A framework for the oversight and local deployment of safe and high-quality prediction models

TLDR

Artificial intelligence and machine learning models are rapidly developed and deployed in clinical practice, yet many lack clear understanding of clinical impact and monitoring plans, leading to safety concerns and a lack of consensus on governance. The authors aim to describe a governance framework that combines regulatory best practices with lifecycle management for predictive models in clinical care. The framework integrates current regulatory guidelines and systematic lifecycle management to oversee deployment, pilot testing, and monitoring of algorithms within healthcare workflows. Since January 2021, the framework has been applied to 52 models, successfully adding them to the governance portfolio and managing them.

Abstract

Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.