Practitioners' Views on Computerized Drug--Drug Interaction Alerts in the VA System

Ko, Yu; Abarca, Jacob; Malone, Daniel C.; Dare, Donna C.; Geraets, Doug; Houranieh, Antoun; Jones, William N.; Nichol, W. Paul; Schepers, Gregory P.; Wilhardt, Michelle S.

doi:10.1197/jamia.m2224

Cited by 90 publications

(58 citation statements)

References 38 publications

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“…Electronic databases can be used (such as Lexi-Interact® or Epocrates®) to identify DDIs, either stand-alone or integrated into a computerized physician order entry (CPOE) system. The problem of such systems is "alert fatigue", a phenomenon related to a high rate of irrelevant alerts leading physicians to override them [20][21][22]. Moreover, in most of these databases, warnings for acenocoumarol come from drug interactions with warfarin.…”

Section: Introductionmentioning

confidence: 99%

Identification and weighting of the most critical “real-life” drug–drug interactions with acenocoumarol in a tertiary care hospital

Gschwind

Rollason

Lovis

et al. 2012

Eur J Clin Pharmacol

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Purpose The objective of this study was to identify the most clinically relevant drug-drug interactions (DDIs) at risk of affecting acenocoumarol safety in our tertiary care university hospital, a 2,000 bed institution. Methods We identified DDIs occurring with acenocoumarol by combining two different sources of information: a 1-year retrospective analysis of acenocoumarol prescriptions and comedications from our Computerized Physician Order Entry (CPOE) system (n = 2,439 hospitalizations) and a retrospective study of clinical pharmacology consultations involving acenocoumarol over the past 14 years (1994-2007) (n = 407). We classified these DDIs using an original risk-analysis method. A criticality index was calculated for each associated drug by multiplying three scores based on mechanism of interaction, involvement in a supratherapeutic international normalized ratio (INR) (≥ 6) and involvement in a severe bleeding.Results One hundred and twenty-six DDIs were identified and weighted. Twenty-eight drugs had a criticality index ≥ 20 and were therefore considered at high risk for interacting with acenocoumarol by increasing its effect: 75% of these drugs involved a pharmacokinetic mechanism and 14 % a pharmacodynamic mechanism. An unknown mechanism of interaction was involved in 11 % of drugs. Conclusion Twenty-eight specific drugs were identified as being at high risk for interacting with acenocoumarol in our hospital using an original risk-analysis method. Most analyzed drugs interact with acenocoumarol via a pharmacokinetic mechanism. Actions such as the implementation of alerts in our CPOE system should be specifically developed for these drugs.

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Section: Introductionmentioning

confidence: 99%

Identification and weighting of the most critical “real-life” drug–drug interactions with acenocoumarol in a tertiary care hospital

Gschwind

Rollason

Lovis

et al. 2012

Eur J Clin Pharmacol

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“…This is in line with a study by Magnus et al [29], where 70% of GPs reported changing their initial prescribing decision based on drug-drug interaction warnings at least sometimes or more often. In a study by Ko et al [30], 49 % of the prescribers reported to change their prescribing decision due to DDI warnings, with a lower percentage for physicians using computer generated alerts.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of usage patterns and user perception of the drug–drug interaction database SFINX

Andersson

Böttiger

Bastholm‐Rahmner

et al. 2015

International Journal of Medical Informatics

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PurposeThe aim of the present study was to investigate how prescribers and pharmacists use and perceive the drug-drug interaction database SFINX in their clinical work. MethodsA questionnaire was developed with questions aimed at the usage of SFINX, and the perceptions of the database. The questionnaire was sent out to all registered users of the web application of SFINX.The anonymous answers from the target users, prescribers and pharmacists were summarized using descriptive statistics. Statistical analysis was performed on age and gender differences for some questions regarding different usage patterns. ResultsThe questionnaire was sent to 11 763 registered SFINX users. The response rate was 23 %, including 1871 answers from prescribers or pharmacists. SFINX was reported to be used at least weekly or more often by 45% of the prescribers and 51% of the pharmacists. Many prescribers reported using the database during the patient consultation (60%) or directly before or after (56%). Among the prescribers, 74% reported that the information received made them change their action at least sometimes. About 20% of the prescribers and 25% of the pharmacists considered the information as irrelevant sometimes or more often. ConclusionMost prescribers and pharmacists reported using SFINX in direct association with a patient consultation. Information received by using SFINX makes prescribers and pharmacists change their handling of patients. DDI databases with relevant information about patient handling might improve drug treatment outcome.

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“…21 In another study of DUR programs in community pharmacies, pharmacy staff members overrode approximately 88% of all DUR alerts. 6 Yet another limitation of drug screening software is its inability to detect potential DDIs involving nonprescription medications because these products may not be entered into the patient's drug profile.…”

Section: Discussionmentioning

confidence: 99%

Pharmacy Students' Ability to Identify Potential Drug-Drug Interactions

Saverno¹,

Malone²,

Kurowsky³

2009

Am J Pharm Educ

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Objective. To evaluate the ability of third-and fourth-year pharmacy students to identify clinically significant drug-drug interactions (DDIs) Methods. A questionnaire designed to measure DDI knowledge was disseminated to fourth-year pharmacy students in a school of pharmacy. A second questionnaire was distributed to third-year pharmacy students in 2 schools of pharmacy (schools A and B) and re-administered to students in 1 of the schools 1 year later. Results. Class of 2005 fourth-year pharmacy students correctly categorized an average of 52% 6 13% DDI pairs on the first questionnaire. Third-year pharmacy students at schools A and B correctly categorized an average of 61% 6 18% and 66% 6 15% of DDI pairs, respectively. The average percentage of correct responses for fourth-year students from the class of 2007 was 65% (6 17%). Conclusion. Pharmacy students' ability to identify important DDIs is far from optimal, even after completing experiential requirements.

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Practitioners' Views on Computerized Drug--Drug Interaction Alerts in the VA System

Abstract: Respondents reported neutral to positive views regarding the effect of CPOE on their jobs. Their opinions suggest DDI alerts are useful but still require additional work to increase their clinical utility.

Cited by 90 publications

References 38 publications

Identification and weighting of the most critical “real-life” drug–drug interactions with acenocoumarol in a tertiary care hospital

Identification and weighting of the most critical “real-life” drug–drug interactions with acenocoumarol in a tertiary care hospital

Evaluation of usage patterns and user perception of the drug–drug interaction database SFINX

Pharmacy Students' Ability to Identify Potential Drug-Drug Interactions

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