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Electronic aids are revolutionalising the way we work. Information to support clinical decisions has never been more available and as advancements in electronic Medicines Management (eMM) systems continue to develop, the obvious question to ponder is, ‘How much information is too much?’
Natalie Page*, a PhD candidate who hopes to commence her thesis under Prof Westbrook at UNSW later this year, joins us to discuss the benefits of optimising drug to drug interactions in eMM systems.
Is there research to suggest that too much electronic information may be negatively impacting medication safety?
Most clinical decision support (CDS) alerts and prompts in electronic Medicines Management (eMM) systems demonstrate benefit in improving prescribing behavior and/or reducing error rates with some also demonstrating a positive impact on clinical outcomes. However many studies show that excessive generation of alerts may lead to alerts being ignored and that the opportunity to avoid preventable errors is being missed.
In terms of the functionality that users want from CDS alerts in eMM systems, Troiano et al.(2013) best sum this up: From a user perspective, the configuration of clinical decision support alerts for medications is conceptually simple: The computer should be programmed to present clinicians with relevant alerts at the proper time; irrelevant alerts are unhelpful and should be eliminated. When the rate of unhelpful alerts far exceeds the rate of relevant alerts, clinicians begin to get distracted and attribute their inattentiveness to alert fatigue.
Is it time to look at introducing standards for CDS alerts in eMM systems?
There is increasing commentary in the literature on the best way to optimise CDS in eMM systems to promote the notification of high-priority clinically-significant alerts and reduce interruptive notification from low- and moderate-priority alerts, particularly regarding drug-drug interactions. Various strategies have been trialed and suggested, and many international researchers are now calling on government and relevant stakeholders to centrally develop and maintain standards for the clinically relevant content used to enable and govern CDS alerts in eMM systems. Anecdotally, Australian sites that have implemented eMM systems are also calling on government agencies to develop such standards.
However further conclusive evidence is needed before these bodies can develop such standards. Whilst there is an extensive and growing body of research on EMM, there is a high degree of heterogeneity in published studies (including differences in software and CDS functionality, and in methodology) making it difficult to compare the effectiveness of eMM studies and of CDS interventions, or to make consensus recommendations.
To date there has been little or no randomisation studies of eMM as an intervention arm; the majority of published eMM studies have used pre–post designs, and whilst suitable for program evaluation, this is “the least robust of available quasi-experimental designs” (Weir, Staggers, & Phansalkar, 2009).
Nor have there been many studies looking at the effect of eMM over several years as part of a staged implementation across a health service. Such studies could seek to answer the question of whether there is an outcome plateau or continual improvement on measures such as medication safety, the impact on clinician work practices, and economic impact. Similarly they may help to identify the best way to configure CDS (within the limited range of options available from commercial eMM vendors) to further positively impact on these measures.
How will your study address these measures?
Our group proposes to investigate the impact of eMM and CDS configuration using a cluster-randomised trial methodology. Following the implementation of eMM across the health service over time, we will progressively measure the outcomes as we randomly rollout to each new cluster as they crossover from control to intervention arm. We hope to be reporting on the outcomes of this study over the coming years to the Annual Electronic Medication Management Conference.
Troiano, D., Jones, M. A., Smith, A. H., Chan, R. C., Laegeler, A. P., Le, T., . . . Chaffee, B. W. (2013). The need for collaborative engagement in creating clinical decision-support alerts. American Journal Of Health-System Pharmacy: AJHP: Official Journal Of The American Society Of Health-System Pharmacists, 70(2), 150-153. doi: 10.2146/ajhp120435
Weir, C. R., Staggers, N., & Phansalkar, S. (2009). The state of the evidence for computerized provider order entry: a systematic review and analysis of the quality of the literature. Int J Med Inform, 78(6), 365-374. doi: 10.1016/j.ijmedinf.2008.12.001