Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.
Front Pharmacol
; 14: 1151560, 2023.
Article
em En
| MEDLINE
| ID: mdl-37124199
ABSTRACT
Aim:
To develop models that predict the presence of medication errors (MEs) (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 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 (http//softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/).Conclusion:
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. Clinical Trial Registration ClinicalTrials.gov, identifier NCT04899960.
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Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article