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

Yalcin N., Kasikci M., Celik H. T., Allegaert K., Demirkan K., Yigit Ş., ...Daha Fazla

Frontiers in Pharmacology, cilt.14, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3389/fphar.2023.1151560
  • Dergi Adı: Frontiers in Pharmacology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: newborn, medication error, machine learning, drug safety, clinical pharmacy, data collection, adverse drug reaction
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.