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Utilization of Chest Pain Decision Aids in a Community Hospital Emergency Department: A Mixed-methods Implementation
Glenn Bean1, Uma Krishnan2, Jason R Stone3
1From the Department of Preventive Cardiology, Pulse Heart Institute, Tacoma General Hospital, Tacoma, WA.
Introduction:
Chest pain is a common reason for emergency department (ED) visits. Evidence-based decision aids assessing risk for an adverse cardiac event are underused in community hospital emergency care. This study explored the acceptability, barriers, facilitators, and potential strategies for implementation of the HEART Score risk stratification tool, accelerated diagnostic pathway, and shared decision-making visual aid with physicians and chest pain patients ages >45 in a community hospital ED.
Methods:
Single center, mixed-methods study. (1) Physician semistructured interviews using The Consolidated Framework for Implementation Research for systematic analysis. (2) Patient and physician surveys. (3) 16-week intervention of physician training and pilot testing of decision aids with ED patients.
Results:
Physician interviews (n = 19); key facilitators: electronic medical record decision support, ease of use, risk stratification and disposition support, and shared decision-making training. Key barriers: time constraints, patient ability, and/or willingness to participate in shared decision-making, lack of integration with medical record and change in practice workflow. Patient study participants (n = 184) with a survey response rate of 92% (n = 170). Most patients (85%) were satisfied with the shared decision-making visual aid. Physicians surveyed (n = 84) with a response rate of 50% (n = 42). Most physicians, 95% (n = 40), support use of the HEART Score, with limited acceptance of the shared decision-making visual aid of 57% (n = 24).
Conclusions:
Using evidence-based chest pain decision aids in a community hospital ED is feasible and acceptable. Key barriers and facilitators for implementation were identified. Further research in community hospitals is needed to verify findings, examine generalizability, and test implementation strategies.
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