Abstract
BackgroundAs a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site.ObjectiveThe purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions.MethodsThe MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized.ResultsCompared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit.ConclusionsThe execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites.
Highlights
Patient safety is maximized when medical errors are efficiently detected and mitigated or prevented in the first place
The most common type of medical errors are medication errors, which are defined as any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer [1]
Common informatics approaches to prevent errors include the use of dedicated systems such as clinical decision support during medication ordering in the electronic health record (EHR) or drug error reduction systems contained in smart infusion pumps; both provide overdose and other types of alerts [9,10]
Summary
Patient safety is maximized when medical errors are efficiently detected and mitigated or prevented in the first place. Common informatics approaches to prevent errors include the use of dedicated systems such as clinical decision support during medication ordering in the electronic health record (EHR) or drug error reduction systems contained in smart infusion pumps; both provide overdose and other types of alerts [9,10]. The former system works to detect errors and reduce the total number of medication errors early in the medication use process (at the ordering stage) [11], but does not detect error types that are introduced downstream in the later phases such as medication administration. In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site
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