Hospital Admission, Length of Stay
Conditions
Keywords
AI Prediction Algorithm, Emergency Department
Brief summary
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.
Interventions
None listed
Sponsors
Mayo Clinic
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Hospital Admissions | 282 days | 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 |
Countries
United States
Outcome results
None listed