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Triangulating Methodologies from Software, Medicine and Human Factors Industries to Measure Usability and Clinical Efficacy of Medication Data Visualization in an Electronic Health Record System
Within the last decade, use of Electronic Health Record (EHR) systems has become intimately integrated into healthcare practice in the United States. However, large gaps remain in the study of clinical usability and require rigorous and innovative approaches for testing usability principles. In this...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543381/ https://www.ncbi.nlm.nih.gov/pubmed/28815147 |
Sumario: | Within the last decade, use of Electronic Health Record (EHR) systems has become intimately integrated into healthcare practice in the United States. However, large gaps remain in the study of clinical usability and require rigorous and innovative approaches for testing usability principles. In this study, validated tools from the core functions that EHRs serve—software, medicine and human factors—were combined to holistically understand and objectively measure usability of medication data displays. The first phase of this study included 132 medical trainee participants who were randomized to one of two simulated EHR environments with either a medication list or a medication timeline visualization. Within these environments human-computer interaction metrics, clinical reasoning and situation awareness tests, and usability surveys captured their multi-faceted interactions. Results showed no statistically significant differences in the two displays from software and situation awareness perspectives, though there were higher statistically significant usability scores of the medication timeline (intervention) as compared to the medication list (control). This first phase of a novel design in triangulating methodologies revealed several limitations from which future experiments will be adjusted with hopes of yielding further insight and a generalizable testing platform for evolving EHR interfaces. |
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