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Prevalence, Causes and Severity of Medication Administration Errors in the Neonatal Intensive Care Unit: A Systematic Review and Meta-Analysis

Henry Basil J, Premakumar CM, Mhd Ali A, Mohd Tahir NA, Mohamed Shah N

Drug Saf, 2022 Dec;45(12):1457-1476.
PMID: 36192535 DOI: 10.1007/s40264-022-01236-6

INTRODUCTION: Neonates are at greater risk of preventable adverse drug events as compared to children and adults.
OBJECTIVE: This study aimed to estimate and critically appraise the evidence on the prevalence, causes and severity of medication administration errors (MAEs) amongst neonates in Neonatal Intensive Care Units (NICUs).
METHODS: A systematic review and meta-analysis was conducted by searching nine electronic databases and the grey literature for studies, without language and publication date restrictions. The pooled prevalence of MAEs was estimated using a random-effects model. Data on error causation were synthesised using Reason's model of accident causation.
RESULTS: Twenty unique studies were included. Amongst direct observation studies reporting total opportunity for errors as the denominator for MAEs, the pooled prevalence was 59.3% (95% confidence interval [CI] 35.4-81.3, I2 = 99.5%). Whereas, the non-direct observation studies reporting medication error reports as the denominator yielded a pooled prevalence of 64.8% (95% CI 46.6-81.1, I2 = 98.2%). The common reported causes were error-provoking environments (five studies), while active failures were reported by three studies. Only three studies examined the severity of MAEs, and each utilised a different method of assessment.
CONCLUSIONS: This is the first comprehensive systematic review and meta-analysis estimating the prevalence, causes and severity of MAEs amongst neonates. There is a need to improve the quality and reporting of studies to produce a better estimate of the prevalence of MAEs amongst neonates. Important targets such as wrong administration-technique, wrong drug-preparation and wrong time errors have been identified to guide the implementation of remedial measures.
Fulltext Development and validation of a risk prediction model for medication administration errors among neonates in the neonatal intensive care unit: a study protocol

Henry Basil J, Premakumar CM, Mhd Ali A, Mohd Tahir NA, Seman Z, Mohamed Shah N

BMJ Paediatr Open, 2023 Feb;7(1).
PMID: 36754439 DOI: 10.1136/bmjpo-2022-001765

INTRODUCTION: Medication 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 ANALYSIS: This 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.
Fulltext Clinical and economic impact of medication administration errors among neonates in neonatal intensive care units

Henry Basil J, Mohd Tahir NA, Menon Premakumar C, Mhd Ali A, Seman Z, Ishak S, et al.

PLoS One, 2024;19(7):e0305538.
PMID: 38990851 DOI: 10.1371/journal.pone.0305538

Despite efforts in improving medication safety, medication administration errors are still common, resulting in significant clinical and economic impact. Studies conducted using a valid and reliable tool to assess clinical impact are lacking, and to the best of our knowledge, studies evaluating the economic impact of medication administration errors among neonates are not yet available. Therefore, this study aimed to determine the potential clinical and economic impact of medication administration errors in neonatal intensive care units and identify the factors associated with these errors. A national level, multi centre, prospective direct observational study was conducted in the neonatal intensive care units of five Malaysian public hospitals. The nurses preparing and administering the medications were directly observed. After the data were collected, two clinical pharmacists conducted independent assessments to identify errors. An expert panel of healthcare professionals assessed each medication administration error for its potential clinical and economic outcome. A validated visual analogue scale was used to ascertain the potential clinical outcome. The mean severity index for each error was subsequently calculated. The potential economic impact of each error was determined by averaging each expert's input. Multinomial logistic regression and multiple linear regression were used to identify factors associated with the severity and cost of the errors, respectively. A total of 1,018 out of 1,288 (79.0%) errors were found to be potentially moderate in severity, while only 30 (2.3%) were found to be potentially severe. The potential economic impact was estimated at USD 27,452.10. Factors significantly associated with severe medication administration errors were the medications administered intravenously, the presence of high-alert medications, unavailability of a protocol, and younger neonates. Moreover, factors significantly associated with moderately severe errors were intravenous medication administration, younger neonates, and an increased number of medications administered. In the multiple linear regression analysis, the independent variables found to be significantly associated with cost were the intravenous route of administration and the use of high-alert medications. In conclusion, medication administration errors were judged to be mainly moderate in severity costing USD 14.04 (2.22-22.53) per error. This study revealed important insights and highlights the need to implement effective error reducing strategies to improve patient safety among neonates in the neonatal intensive care unit.
Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: A prospective direct observational study

Henry Basil J, Lim WH, Syed Ahmad SM, Menon Premakumar C, Mohd Tahir NA, Mhd Ali A, et al.

Digit Health, 2024;10:20552076241286434.
PMID: 39430694 DOI: 10.1177/20552076241286434

OBJECTIVE: Neonates' physiological immaturity and complex dosing requirements heighten their susceptibility to medication administration errors (MAEs), with the potential for severe harm and substantial economic impact on healthcare systems. Developing an effective risk prediction model for MAEs is crucial to reduce and prevent harm.
METHODS: This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. Model performance, prioritising F1-score for MAEs, was evaluated using various measures. Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms.
RESULTS: A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. F1-scores for the ten models ranged from 76.15% to 83.28%. Adaptive boosting (AdaBoost) emerged as the best-performing model (F1-score: 83.28%, accuracy: 77.63%, area under the receiver operating characteristic: 82.95%, precision: 84.72%, sensitivity: 81.88% and negative predictive value: 64.00%). The most influential features in AdaBoost were the intravenous route of administration, working hours, and nursing experience.
CONCLUSIONS: This study developed and validated an ML-based model to predict the presence of MAEs among neonates in NICUs. AdaBoost was identified as the best-performing algorithm. Utilising the model's predictions, healthcare providers can potentially reduce MAE occurrence through timely interventions.