Key result
ED clinicians favor machine learning acute heart failure decision support integrated into existing workflows for disposition.
Why the study?
Hospital admissions for acute heart failure remain high, prompting the need to understand implementation barriers and opportunities before deploying a machine learning risk stratification tool.
Cross-Sectional (n=75)
Yes
Engagement with frontline users reveals that an ML-based acute heart failure risk tool requires seamless workflow integration and provider education to maximize adoption.
Supports user-centered ML tool design for ED acute HF disposition; leaves open whether integration improves outcomes or adoption.
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 (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.
No takes yet. Share an insight, caveat, or question.
Sax et al. (2022) conducted a cross-sectional in Acute Heart Failure (provider perspectives) (n=75). Machine learning-based acute heart failure risk prediction tool and clinical decision support was evaluated on Provider-reported barriers and opportunities regarding implementation. Most ED providers desired machine learning-based decision support for acute heart failure streamlined into existing workflows to primarily assist with ED disposition decisions.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: