Assessing the impact of a mixed intervention model on the reduction of medication administration errors in an Australian hospital

Author(s):  
Viviane Khalil ◽  
Antoinette Bates
2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i9-i9
Author(s):  
C L Tolley ◽  
N W Watson ◽  
A Heed ◽  
J Einbeck ◽  
S Medows ◽  
...  

Abstract 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.


2016 ◽  
Vol 36 (4) ◽  
pp. 19-35 ◽  
Author(s):  
Fran Flynn ◽  
Julie Q. Evanish ◽  
Josephine M. Fernald ◽  
Dawn E. Hutchinson ◽  
Cheryl Lefaiver

Background Because of the high frequency of interruptions during medication administration, the effectiveness of strategies to limit interruptions during medication administration has been evaluated in numerous quality improvement initiatives in an effort to reduce medication administration errors. Objectives To evaluate the effectiveness of evidence-based strategies to limit interruptions during scheduled, peak medication administration times in 3 progressive cardiac care units (PCCUs). A secondary aim of the project was to evaluate the impact of limiting interruptions on medication errors. Methods The percentages of interruptions and medication errors before and after implementation of evidence-based strategies to limit interruptions were measured by using direct observations of nurses on 2 PCCUs. Nurses in a third PCCU served as a comparison group. Results Interruptions (P < .001) and medication errors (P = .02) decreased significantly in 1 PCCU after implementation of evidence-based strategies to limit interruptions. Avoidable interruptions decreased 83% in PCCU1 and 53% in PCCU2 after implementation of the evidence-based strategies. Conclusions Implementation of evidence-based strategies to limit interruptions in PCCUs decreases avoidable interruptions and promotes patient safety.


Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.


2021 ◽  
pp. 106002802199964
Author(s):  
Matthew D. Jones ◽  
Jonathan Clarke ◽  
Calandra Feather ◽  
Bryony Dean Franklin ◽  
Ruchi Sinha ◽  
...  

Background: In a recent human reliability analysis (HRA) of simulated pediatric resuscitations, ineffective retrieval of preparation and administration instructions from online injectable medicines guidelines was a key factor contributing to medication administration errors (MAEs). Objective: The aim of the present study was to use a specific HRA to understand where intravenous medicines guidelines are vulnerable to misinterpretation, focusing on deviations from expected practice ( discrepancies) that contributed to large-magnitude and/or clinically significant MAEs. Methods: Video recordings from the original study were reanalyzed to identify discrepancies in the steps required to find and extract information from the NHS Injectable Medicines Guide (IMG) website. These data were combined with MAE data from the same original study. Results: In total, 44 discrepancies during use of the IMG were observed across 180 medication administrations. Of these discrepancies, 21 (48%) were associated with an MAE, 16 of which (36% of 44 discrepancies) made a major contribution to that error. There were more discrepancies (31 in total, 70%) during the steps required to access the correct drug webpage than there were in the steps required to read this information (13 in total, 30%). Discrepancies when using injectable medicines guidelines made a major contribution to 6 (27%) of 22 clinically significant and 4 (15%) of 27 large-magnitude MAEs. Conclusion and Relevance: Discrepancies during the use of an online injectable medicines guideline were often associated with subsequent MAEs, including those with potentially significant consequences. This highlights the need to test the usability of guidelines before clinical use.


2010 ◽  
Vol 95 (2) ◽  
pp. 113-118 ◽  
Author(s):  
M. A. Ghaleb ◽  
N. Barber ◽  
B. D. Franklin ◽  
I. C. K. Wong

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