A Study of Emergency Department AI Prediction Impact

Mayo Clinic80 enrolled

Overview

The purpose of this study is to evaluate the impact of an AI admission prediction tool on the number of preventable hospital admissions, emergency department (ED) length of stay, when the predictions are displayed only to a dedicated ED triage team. Also, to evaluate user perceptions of the AI tool among the triage team users and medical officer of the day users. Additionally, to evaluate any impact of the AI tool on the number of interventions performed by the triage team, and to evaluate the impact of the tool on time-to-admission after an admission order is placed.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Hospital Admission Length of Stay

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * For the survey component, any HIM clinician that works a shift in the triage area, ED physicians, and the medical officer of the day will be included. * For length of stay data, adult patients registered in the Mayo Clinic-Rochester St. Mary's Emergency Department will be included. Exclusion Criteria: * For the survey, clinicians not working a triage shift during the study period will be excluded. * For the length of stay analysis, only adult ED patients will be included, who do not triaged to the behavioral health/psychiatry pathway, nor patients who are triaged to the Emergency Department observation pathway.

Locations (1)

Mayo Clinic Minnesota

Rochester, Minnesota, United States

Outcomes

Primary Outcomes

Hospital Admissions

Number of avoidable admissions prevented as a fraction of all ED patients in a day, specifically, the number of patients who were seen by the SAPPHIRE triage team and discharged home

Time frame: 282 days

Data from ClinicalTrials.gov

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