An integration engineering framework for machine learning in healthcare.

ABSTRACT:

Background and objectives

Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare.

Methods

A

SUBMITTER: Assadi A 

PROVIDER: S-EPMC9386122 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature