AUTHOR=Yalçın Nadir , Kaşıkcı Merve , Çelik Hasan Tolga , Allegaert Karel , Demirkan Kutay , Yiğit Şule , Yurdakök Murat 

TITLE=Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1151560

DOI=10.3389/fphar.2023.1151560

ISSN=1663-9812

ABSTRACT=Aim: To develop models that predict medication errors (MEs) presence (prescription, preparation, administration, and monitoring) using machine learning in NICU patients.
Design: Prospective, observational cohort study randomized with machine learning (ML) algorithms.
Setting: A 22-bed capacity NICU in Ankara, Turkey between February 2020 and July 2021.
Results: A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/day) who received 2280 prescriptions over 32,925 patient days were analyzed. At least one physician-related ME and nurse-related ME were found in 174 (42.2%) and 235 (57.0%) of the patients, respectively. The parameters that had the highest correlation with ME occurrence and subsequently included in the model were: total number of drugs, anti-infective drugs, nervous system drugs, 5-minute APGAR score, postnatal age, alimentary tract and metabolism drugs, and respiratory system drugs as patient-related, as well weekly working hours of nurses, weekly working hours of physicians, and number of nurses' monthly shifts as care provider-related parameters. The obtained model showed high performance to predict ME (AUC: 0.920; 95% CI 0.876-0.970) presence, and is accessible (http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/) online.
Conclusion: This is the first developed and validated model to predict presence of ME using work environment and pharmacotherapy parameters with high-performance ML algorithms in NICU patients. This approach and the current model hold the promise of implementation of targeted/precision screening to prevent MEs in neonates.