A survey of extant organizational and computational setups for deploying predictive models in health systems.

ABSTRACT:

Objective

Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.

Materials and methods

We conducted structured interviews with health systems with AI deployment experience about their organizational and computational setups for deploying AI-CDS at point of care.

Results

We contacted 34 health systems and interviewed 20 healthcare sites (58% response rate). Twelve (60%) sites used

SUBMITTER: Kashyap S 

PROVIDER: S-EPMC8510384 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature