Development and validation of a risk prediction model for medication administration errors among neonates in the neonatal intensive care unit: a study protocol

• Published in: BMJ paediatrics open Vol. 7; no. 1; p. e001765
• Main Authors: Henry Basil, Josephine, Premakumar, Chandini Menon, Mhd Ali, Adliah, Mohd Tahir, Nurul Ain, Seman, Zamtira, Mohamed Shah, Noraida
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd 01.02.2023; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: Adult; Data collection; Drug administration; Drug dosages; Female; Humans; Infant, Newborn; Intensive care; Intensive Care Units, Neonatal; Male; Medical errors; Medication Errors - prevention & control; Neonatal care; Neonatology; Newborn babies; Nurses; Observational studies; Observational Studies as Topic; Patients; Pediatrics; Pharmaceutical Preparations; Prospective Studies; Protocol; Reproducibility of Results; Risk factors; United States > US; Malaysia; Neonatology
• ISSN: 2399-9772; 2399-9772
• DOI: 10.1136/bmjpo-2022-001765

IntroductionMedication administration errors (MAEs) are the most common type of medication error. Furthermore, they are more common among neonates as compared with adults. MAEs can result in severe patient harm, subsequently causing a significant economic burden to the healthcare system. Targeting and prioritising neonates at high risk of MAEs is crucial in reducing MAEs. To the best of our knowledge, there is no predictive risk score available for the identification of neonates at risk of MAEs. Therefore, this study aims to develop and validate a risk prediction model to identify neonates at risk of MAEs.Methods and analysisThis is a prospective direct observational study that will be conducted in five neonatal intensive care units. A minimum sample size of 820 drug preparations and administrations will be observed. Data including patient characteristics, drug preparation-related and administration-related information and other procedures will be recorded. After each round of observation, the observers will compare his/her observations with the prescriber’s medication order, hospital policies and manufacturer’s recommendations to determine whether MAE has occurred. To ensure reliability, the error identification will be independently performed by two clinical pharmacists after the completion of data collection for all study sites. Any disagreements will be discussed with the research team for consensus. To reduce overfitting and improve the quality of risk predictions, we have prespecified a priori the analytical plan, that is, prespecifying the candidate predictor variables, handling missing data and validation of the developed model. The model’s performance will also be assessed. Finally, various modes of presentation formats such as a simplified scoring tool or web-based electronic risk calculators will be considered.