Reasons provided by prescribers when overriding drug-drug interaction alerts (original) (raw)
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Structured override reasons for drug-drug interaction alerts in electronic health records
Journal of the American Medical Informatics Association, 2019
Objective The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records. Materials and Methods We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices. Results Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: “will monitor or take precautions,” “not clinically significant,” and “benefit outweighs risk.” Discussion We found wide variability in override reasons between sites and many opportunities to improve alerts. Some o...
Physicians’ responses to computerized drug–drug interaction alerts for outpatients
Computer Methods and Programs in Biomedicine, 2013
Introduction: Adverse drug reactions (ADR) increase morbidity and mortality; potential drug-drug interactions (DDI) increase the probability of ADR. Studies have proven that computerized drug-interaction alert systems (DIAS) might reduce medication errors and potential adverse events. However, the relatively high override rates obscure the benefits of alert systems, which result in barriers for availability. It is important to understand the frequency at which physicians override DIAS and the reasons for overriding reminders. Method: All the DDI records of outpatient prescriptions from a tertiary university hospital from 2005 and 2006 detections by the DIAS are included in the study. The DIAS is a JAVA language software that was integrated into the computerized physician order entry system. The alert window is displayed when DDIs occur during order entries, and physicians choose the appropriate action according to the DDI alerts. There are seven response choices are obligated in representing overriding and acceptance: (1) necessary order and override; (2) expected DDI and override; (3) expected DDI with modified dosage and override; (4) no DDI and override; (5) too busy to respond and override; (6) unaware of the DDI and accept; and (7) unexpected DDI and accept. The responses were collected for analysis. Results: A total of 11,084 DDI alerts of 1,243,464 outpatient prescriptions were present, 0.89% of all computerized prescriptions. The overall rate for accepting was 8.5%, but most of the alerts were overridden (91.5%). Physicians of family medicine and gynecology-obstetrics were more willing to accept the alerts with acceptance rates of 20.8% and 20.0% respectively (p < 0.001). Information regarding the recognition of DDIs indicated that 82.0% of the DDIs were aware by physicians, 15.9% of DDIs were unaware by physicians, and 2.1% of alerts were ignored. The percentage of total alerts declined from 1.12% to 0.79% during 24 months' study period, and total overridden alerts also declined (from 1.04% to 0.73%). Conclusion: We explored the physicians' behavior by analyzing responses to the DDI alerts. Although the override rate is still high, the reasons why physicians may override DDI alerts were well analyzed and most DDI were recognized by physicians. Nonetheless, the trend of
Electronic Drug Interaction Alerts in Ambulatory Care
Drug Safety, 2011
Background: Computerized physician order entry systems are known to improve patient safety in acute-care hospitals. However, as clinicians frequently override drug interaction and allergy alerts, their value in ambulatory care remains uncertain. Objective: The purpose of the study was to examine whether ambulatory care clinicians were more likely to accept drug-drug interaction alerts that an expert panel judged to be of high clinical value. Study Design: We convened an expert panel to examine drug-drug interaction alerts generated by 2872 clinicians in Massachusetts, Pennsylvania and New Jersey who used a common electronic prescribing system between 1 January 2006 and 30 September 2006. We selected 120 representative drug interaction alerts from the most commonly encountered class-class interactions. Measurements: The expert panel rated each alert based on the following categories: (i) strength of the scientific evidence; (ii) probability that the interaction would result in an adverse drug event (ADE); (iii) severity of typical and most serious ADEs; (iv) the likelihood that a clinician could act on the information; and (v) the overall value of the alert to the average primary care clinician. We then used multivariate regression techniques to examine the relationship between the alert acceptance rate and the expert panel's mean rating of each category. Results: The decision of clinicians to accept drug interaction alerts increased (relative to a baseline alert acceptance rate of 8.8%) by 2.7% (95% CI 0.4, 5.1) for interactions that panelists judged would result in an ADE, by 2.3% (95% CI 0.9, 3.7) when primary care providers (PCPs) lacked prior knowledge about the information presented in the alert, and by 3.3% (95% CI 0.9, 5.8) when the PCP could readily act on the information provided in the alert.
Journal of the American Medical Informatics Association : JAMIA, 2016
The United States Office of the National Coordinator for Health Information Technology sponsored the development of a "high-priority" list of drug-drug interactions (DDIs) to be used for clinical decision support. We assessed current adoption of this list and current alerting practice for these DDIs with regard to alert implementation (presence or absence of an alert) and display (alert appearance as interruptive or passive). We conducted evaluations of electronic health records (EHRs) at a convenience sample of health care organizations across the United States using a standardized testing protocol with simulated orders. Evaluations of 19 systems were conducted at 13 sites using 14 different EHRs. Across systems, 69% of the high-priority DDI pairs produced alerts. Implementation and display of the DDI alerts tested varied between systems, even when the same EHR vendor was used. Across the drug pairs evaluated, implementation and display of DDI alerts differed, ranging fro...
Journal of the American Medical Informatics Association, 2011
Objective Pharmacy clinical decision-support (CDS) software that contains drugedrug interaction (DDI) information may augment pharmacists' ability to detect clinically significant interactions. However, studies indicate these systems may miss some important interactions. The purpose of this study was to assess the performance of pharmacy CDS programs to detect clinically important DDIs. Design Researchers made on-site visits to 64 participating Arizona pharmacies between December 2008 and November 2009 to analyze the ability of pharmacy information systems and associated CDS to detect DDIs. Software evaluation was conducted to determine whether DDI alerts arose from prescription orders entered into the pharmacy computer systems for a standardized fictitious patient. The fictitious patient's orders consisted of 18 different medications including 19 drug pairsd13 of which were clinically significant DDIs, and six were non-interacting drug pairs. Measurements The sensitivity, specificity, positive predictive value, negative predictive value, and percentage of correct responses were measured for each of the pharmacy CDS systems. Results Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. The median percentage of correct DDI responses was 89% (range 47e100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23e1.0); median specificity was 1.0 (range 0.83e1.0); median positive predictive value was 1.0 (range 0.88e1.0); and median negative predictive value was 0.75 (range 0.38e1.0). Conclusions These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted.
BMJ Open
IntroductionDrug–drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients’ medication orders. This project aims to determine the impact of DDI alerts on DDI rates and on patient harm in the inpatient setting. It also aims to identify barriers and facilitators to optimal use of alerts, quantify the alert burden posed to prescribers with implementation of DDI alerts and to develop algorithms to improve the specificity of DDI alerting systems.Methods and analysisA controlled pre-post design will be used. Study sites include six major referral hospitals in two Australian states, New South Wales and Queensland. Three hospitals will act as control sites and will implement an EMM system without DDI alerts, and three as intervention sites with DDI alerts. The medical records of 280 patients admitted in the 6 months prior to and 6 months following implement...
Journal of the American Medical Informatics Association, 2009
are drug combinations that result in a decreased drug effect due to coadministration of a second drug. Such interactions can be prevented by separately administering the drugs. This study attempted to reduce drug administration errors due to overridden TDDIs in a care provider order entry (CPOE) system. In four periods divided over two studies, logged TDDIs were investigated by reviewing the time intervals prescribed in the CPOE and recorded on the patient chart. The first study showed significant drug administration error reduction from 56.4 to 36.2% (p Ͻ 0.05), whereas the second study was not successful (46.7 and 45.2%; p Ͼ 0.05). Despite interventions, drug administration errors still occurred in more than one third of cases and prescribing errors in 79 -87%. Probably the low alert specificity, the unclear alert information content, and the inability of the software to support safe and efficient TDDI alert handling all diminished correct prescribing, and consequently, insufficiently reduced drug administration errors.