Dataset Information

Development of a context model to prioritize drug safety alerts in CPOE systems.

ABSTRACT:

Background

Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation.

Methods

We used a combination of literature searches and expert interviews to identify and validate the possible context factors. The internal validation of the context factors was performed by calculating the inter-rater agreement of two researcher's classification of 33 relevant articles.

Results

We developed a context model containing 20 factors. We grouped these context factors into three categories: characteristics of the patient or case (e.g. clinical status of the patient); characteristics of the organizational unit or user (e.g. professional experience of the user); and alert characteristics (e.g. severity of the effect). The internal validation resulted in nearly perfect agreement (Cohen's Kappa value of 0.97).

Conclusion

To our knowledge, this is the first structured attempt to develop a comprehensive context model for prioritizing drug safety alerts in CPOE systems. The outcome of this work can be used to develop future tailored drug safety alerting in CPOE systems.

SUBMITTER: Riedmann D

PROVIDER: S-EPMC3127742 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Publications

Development of a context model to prioritize drug safety alerts in CPOE systems.

Riedmann Daniel D Jung Martin M Hackl Werner O WO Stühlinger Wolf W van der Sijs Heleen H Ammenwerth Elske E

BMC medical informatics and decision making 20110525

<h4>Background</h4>Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation.<h4>Methods</h4>We used a combination ...[more]

PMID: 21612623

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Project description:BackgroundDrug-drug interactions (DDIs) involving vitamin K antagonists (VKAs) constitute an important cause of in-hospital morbidity and mortality. However, the list of potential DDIs is long; the implementation of all these interactions in a clinical decision support system (CDSS) results in over-alerting and alert fatigue, limiting the benefits provided by the CDSS.ObjectiveTo estimate the probability of occurrence of international normalized ratio (INR) changes for each DDI rule, via the reuse of electronic health records.MethodsAn 8-year, exhaustive, population-based, historical cohort study including a French community hospital, a group of Danish community hospitals, and a Bulgarian hospital. The study database included 156,893 stays. After filtering against two criteria (at least one VKA administration and at least one INR laboratory result), the final analysis covered 4047 stays. Exposure to any of the 145 drugs known to interact with VKA was tracked and analyzed if at least 3 patients were concerned. The main outcomes are VKA potentiation (defined as an INR≥5) and VKA inhibition (defined as an INR≤1.5). Groups were compared using the Fisher exact test and logistic regression, and the results were expressed as an odds ratio (95% confidence limits).ResultsThe drugs known to interact with VKAs either did not have a statistically significant association regarding the outcome (47 drug administrations and 14 discontinuations) or were associated with significant reduction in risk of its occurrence (odds ratio<1 for 18 administrations and 21 discontinuations).ConclusionsThe probabilities of outcomes obtained were not those expected on the basis of our current body of pharmacological knowledge. The results do not cast doubt on our current pharmacological knowledge per se but do challenge the commonly accepted idea whereby this knowledge alone should be used to define when a DDI alert should be displayed. Real-life probabilities should also be considered during the filtration of DDI alerts by CDSSs, as proposed in SPC-CDSS (statistically prioritized and contextualized CDSS). However, these probabilities may differ from one hospital to another and so should probably be calculated locally.

Project description:We examine the characteristics of clinical decision support alerts triggered when opioids are prescribed, including alert type, override rates, adverse drug events associated with opioids, and preventable adverse drug events.This was a retrospective chart review study assessing adverse drug event occurrences for emergency department (ED) visits in a large urban academic medical center using a commercial electronic health record system with clinical decision support. Participants include those aged 18 to 89 years who arrived to the ED every fifth day between September 2012 and January 2013. The main outcome was characteristics of opioid drug alerts, including alert type, override rates, opioid-related adverse drug events, and adverse drug event preventability by clinical decision support.Opioid drug alerts were more likely to be overridden than nonopioid alerts (relative risk 1.35; 95% confidence interval [CI] 1.21 to 1.50). Opioid drug-allergy alerts were twice as likely to be overridden (relative risk 2.24; 95% CI 1.74 to 2.89). Opioid duplicate therapy alerts were 1.57 times as likely to be overridden (95% CI 1.30 to 1.89). Fourteen of 4,581 patients experienced an adverse drug event (0.31%; 95% CI 0.15% to 0.47%), and 8 were due to opioids (57.1%). None of the adverse drug events were preventable by clinical decision support. However, 46 alerts were accepted for 38 patients that averted a potential adverse drug event. Overall, 98.9% of opioid alerts did not result in an actual or averted adverse drug event, and 96.3% of opioid alerts were overridden.Overridden opioid alerts did not result in adverse drug events. Clinical decision support successfully prevented adverse drug events at the expense of generating a large volume of inconsequential alerts. To prevent 1 adverse drug event, providers dealt with more than 123 unnecessary alerts. It is essential to refine clinical decision support alerting systems to eliminate inconsequential alerts to prevent alert fatigue and maintain patient safety.

Project description:To assess the risk of medication errors in subjects with renal impairment (defined as an estimated glomerular filtration rate (eGFR) ?40 ml/min/1.73 m(2)) and the effectiveness of automatic eGFR ?40-alerts relayed to community pharmacists.Clinical survey.The city of Zwolle, The Netherlands, in a primary care setting including 22 community pharmacists and 65 general practitioners.All adults who underwent ambulatory creatine measurements which triggered an eGFR ?40-alert.The total number of ambulatory subjects with an eGFR ?40-alert during the study period of 1 year and the number of medication errors related to renal impairment. The type and number of proposed drug adjustments recommended by the community pharmacist and acceptance rate by the prescribing physicians. Classification of all medication errors on their potential to cause an adverse drug event (ADE) and the actual occurrence of ADEs (limited to those identified through hospital record reviews) 1 year after the introduction of the alerts.Creatine measurements were performed in 25 929 adults. An eGFR ?40-alert was indicated for 5.3% (n=1369). This group had a median (IQR) age of 78 (69, 84) years, and in 73% polypharmacy (?5 drugs) was present. In 15% (n=211) of these subjects, a medication error was detected. The proportion of errors increased with age. Pharmacists recommended 342 medication adjustments, mainly concerning diuretics (22%) and antibiotics (21%). The physicians' acceptance rate was 66%. Of all the medication errors, 88% were regarded as potential ADEs, with most classified as significant or serious. At follow-up, the ADE risk (n=40) appeared highest when the proposed medication adjustments were not implemented (38% vs 6%).The introduction of automatic eGFR-alerts identified a considerable number of subjects who are at risk for ADEs due to renal impairment in an ambulatory setting. The nationwide implementation of this simple protocol could identify many potential ADEs, thereby substantially reducing iatrogenic complications in subjects with impaired renal function.

Dataset Information

Development of a context model to prioritize drug safety alerts in CPOE systems.

Background

Methods

Results

Conclusion

Publications

Development of a context model to prioritize drug safety alerts in CPOE systems.

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