National Surveillance of Emergency Department Visits for Outpatient Adverse Drug Events

Budnitz, Daniel S.; Pollock, Daniel A.; Weidenbach, Kelly N.; Mendelsohn, Aaron B.; Schroeder, Thomas J.; Annest, Joseph L.

doi:10.1001/jama.296.15.1858

Cited by 761 publications

(540 citation statements)

References 39 publications

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“…Warfarin is a commonly prescribed drug with pharmacogenomic implications due to its narrow window for effectiveness and high incidence of bleeding complications [14]. The variability in warfarin response is partially due to genetic differences in the cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) [15].…”

Section: Background and Significancementioning

confidence: 99%

“…Therefore, the implementation of personalized medicine has largely relied on mechanisms to collect and interpret large amounts of information through computerized systems, such as clinical decision support systems (CDSS); defined as any tool or system which provides filtered clinical and patient-related information to clinicians or patients in order to assist in patient care [10,11]. However, there have been limited assessments of CDSSs on perspectives, usability, and user satisfaction with no data available in pharmacists despite their role in personalized medicine [12,13].Warfarin is a commonly prescribed drug with pharmacogenomic implications due to its narrow window for effectiveness and high incidence of bleeding complications [14]. The variability in warfarin response is partially due to genetic differences in the cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) [15].…”

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confidence: 99%

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Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing

Melton¹,

Zillich²,

Saleem³

et al. 2016

Appl Clin Inform

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SummaryObjective: Pharmacogenomic-guided dosing has the potential to improve patient outcomes but its implementation has been met with clinical challenges. Our objective was to develop and evaluate a clinical decision support system (CDSS) for pharmacogenomic-guided warfarin dosing designed for physicians and pharmacists. Methods: Twelve physicians and pharmacists completed 6 prescribing tasks using simulated patient scenarios in two iterations (development and validation phases) of a newly developed pharmacogenomic-driven CDSS prototype. For each scenario, usability was measured via efficiency, recorded as time to task completion, and participants' perceived satisfaction which were compared using Kruskal-Wallis and Mann Whitney U tests, respectively. Debrief interviews were conducted and qualitatively analyzed. Usability findings from the first (i.e. development) iteration were incorporated into the CDSS design for the second (i.e. validation) iteration. Results: During the CDSS validation iteration, participants took more time to complete tasks with a median (IQR) of 183 (124-247) seconds versus 101 (73.5-197) seconds in the development iteration (p=0.01). This increase in time on task was due to the increase in time spent in the CDSS corresponding to several design changes. Efficiency differences that were observed between pharmacists and physicians in the development iteration were eliminated in the validation iteration. The increased use of the CDSS corresponded to a greater acceptance of CDSS recommended doses in the validation iteration (4% in the first iteration vs. 37.5% in the second iteration, p<0.001). Overall satisfaction did not change statistically between the iterations but the qualitative analysis revealed greater trust in the second prototype. Conclusions: A pharmacogenomic-guided CDSS has been developed using warfarin as the test drug. The final CDSS prototype was trusted by prescribers and significantly increased the time using the tool and acceptance of the recommended doses. This study is an important step toward incorporating pharmacogenomics into CDSS design for clinical testing. Research ArticleBL Background And SignificancePersonalized medicine is an emerging field with the fundamental objective to predict a patient's response in order to individualize drug therapy for improved medication effectiveness and safety. Pharmacogenomic-guided drug therapy is at the forefront of personalized medicine with over 150 United States Food and Drug Administration (FDA) approved drug labels incorporating pharmacogenomic information.[1] Nonetheless, the translation of pharmacogenomic testing into routine clinical practice has been slow due to several obstacles towards its implementation [2][3][4]. Currently, the routine use of clinical pharmacogenomics is limited to centers with resources to overcome these obstacles [5][6][7][8][9].A major obstacle in the implementation of clinical pharmacogenomics is the rapid pace that science has identified genetic polymorphisms that can predict drug response. Th...

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Section: Background and Significancementioning

confidence: 99%

mentioning

confidence: 99%

Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing

Melton¹,

Zillich²,

Saleem³

et al. 2016

Appl Clin Inform

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“…When a new drug is marketed, an active surveillance program might be established to monitor prospectively for safety signals that were too rare to appear in the typical clinical trial or, in some cases, to look for events that were hinted at in the clinical trials, but that did not have sufficient statistical power to definitively identify; this type of surveillance is drug-based. Certain settings like emergency departments (ED) can be used to look to see what drugrelated events are presenting to ED (15). Anaphylaxis is a good example of a critical event that would be seen in the ED and might be related to a drug.…”

Section: Active Surveillance and Other Enhanced Reporting Systemsmentioning

confidence: 99%

“…This data source can be used to identify new safety signals or can also provide information about the public health impact of know adverse events. A recent summary of this program's active surveillance findings for 2004 and 2005 showed that allergic reactions were the most common adverse event treated in EDs followed by unintentional overdoses (15).…”

Section: Enhanced Surveillance Based On Certain Settings National Elementioning

confidence: 99%

Informatic Tools and Approaches in Postmarketing Pharmacovigilance Used by FDA

Weaver

Willy

Avigan

2008

AAPS J

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Abstract. The safety profile of newly approved drugs and therapeutic biologics is less well developed by pre-marketing clinical testing than is the efficacy profile. The full safety profile of an approved product is established during years of clinical use. For nearly 40 years, the FDA has relied on the voluntary reporting of adverse events by healthcare practitioners and patients to help establish the safety of marketed products. Epidemiologic studies, including case series, secular trends, case-control and cohort studies, are used to supplement the investigation of a safety signal. Ideally, active surveillance systems would supplement the identification and exploration of safety signals. The FDA has implemented a number of initiatives to help identify safety problems with drugs and continues to evaluate their efforts.

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“…On average, 50% of older adults do not take their medications as prescribed (Haynes, McKibbon, & Kanani, 1996). Moreover, over one third of older adults' hospital admissions are due to adverse drug events, often reflecting medication misuse (Budnitz, Pollock, Weidenbach, Mendelsohn, Schroeder, & Annest, 2006). Among other factors, nonadherence has been linked to age-related differences in cognitive function and health literacy combined with inadequate collaboration between patients and providers, leading to calls to improve self-care by supporting patient/provider collaboration and planning (for review see Aspden, Wolcott, Bootman, & Croenwett, 2007).…”

mentioning

confidence: 99%

External support for collaborative problem solving in a simulated provider/patient medication scheduling task.

Morrow¹,

Raquel²,

Schriver³

et al. 2008

Journal of Experimental Psychology: Applied

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Taking medication requires developing plans to accomplish the activity. This planning challenges older adults because of age-related cognitive limits and inadequate collaboration with health providers. The authors investigated whether an external aid (medtable) supports collaborative planning in the context of a simulated patient/provider task in which pairs of older adults worked together to create medication schedules. Experiment 1 compared pairs who used the medtable, blank paper (unstructured aid), or no aid to create schedules varying in complexity of medication constraints (number of medications and medication co-occurrence restrictions) and patient constraints (available times during the day to take medication). Both aids increased problem-solving accuracy and efficiency (time per unit accuracy) compared to the no-aid condition, primarily for more complex schedules. However, benefits were similar for the two aids. In Experiment 2, a redesigned medtable increased problem-solving accuracy and efficiency compared to blank paper. Both aids presumably supported problem solving by providing a jointly visible workspace for developing schedules. The medtable may be more effective because it externalizes constraints (relationships between medication and patient information), so that participants can more easily organize information.

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National Surveillance of Emergency Department Visits for Outpatient Adverse Drug Events

Cited by 761 publications

References 39 publications

Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing

Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing

Informatic Tools and Approaches in Postmarketing Pharmacovigilance Used by FDA

External support for collaborative problem solving in a simulated provider/patient medication scheduling task.

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