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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 3 of 3 Research Studies DisplayedSalwei ME, Hoonakker P, Carayon P
Usability of a human factors-based clinical decision support in the emergency department: lessons learned for design and implementation.
A human-centered design process was followed to assess the usability and adoption of human factors (HF)-based clinical decision support (CDS) in the emergency department (ED). A CDS was developed to aid in pulmonary embolism (PE) diagnosis, showing high usability in testing. However, despite positive perceptions, actual CDS usage remained low due to integration issues with clinician workflow. The findings highlight the need for ongoing refinement of CDS design to align with clinical workflows and enhance usability.
AHRQ-funded; HS026395; HS024558; HS022086. NIH 142099
Citation: Salwei ME, Hoonakker P, Carayon P .
Usability of a human factors-based clinical decision support in the emergency department: lessons learned for design and implementation.
Hum Factors 2024 Mar; 66(3):647-57. doi: 10.1177/00187208221078625.
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Emergency Department, Implementation
Hinson JS, Klein E, Smith A
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
This study’s objective was to develop, implement, and evaluate an electronic health record (EHR) embedded clinical decision support (CDS) system that leveraged machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 hours and inpatient care needs within 72 hours into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. A retrospective cohort of 21,452 ED patients who visited one of five ED study sites was used to derive ML models and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation. Model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. ML model performance was excellent under all conditions. AUC ranged from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after the implementation.
AHRQ-funded; HS026640.
Citation: Hinson JS, Klein E, Smith A .
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
NPJ Digit Med 2022 Jul 16;5(1):94. doi: 10.1038/s41746-022-00646-1..
Keywords: COVID-19, Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation, Electronic Health Records (EHRs), Emergency Department, Shared Decision Making
Salwei ME, Carayon P, Hoonakker PLT
Workflow integration analysis of a human factors-based clinical decision support in the emergency department.
Numerous challenges with the implementation, acceptance, and use of health IT are related to poor usability and a lack of integration of the technologies into clinical workflow, and have, therefore, limited the potential of these technologies to improve patient safety. In this paper, the investigators propose a definition and conceptual model of health IT workflow integration. Using interviews of 12 emergency department (ED) physicians, they identified 134 excerpts of barriers and facilitators to workflow integration of a human factors (HF)-based clinical decision support (CDS) implemented in the ED.
AHRQ-funded; HS022086.
Citation: Salwei ME, Carayon P, Hoonakker PLT .
Workflow integration analysis of a human factors-based clinical decision support in the emergency department.
Appl Ergon 2021 Nov;97:103498. doi: 10.1016/j.apergo.2021.103498..
Keywords: Emergency Department, Workflow, Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation