Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning-Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders.

ABSTRACT:

Background

Peripheral arterial disease (PAD) is underdiagnosed, partially due to a high prevalence of atypical symptoms and a lack of physician and patient awareness. Implementing clinical decision support tools powered by machine learning algorithms may help physicians identify high-risk patients for diagnostic workup.

Objective

This study aims to evaluate barriers and facilitators to the implementation of a novel machine learning-based screening tool for PAD among physician and patient stakeholders using the Consolidated Framework for Implementation Research (CFIR).

Methods

We performed semistructured interviews with physicians and patients from the Stanford University Department of Primary Care and Population Health, Division of Cardiology, and Division of Vascular

SUBMITTER: Ho V 

PROVIDER: S-EPMC10660241 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature