The Impact of a Bedside Medication Scanning Device on Administration Errors in the Hospital Setting: A Prospective Observational Study
Authors: Tolley C L , Watson N W , Heed A , Einbeck J , Medows S , Wood L , Campbell L , Slight S P 
Published: March 2021

Introduction

The medication administration process is complex and influenced by interruptions, multi-tasking and responding to patient’s needs and is consequently prone to errors.1 Over half (54.4%) of the 237 million medication errors estimated to have occurred in England each year were found to have taken place at the administration stage and 7.6% were associated with moderate or severe harm. The implementation of a Closed Loop Medication Administration solution aims to reduce medication administration errors and prevent patient harm.

Aim

We conducted the first evaluation to assess the impact of a novel optical medication scanning device, MedEye, on the rate of medication administration errors in solid oral dosage forms.

Methods

We performed a before and after study on one ward at a tertiary-care teaching hospital that used a commercial electronic prescribing and medication administration system and was implementing MedEye (a bedside tool for stopping and preventing medication administration errors). Pre-MedEye data collection occurred between Aug-Nov 2019 and post-MedEye data collection occurred between Feb-Mar 2020. We conducted direct observations of nursing drug administration rounds before and after the MedEye implementation. Observers recorded what they observed being administered (e.g., drug name, form, strength and quantity) and compared this to what was prescribed. Errors were classified as either a ‘timing’ error, ‘omission’ error or ‘other’ error. We calculated the rate and type of medication administration errors (MAEs) before and after the MedEye implementation. A sample size calculation suggested that approximately 10,000 medication administrations were needed. Data collection was reduced due to the COVID 19 pandemic and implementation delays.

Results

Trained pharmacists or nurses observed a total of 1,069 administrations of solid oral dosage forms before and 432 after the MedEye intervention was implemented. The percentage of MAEs pre-MedEye (69.1%) and post-MedEye (69.9%) remained almost the same. Non-timing errors (combination of ‘omission’ + ‘other’ errors) reduced from 51 (4.77%) to 11 (2.55%), which had borderline significance (p=0.05) however after adjusting for confounders, significance was lost. We also saw a non-significant reduction in ‘other’ error types (e.g., dose and documentation errors) following the implementation of MedEye from 34 (3.2%) to 7 (1.62%). An observer witnessed a nurse dispense the wrong medication (prednisolone) instead of the intended medication (furosemide) in the post-MedEye period. After receiving a notification from MedEye that an unexpected medication had been dispensed, the nurse corrected the dose thus preventing an error. We also identified one instance where the nurse correctly dispensed a prescribed medication (amlodipine) but this was mistakenly identified by the MedEye scanner as another prescribed medication (metoclopramide).

Conclusions

This is the first evaluation of a novel optical medication scanning device, MedEye on the rate of MAEs in one of the largest NHS trusts in England. We found a non-statistically significant reduction in non-timing error rates. This was notable because incidents within this category e.g., dose errors, are more likely to be associated with harm compared to timing errors.2 However, further research is needed to investigate the impact of MedEye on a larger sample size and range of medications.

References

1. Elliott, R., et al., Prevalence and economic burden of medication errors in the NHS in England. Rapid evidence synthesis and economic analysis of the prevalence and burden of medication error in the UK, 2018.

2. Poon, E.G., et al., Effect of bar-code technology on the safety of medication administration. New England Journal of Medicine, 2010. 362(18): p. 1698–1707.