin Zayed University of Artificial Intelligence Department of Human-Computer Interaction pays non établi dans la noticeUniversité ou école supérieure
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University of Victoria pays non établi dans la noticeUniversité ou école supérieure
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School of Public Health Department of Health Behavior and Health Equity pays non établi dans la noticeUniversité ou école supérieure
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School of Medicine Department of Learning Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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Covenant Community Care pays non établi dans la noticeInstitution
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School of Health Information Science pays non établi dans la noticeUniversité ou école supérieure
University of Michigan, University of Virginia et Michigan Medicine, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.