Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: A prospective direct observational study.

ABSTRACT:

Objective

Neonates' physiological immaturity and complex dosing requirements heighten their susceptibility to medication administration errors (MAEs), with the potential for severe harm and substantial economic impact on healthcare systems. Developing an effective risk prediction model for MAEs is crucial to reduce and prevent harm.

Methods

This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and exp

SUBMITTER: Henry Basil J 

PROVIDER: S-EPMC11489987 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature