Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In JMIR human factors

BACKGROUND : Not thinking of a diagnosis is a leading cause of diagnostic error in the emergency department, resulting in delayed treatment, morbidity, and excess mortality. Electronic differential diagnostic support (EDS) results in small but significant reductions in diagnostic error. However, the uptake of EDS by clinicians is limited.

OBJECTIVE : We sought to understand physician perceptions and barriers to the uptake of EDS within the emergency department triage process.

METHODS : We conducted a qualitative study using a research associate to rapidly prototype an embedded EDS into the emergency department triage process. Physicians involved in the triage assessment of a busy emergency department were provided the output of an EDS based on the triage complaint by an embedded researcher to simulate an automated system that would draw from the electronic medical record. Physicians were interviewed immediately after their experience. Verbatim transcripts were analyzed by a team using open and axial coding, informed by direct content analysis.

RESULTS : In all, 4 themes emerged from 14 interviews: (1) the quality of the EDS was inferred from the scope and prioritization of the diagnoses present in the EDS differential; (2) the trust of the EDS was linked to varied beliefs around the diagnostic process and potential for bias; (3) clinicians foresaw more benefit to EDS use for colleagues and trainees rather than themselves; and (4) clinicians felt strongly that EDS output should not be included in the patient record.

CONCLUSIONS : The adoption of an EDS into an emergency department triage process will require a system that provides diagnostic suggestions appropriate for the scope and context of the emergency department triage process, transparency of system design, and affordances for clinician beliefs about the diagnostic process and addresses clinician concern around including EDS output in the patient record.

Sibbald Matthew, Abdulla Bashayer, Keuhl Amy, Norman Geoffrey, Monteiro Sandra, Sherbino Jonathan


adoption, artificial intelligence, attitude, automation, clinical reasoning, diagnosis, diagnostic, diagnostic error, electronic differential diagnostic support, emergency, human factors, natural language processing, support system, triage