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=14

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=<p><bold>Aim:</bold> To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients.</p><p><bold>Design:</bold> Prospective, observational cohort study randomized with machine learning (ML) algorithms.</p><p><bold>Setting:</bold> A 22-bed capacity NICU in Ankara, Turkey, between February 2020 and July 2021.</p><p><bold>Results:</bold> A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/day) who received 2,280 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-min APGAR score, postnatal age, alimentary tract and metabolism drugs, and respiratory system drugs as patient-related parameters, and 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 online (<ext-link ext-link-type="uri" xlink:href="http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/" xmlns:xlink="http://www.w3.org/1999/xlink">http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/</ext-link>).</p><p><bold>Conclusion:</bold> This is the first developed and validated model to predict the 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.</p><p><bold>Clinical Trial Registration:</bold><ext-link ext-link-type="uri" xlink:href="http://ClinicalTrials.gov" xmlns:xlink="http://www.w3.org/1999/xlink">ClinicalTrials.gov</ext-link>, identifier NCT04899960.</p>