Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department

Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predic...

Full description

Saved in:
Bibliographic Details
Published in:Diagnostics (Basel) 2022-10, Vol.12 (10), p.2463
Main Authors: Sax, Dana R, Sturmer, Lillian R, Mark, Dustin G, Rana, Jamal S, Reed, Mary E
Format: Article
Language:English
Subjects:
acute heart failure
clinical decision support
Data collection
Decision making
Electronic health records
Emergency medical care
Emergency medicine
Health aspects
Health risk assessment
Heart failure
Interviews
Machine learning
Methods
Patients
Physicians
predictive models
Qualitative research
Risk factors
risk stratification
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12102463