Medication Administration Errors Among Neonates In The Neonatal Intensive Care Units

• By: MOHAMED SHAH, Noraida (Universiti Kebangsaan Malaysia, Malaysia)
• Co-author(s): Dr Noraida Mohamed Shah (Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia)
  Ms. Jospehine Henry Basil (Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia)
  Dr. Adliah Mhd Ali (Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia)
  Dr. Nurul Ain Mohd Tahir (Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia)
  Dr. Chandini Menon Premakumar (Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia)
  Dr. Wern Han Lim (School of Information Technology, Monash University, Bandar Sunway, Selangor)
  Dr. Shareena Ishak (Department of Paediatrics, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia)
  
• Abstract:

  Introduction: Medication administration errors (MAEs) are the most common type of medication error. They are more common among neonates as compared with adults. These errors pose significant risks to patients and impose substantial economic burden on the healthcare system. Targeting and prioritizing neonates at high risk of MAEs is crucial in reducing these errors. Therefore, the overall aim of this study was to develop and validate a risk prediction model for identifying neonates at risk of MAEs.
  Method: This was a four-phase study. Firstly, a systematic review and meta-analysis was conducted to critically appraise the evidence on the prevalence and causes of MAEs among neonates in neonatal intensive care units (NICUs). Secondly, a cross-sectional study using a validated self-administered questionnaire was carried out to determine the estimated percentage of MAE reporting and describe the reasons for the occurrence of MAEs from nurses’ perspective in five Malaysian public hospital NICUs. Thirdly, a prospective direct observational was conducted at the same study sites to determine the prevalence of MAEs and identify factors associated with MAEs. Lastly, a machine-learning (ML) model was developed and validated for the identification of MAEs among neonates in the NICU using the factors identified in the first three phases.
  Results: In the first phase, the pooled prevalence of MAEs for direct observation and non-direct observation studies was 59.3% and 64.8%, respectively, with error-provoking environments identified as the most common cause. Based on the second phase, the estimated percentage of MAE reporting was 30.6%, with inadequate nursing staff, drugs which look alike and with similar drug packaging among the common reasons for MAEs. The error rate recorded in the third phase was 68.0%, affecting a large population of neonates in the NICU (92.4%). Factors significantly associated with MAEs were medications administered intravenously (AOR = 21.18; 95% CI = 13.35-33.61; p<0.001), unavailability of a protocol related to the preparation and administration of medications (AOR = 2.43; 95% CI = 1.54-3.84; p<0.001), the number of prescribed medications (AOR = 1.11; 95% CI = 1.01-1.23; p=0.048), nursing experience (AOR = 1.07; 95% CI = 1.04-1.11; p<0.001), non-ventilated neonates (AOR=2.03; 95% CI=1.13-3.64; p=0.018), and gestational age in weeks (AOR=0.94; 95% CI=0.91-0.97; p<0.001). In the final phase, ten ML algorithms were assessed, and adaptive boosting (AdaBoost) was found to be 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%, negative predictive value: 64.00%). The most influential features in AdaBoost were intravenous route of administration, followed by working hours and nursing experience.
  Conclusion: The findings from this research highlighted the burden of MAEs among neonates in NICUs and factors significantly associated with these errors. The developed and validated model could potentially prevent MAEs by identifying neonates at risk of these errors, leading to a reduction in patient harm and positively impacting the healthcare system.