RESUMO
The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the "gold standard" of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users' needs and feedback in the design process.
RESUMO
BACKGROUND: Face shields are a critical piece of personal protective equipment and their comfort impacts compliant use and thus protectiveness. Optimal design criteria for face shield use in healthcare environments are limited. We attempt to identify factors affecting face shield usability and to test and optimize a face shield for comfort and function in health care settings. METHODS: A broad range of workers in a large health care system were surveyed regarding face shield features and usability. Quantitative and qualitative analysis informed the development of iterative prototypes which were tested against existing shields. Iterative testing and redesign utilized expert insight and feedback from participant focus groups to inform subsequent prototype designs. RESULTS: From 1,648 responses, 6 key elements were identified: ability to adjust tension, shifting load bearing from the temples, anti-fogging, ventilation, freedom of movement, and durability. Iterative prototypes received consistently excellent feedback based on use in the clinical environment, demonstrating incremental improvement. CONCLUSION: We defined elements of face shield design necessary for usability in health care and produced a highly functional face shield that satisfies frontline provider criteria and Emergency Use Authorization standards set by the Food and Drug Administration. Integrating human factors principles into rapid-cycle prototyping for personal protective equipment is feasible and valuable.
