Improving Recognition of Drug Interactions

Glassman, Peter; Simón, Barbara; Belperio, Pamela S.; Lanto, Andrew B.

doi:10.1097/00005650-200212000-00004

Cited by 244 publications

(64 citation statements)

References 25 publications

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“…16,17 In the study by Weingart et al, 11 physician reviewers judged onethird of generated alerts to be inappropriate. Similarly, our previous experience with drug-allergy alerts highlighted that the vast majority of allergy alert overrides were clinically appropriate and did not lead to adverse drug events.…”

Section: Discussionmentioning

confidence: 99%

Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care

Shah

Seger

et al. 2006

Journal of the American Medical Informatics Association

324

242

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A b s t r a c t Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workflow disruptions by designating only critical to high-severity alerts to be interruptive to clinician workflow. The alerts were presented to clinicians using computerized prescribing within an electronic medical record in 31 Boston-area practices. There were 18,115 drug alerts generated during our six-month study period. Of these, 12,933 (71%) were noninterruptive and 5,182 (29%) interruptive. Of the 5,182 interruptive alerts, 67% were accepted. Reasons for overrides varied for each drug alert category and provided potentially useful information for future alert improvement. These data suggest that it is possible to design computerized prescribing decision support with high rates of alert recommendation acceptance by clinicians. Computerized prescribing applications that embed clinical decision support systems (CDSS) within computerized provider order entry reduce medication error rates both by structuring prescriptions and by checking them for potential problems such as drug interactions, allergies, and other issues.1-9 If a potential problem is found, the CDSS can provide clinicians with real-time alerts, allowing the clinician to make appropriate changes before the prescription is finalized.While computerized prescribing applications are commercially available (either as stand-alone applications or as part of an electronic medical record), these systems may not be as effective for improving safety if clinicians override clinically important alerts. When the threshold for alerting is set too low, clinicians are inundated with alerts of low clinical significance, leading to high override rates and the potential to override even important alerts. [10][11][12] In one inpatient study, Payne et al. 10 found an 88% override rate for drug interaction alerts, and a 69% override rate for drug-allergy alerts. Similarly, Weingart et al 11 found ambulatory physicians overrode 91% of drug-allergy alerts, and 89% of high-severity drug-drug interaction alerts.

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Section: Discussionmentioning

confidence: 99%

Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care

Shah

Seger

et al. 2006

Journal of the American Medical Informatics Association

324

242

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“…Too many unwanted alerts (Robertson, et al, 2011); (Glassman, Simon, Belperio, & Lanto, 2002); (Tierney, et al, 2005); (Murray, et al, 2004) Poor system design (Robertson, et al, 2011 …”

Section: List Of Barriers Papersmentioning

confidence: 99%

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

Devaraj¹,

Sharma²,

Fausto³

et al. 2014

JBAR

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The objective of the study was to identify potential barriers and facilitators to improve clinical practice using computer-based Clinical Decision Support System (CDSS). Studies published since 2000 were found using PubMed database, PsychInfo, CINAHL, EBSCOhost database, and Google scholar. Twenty-six relevant publications were examined. Thirty-five unique barriers and twenty-five unique facilitators were identified in the literature as important determinants of CDSS's adoption in clinical practice. The list of barriers and facilitators collected from each study were then organized under the four dimensions of The Unified Theory of Acceptance and Use of Technology (UTAUT) model: performance expectancy, effort expectancy, social influence, and facilitating conditions. Some of the important barriers to CDSS use include; lack of time or time constraints, economic constraints (e.g., finance and resources), lack of knowledge of system or content, reluctance to use system in front of patients, obscure workflow issues, less authenticity or reliability of information, lack of agreement with the system, and physician or user attitude toward the system. The study contributes immensely to the literature by identifying the important barriers and facilitators of CDSS.

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“…Results from a survey of prescribers revealed that specialists were less likely to identify interactions than general physicians [19]. Another study found that younger clinicians identified more potential interactions than older clinicians due to possible reasons like aged-related memory loss and the lack of continuing medical education on potential DDIs [20]. Conclusions from this study reflect the need to alert oncologists about potentially interacting AED-oral ACD pairs when they are being co-prescribed.…”

Section: Discussionmentioning

confidence: 96%

Drug-Drug Interactions between Oral Antiepileptics and Oral Anticancer Drugs: Implications to Clinicians

Cheung¹,

Yap²,

Chui³

et al. 2010

Eur Neurol

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Background: Existing research has suggested that there can be potential drug-drug interaction (DDI) between antiepileptic drugs (AED) and anticancer drugs (ACD). However, information on the prevalence of patients on concurrent oral AED and oral ACD is limited. Methods: A retrospective study was conducted at the National Cancer Centre Singapore. Prevalence was calculated by identifying prescriptions with both oral AED and oral ACD from the outpatient prescription database over three years. Prevalence and physicians’ prescribing patterns were evaluated. Co-prescription was defined as medications that were prescribed by the same physician on the same day. Potentially interacting combinations were further detected using an existing database, OncoRx (www.onco-informatics.com). Results: 42,810 prescriptions that contained at least one oral ACD were identified from the database. The number and prevalence of prescriptions that had a combination of oral ACD and AED were 274 and 0.64%, respectively, with the majority (82.8%) of the AED-oral ACD pairs being co-prescribed. Per patient, the average number of exposure days to the AED-oral ACD pair was 19.5 days annually. Fifty-one (18.6%) prescriptions were identified as containing potentially interacting AED-oral ACD pairs. Discussion: There is a relatively low prevalence of AED-oral ACD combined exposure in the population we sampled; however, the combined exposure is long enough to produce clinically important DDI effects.

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Improving Recognition of Drug Interactions

Cited by 244 publications

References 25 publications

Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care

Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

Drug-Drug Interactions between Oral Antiepileptics and Oral Anticancer Drugs: Implications to Clinicians

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