Machine learning-based prediction of low-value care for hospitalized patients.

ABSTRACT:

Objective

Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision.

Methods

We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical feat

SUBMITTER: King AJ 

PROVIDER: S-EPMC10735238 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature