Browsing by Subject "Decision Support Systems, Clinical"

Now showing 1 - 4 of 4
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    Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation
    (Oxford University Press, 2014-10) Russ, Alissa L.; Zillich, Alan J.; Melton, Brittany L.; Russell, Scott A.; Chen, Siying; Spina, Jeffrey R.; Weiner, Michael; Johnson, Elizabette G.; Daggy, Joanne K.; McAnas, M. Sue; Hawsey, Jason M.; Puleo, Anthony G.; Doebbeling, Bradley N.; Saleem, Jason J.; Medicine Faculty Volunteers, IU School of Medicine
    OBJECTIVE: To apply human factors engineering principles to improve alert interface design. We hypothesized that incorporating human factors principles into alerts would improve usability, reduce workload for prescribers, and reduce prescribing errors. MATERIALS AND METHODS: We performed a scenario-based simulation study using a counterbalanced, crossover design with 20 Veterans Affairs prescribers to compare original versus redesigned alerts. We redesigned drug-allergy, drug-drug interaction, and drug-disease alerts based upon human factors principles. We assessed usability (learnability of redesign, efficiency, satisfaction, and usability errors), perceived workload, and prescribing errors. RESULTS: Although prescribers received no training on the design changes, prescribers were able to resolve redesigned alerts more efficiently (median (IQR): 56 (47) s) compared to the original alerts (85 (71) s; p=0.015). In addition, prescribers rated redesigned alerts significantly higher than original alerts across several dimensions of satisfaction. Redesigned alerts led to a modest but significant reduction in workload (p=0.042) and significantly reduced the number of prescribing errors per prescriber (median (range): 2 (1-5) compared to original alerts: 4 (1-7); p=0.024). DISCUSSION: Aspects of the redesigned alerts that likely contributed to better prescribing include design modifications that reduced usability-related errors, providing clinical data closer to the point of decision, and displaying alert text in a tabular format. Displaying alert text in a tabular format may help prescribers extract information quickly and thereby increase responsiveness to alerts. CONCLUSIONS: This simulation study provides evidence that applying human factors design principles to medication alerts can improve usability and prescribing outcomes.
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    Decision Support from Local Data: Creating Adaptive Order Menus from Past Clinician Behavior
    (Elsevier, 2014-04) Klann, Jeffrey G.; Szolovits, Peter; Downs, Stephen; Schadow, Gunther; Department of Pediatrics, IU School of Medicine
    Objective Reducing care variability through guidelines has significantly benefited patients. Nonetheless, guideline-based clinical decision support (CDS) systems are not widely implemented or used, are frequently out-of-date, and cannot address complex care for which guidelines do not exist. Here, we develop and evaluate a complementary approach - using Bayesian network (BN) learning to generate adaptive, context-specific treatment menus based on local order-entry data. These menus can be used as a draft for expert review, in order to minimize development time for local decision support content. This is in keeping with the vision outlined in the US Health Information Technology Strategic Plan, which describes a healthcare system that learns from itself. Materials and Methods We used the Greedy Equivalence Search algorithm to learn four 50-node domain-specific BNs from 11,344 encounters: abdominal pain in the emergency department, inpatient pregnancy, hypertension in the urgent visit clinic, and altered mental state in the intensive care unit. We developed a system to produce situation-specific, rank-ordered treatment menus from these networks. We evaluated this system with a hospital-simulation methodology and computed Area Under the Receiver-Operator Curve (AUC) and average menu position at time of selection. We also compared this system with a similar association-rule-mining approach. Results A short order menu on average contained the next order (weighted average length 3.91–5.83 items). Overall predictive ability was good: average AUC above 0.9 for 25% of order types and overall average AUC .714–.844 (depending on domain). However, AUC had high variance (.50–.99). Higher AUC correlated with tighter clusters and more connections in the graphs, indicating importance of appropriate contextual data. Comparison with an association rule mining approach showed similar performance for only the most common orders with dramatic divergence as orders are less frequent. Discussion and Conclusion This study demonstrates that local clinical knowledge can be extracted from treatment data for decision support. This approach is appealing because: it reflects local standards; it uses data already being captured; and it produces human-readable treatment-diagnosis networks that could be curated by a human expert to reduce workload in developing localized CDS content. The BN methodology captured transitive associations and co-varying relationships, which existing approaches do not. It also performs better as orders become less frequent and require more context. This system is a step forward in harnessing local, empirical data to enhance decision support.
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    Effectiveness of a clinical decision support system for reducing the risk of QT interval prolongation in hospitalized patients
    (Ovid Technologies Wolters Kluwer - American Heart Association, 2014-05) Tisdale, James E.; Jaynes, Heather A.; Kingery, Joanna R.; Overholser, Brian R.; Mourad, Noha A.; Trujillo, Tate N.; Kovacs, Richard J.; Department of Medicine, IU School of Medicine
    BACKGROUND: We evaluated the effectiveness of a computer clinical decision support system (CDSS) for reducing the risk of QT interval prolongation in hospitalized patients. METHODS AND RESULTS: We evaluated 2400 patients admitted to cardiac care units at an urban academic medical center. A CDSS incorporating a validated risk score for QTc prolongation was developed and implemented using information extracted from patients' electronic medical records. When a drug associated with torsades de pointes was prescribed to a patient at moderate or high risk for QTc interval prolongation, a computer alert appeared on the screen to the pharmacist entering the order, who could then consult the prescriber on alternative therapies and implement more intensive monitoring. QTc interval prolongation was defined as QTc interval >500 ms or increase in QTc of ≥60 ms from baseline; for patients who presented with QTc >500 ms, QTc prolongation was defined solely as increase in QTc ≥60 ms from baseline. End points were assessed before (n=1200) and after (n=1200) implementation of the CDSS. CDSS implementation was independently associated with a reduced risk of QTc prolongation (adjusted odds ratio, 0.65; 95% confidence interval, 0.56-0.89; P<0.0001). Furthermore, CDSS implementation reduced the prescribing of noncardiac medications known to cause torsades de pointes, including fluoroquinolones and intravenous haloperidol (adjusted odds ratio, 0.79; 95% confidence interval, 0.63-0.91; P=0.03). CONCLUSIONS: A computer CDSS incorporating a validated risk score for QTc prolongation influences the prescribing of QT-prolonging drugs and reduces the risk of QTc interval prolongation in hospitalized patients with torsades de pointes risk factors.
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    Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network.
    (Springer, 2021-07) Ginsburg, Geoffrey S.; Cavallari, Larisa H.; Chakraborty, Hrishikesh; Cooper-DeHoff, Rhonda M.; Dexter, Paul R.; Eadon, Michael T.; Ferket, Bart S.; Horowitz, Carol R.; Johnson, Julie A.; Kannry, Joseph; Kucher, Natalie; Madden, Ebony B.; Orlando, Lori A.; Parker, Wanda; Peterson, Josh; Pratt, Victoria M.; Rakhra-Burris, Tejinder K.; Ramos, Michelle A.; Skaar, Todd C.; Sperber, Nina; Steen-Burrell, Kady-Ann; Van Driest, Sara L.; Voora, Deepak; Wiisanen, Kristin; Winterstein, Almut G.; Volpi, Simona
    PURPOSE: A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions. METHODS: The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation. RESULTS: Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression. CONCLUSION: IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.