ABSTRACT
Objectives@#Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully. @*Methods@#Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement. @*Results@#While the participants expressed expectations that medical AI could enhance their patients’ outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment. @*Conclusions@#Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.
ABSTRACT
Purpose@#For patients with time-critical acute coronary syndrome, reporting electrocardiogram (ECG) findings is the most importantcomponent of the treatment process. We aimed to develop and validate an automated Fast Healthcare InteroperabilityResources (FHIR)-based 12-lead ECG mobile alert system for use in an emergency department (ED). @*Materials and Methods@#An automated FHIR-based 12-lead ECG alert system was developed in the ED of an academic tertiarycare hospital. The system was aimed at generating an alert for patients with suspected acute coronary syndrome based on interpretationby the legacy device. The alert is transmitted to physicians both via a mobile application and the patient’s electronic medicalrecord (EMR). The automated FHIR-based 12-lead ECG alert system processing interval was defined as the time from ED arrivaland 12-lead ECG capture to the time when the FHIR-based notification was transmitted. @*Results@#During the study period, 3812 emergency visits and 1581 12-lead ECGs were recorded. The FHIR system generated 155alerts for 116 patients. The alerted patients were significantly older [mean (standard deviation): 68.1 (12.4) years vs. 59.6 (16.8)years, p<0.001], and the cardiac-related symptom rate was higher (34.5% vs. 19%, p<0.001). Among the 155 alerts, 146 (94%) weretransmitted successfully within 5 minutes. The median interval from 12-lead ECG capture to FHIR notification was 2.7 min [interquartilerange (IQR) 2.2–3.1 min] for the group with cardiac-related symptoms and 3.0 min (IQR 2.5–3.4 min) for the group withnon-cardiac-related symptoms. @*Conclusion@#An automated FHIR-based 12-lead ECG mobile alert system was successfully implemented in an ED.