Dataset Information

Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection.

ABSTRACT: BACKGROUND:The Veterans Affairs Portland Healthcare System developed a medication history collection software that displays prescription names and medication images. OBJECTIVE:This article measures the frequency of medication discrepancy reporting using the medication history collection software and compares with the frequency of reporting using a paper-based process. This article also determines the accuracy of each method by comparing both strategies to a best possible medication history. STUDY DESIGN:Randomized, controlled, single-blind trial. SETTING:Three community-based primary care clinics associated with the Veterans Affairs Portland Healthcare System: a 300-bed teaching facility and ambulatory care network serving Veteran soldiers in the Pacific Northwest United States. PARTICIPANTS:Of 212 patients with primary care appointments, 209 patients fulfilled the study requirements. INTERVENTION:Patients randomized to a software-directed medication history or a paper-based medication history. Randomization and allocation to treatment groups were performed using a computer-based random number generator. Assignments were placed in a sealed envelope and opened after participant consent. The research coordinator did not know or have access to the treatment assignment until the time of presentation. MAIN OUTCOME MEASURES:The primary analysis compared the discrepancy detection rates between groups with respect to the health record and a best possible medication history. RESULTS:Of 3,500 medications reviewed, we detected 1,435 discrepancies. Forty-six percent of those discrepancies were potentially high risk for causing an adverse drug event. There was no difference in detection rates between treatment arms. Software sensitivity was 83% and specificity was 91%; paper sensitivity was 81% and specificity was 94%. No participants were lost to follow-up. CONCLUSION:The medication history collection software is an efficient and scalable method for gathering a medication history and detecting high-risk discrepancies. Although it included medication images, the technology did not improve accuracy over a paper list when compared with a best possible medication history. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02135731.

SUBMITTER: Lesselroth BJ

PROVIDER: S-EPMC5931918 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection.

Lesselroth Blake J BJ Adams Kathleen K Church Victoria L VL Tallett Stephanie S Russ Yelizaveta Y Wiedrick Jack J Forsberg Christopher C Dorr David A DA

Applied clinical informatics 20180401 2

<h4>Background</h4>The Veterans Affairs Portland Healthcare System developed a medication history collection software that displays prescription names and medication images.<h4>Objective</h4>This article measures the frequency of medication discrepancy reporting using the medication history collection software and compares with the frequency of reporting using a paper-based process. This article also determines the accuracy of each method by comparing both strategies to a best possible medicatio ...[more]

PMID: 29719884

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Project description:BackgroundIn this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data.MethodsWe collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children's Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated, gold-standard set of medication reconciliation data was created for this collection. We then developed a hybrid algorithm consisting of three processes: (1) a ML algorithm to identify medication entities from clinical notes, (2) a rule-based method to link medication names with their attributes, and (3) a NLP-based, hybrid approach to match medications with structured prescriptions in order to detect medication discrepancies. The performance was validated on the gold-standard medication reconciliation data, where precision (P), recall (R), F-value (F) and workload were assessed.ResultsThe hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage. The medication matching achieved 92.4%/90.7%/91.5% (P/R/F) on identifying matched medications in the gold-standard and 88.6%/82.5%/85.5% (P/R/F) on discrepant medications. By combining all processes, the algorithm achieved 92.4%/90.7%/91.5% (P/R/F) and 71.5%/65.2%/68.2% (P/R/F) on identifying the matched and the discrepant medications, respectively. The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection.ConclusionBy leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.

Project description: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.

Project description:Accurate medication reconciliation at the time of hospital admission is vital to preventing adverse drug events. Compliance with medication reconciliation in our pediatric intensive care unit was low initially with overall medication reconciliation at 70%. Due to the high front line provider turnover in our unit, we focused on technological reminders for completion and used unique and innovative ways to motivate our supervising staff. Our goal was to reach >95% completion within 24 hours for medication reconciliation for all patients admitted to the pediatric intensive care unit. Pre-pilot discussions focused on examples of errors of medication reconciliation within our own institution resulting in patient harm via traditional power point presentation. The initial pilot phase instituted a job aid on how to add the medication reconciliation completion reminder column. Email updates on completion status began one week after initiation. During the implementation and spread phase, fun interactive videos were used to acknowledge roll out to the full unit. Compliance was monitored and humorous biweekly video updates emphasizing accountability were introduced. In the sustain phase, monthly video updates served as education and a reminder for provider staff. The use of a medication reconciliation completion column and reminder emails resulted in goal completion (>95%) by three weeks post intervention for the pilot unit. Overall medication reconciliation completion also reached goal of >95% completion within the entire unit after three weeks of implementation and spread. Compliance fell below goal so video updates were modified utilizing humor and emphasizing accountability. This resulted in sustained compliance now more than forty weeks post implementation. Unit compliance currently remains >95% completion within 24 hours now more than forty weeks post intervention. The use of the medication reconciliation reminder column resulted in improvement in compliance; however, that improvement was not sustained. The addition of humorous videos highlighting accountability allowed for sustained improvements.

Dataset Information

Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection.

Publications

Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection.

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