Back

Navigating Medication Safety with Electronic Medical Records: Insights from a Dual-Phase Implementation in Paediatric, Neonatal and Maternity Care

Mordaunt, D. A.; Johnson, N.; Verghese, S.; Parker, R.; Gibb, K.; Palmer, L. J.

2024-12-05 public and global health
10.1101/2024.12.03.24318226 medRxiv
Show abstract

ObjectiveEMR implementations can lead to changes in medication safety events due to the disruption of clinical activities by the implementation. The current study aimed to evaluate the impact of an Electronic Medical Record (EMR) implementation on medication safety events within womens and childrens services of a large tertiary public hospital. MethodsThis Real-World Evidence (RWE) study utilised a differences-in-differences analysis with negative binomial regression to accommodate overdispersion in medication safety event counts. We compared change over time in key outcomes between areas where the EMR was activated and areas where it was not activated. Data were collected from January 2020 to February 2024 from the enterprise incident management system, spanning periods before and after two separate EMR system activations in 2021 and 2023. ResultsThere was an initial rise in minor and near-miss incidents immediately following activation, with no overall increase in events in groups not activated. The observed rise in incidents was in the time immediately around the activation and was not sustained over the longer term. There were no significant changes in trend over time. ConclusionsOur findings suggest that the implementation of the EMR system was not associated with a change in the occurrence of medication safety events over the longer term. Our study highlights the potential of EMR systems to be integrated into healthcare settings without worsening medication safety outcomes; implementation also doesnt appear to have improved rates.

Published in Australian Health Review · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.