Drug-drug Interaction Alerts Research Articles

Electronic Drug Interaction Alerts in Ambulatory Care

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. 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. 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. 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. 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. The value of electronic drug interaction alerts is influenced heavily by clinicians' judgements about the clinical value of the alert. Expert judgement should be taken into account when developing electronic decision support.

Comment: Evaluation of Contraindicated Drug-Drug Interaction Alerts in a Hospital Setting

Comment: Evaluation of Contraindicated Drug-Drug Interaction Alerts in a Hospital Setting

Pharmacists’ awareness of clinical decision support in pharmacy information systems: An exploratory evaluation

Background Clinical decision support (CDS), such as drug-drug interaction (DDI) and drug-allergy checking, has been used in pharmacy information systems for several decades; however, there has been limited research on CDS use by practicing pharmacists. Objective The purpose of this study was to document pharmacists’ awareness of DDI and other medication-related CDS features available within pharmacy information systems. Methods Researchers conducted on-site interviews with pharmacists throughout the state of Arizona from December 2008 to November 2009 regarding their pharmacy information systems features. Pharmacists were asked to provide information about DDI and other medication-related decision support features of the pharmacy software at their practice site. Descriptive statistics were used to summarize interview responses. Results Sixty-one pharmacists from a variety of practice settings completed the interview. All respondents indicated that their pharmacy system provided drug-allergy and DDI alerts. Approximately 60% of the pharmacists reported that their DDI decision support systems included recommendations for managing drug interactions. Two-thirds of respondents reported that their pharmacy’s computer system permitted the addition of medications from other pharmacies and/or over-the-counter products to a patient’s profile. Approximately 40% of the pharmacists reported that some drugs entered into the pharmacy computer system were not included in (or linked to) the electronic DDI checking. Most pharmacists indicated the presence of other medication-related decision support features, such as drug-disease (78%), drug-age precautions (67%), and inappropriate dosage alerts (79%). However, fewer pharmacists reported more advanced functionality, such as laboratory recommendations (34%) and pediatric dosing (39%). Conclusion Overall, pharmacists’ awareness regarding the many decision support functionalities of their systems was limited. Based on the study findings, it appears that there are a number of limitations associated with currently available pharmacy decision support software. Further research is needed to formally evaluate pharmacist knowledge of pharmacy decision support software functionality. More formal training about software capabilities coupled with the addition of more advanced decision support features has the potential to improve pharmacists’ use of these systems to make better clinical decisions and avoid preventable errors.

Development and validation of a survey instrument for assessing prescribers' perception of computerized drug-drug interaction alerts.

To develop a theoretically informed and empirically validated survey instrument for assessing prescribers' perception of computerized drug-drug interaction (DDI) alerts. The survey is grounded in the unified theory of acceptance and use of technology and an adapted accident causation model. Development of the instrument was also informed by a review of the extant literature on prescribers' attitude toward computerized medication safety alerts and common prescriber-provided reasons for overriding. To refine and validate the survey, we conducted a two-stage empirical validation study consisting of a pretest with a panel of domain experts followed by a field test among all eligible prescribers at our institution. The resulting survey instrument contains 28 questionnaire items assessing six theoretical dimensions: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived fatigue, and perceived use behavior. Satisfactory results were obtained from the field validation; however, a few potential issues were also identified. We analyzed these issues accordingly and the results led to the final survey instrument as well as usage recommendations. High override rates of computerized medication safety alerts have been a prevalent problem. They are usually caused by, or manifested in, issues of poor end user acceptance. However, standardized research tools for assessing and understanding end users' perception are currently lacking, which inhibits knowledge accumulation and consequently forgoes improvement opportunities. The survey instrument presented in this paper may help fill this methodological gap. We developed and empirically validated a survey instrument that may be useful for future research on DDI alerts and other types of computerized medication safety alerts more generally.

Medical, nursing, and pharmacy students’ ability to recognize potential drug-drug interactions: A comparison of healthcare professional students

To evaluate and compare the drug-drug interaction (DDI) knowledge of pharmacy, medical, and nurse practitioner (NP) students who are beginning supervised clinical practice. This study utilized a prospective evaluation of DDI knowledge among healthcare professional students who were currently enrolled in their final didactic year at the University of Arizona Colleges of Medicine, Pharmacy, or Nursing's NP program. Students were asked to assess 15 drug pairs and to select an appropriate management strategy for each pair. The primary outcome measure was the ability to correctly categorize each drug pair into one of the five management responses. The secondary outcome measure was the number of clinically significant DDIs recognized. Pharmacy students demonstrated significantly better knowledge than medical and NP students with respect to identifying and selecting management strategies for possible DDIs. However, there is much room for improvement for all groups. An increase in curricular content that focuses on DDIs has the potential to better prepare medical, pharmacy, and NP students for practice situations involving DDI alerts, and to increase the quality of patient care.

Evaluation of Contraindicated Drug-Drug Interaction Alerts in a Hospital Setting

Risks associated with contraindicated drug-drug interaction alerts (CDDIAs) should always outweigh benefits. Misclassified CDDIAs should be eliminated. To review CDDIAs and determine if they are contraindicated according to Food and Drug Administration-approved product labeling and if there are circumstances in which contraindicated interactions are acceptable. A cross-sectional observational and quality improvement study was conducted over two 1-year periods. The 20 most common CDDIAs from May 2007 to May 2008 and all CDDIAs from April 2008 to April 2009 were collected at a large teaching hospital. Horizon Meds Manager used First DataBank as the knowledge base for decision support. Interactions were deemed truly contraindicated if listed in the contraindications section of the labeling of at least one of the interacting drugs. Alerts were grouped by drug and pharmacologic class to evaluate the evidence supporting the relevance of these interactions. An expert panel determined when an alert was misclassified. A medical advisory committee determined whether a contraindicated drug-drug combination was acceptable. Twelve (60%) of the most common 20 contraindicated interaction pairs from 2007 to 2008 were inappropriately classified. Half of the alerts were not truly contraindicated. The 8 truly contraindicated drug-drug pairs were ketorolac and other nonsteroidal antiinflammatory drugs or oral solid potassium products and anticholinergics. Half of these interactions were subsequently deemed acceptable under specific circumstances. Similar results were found in the second year, with only 55.1% of all CDDIAs being truly contraindicated despite eliminating some of the alerts that were misclassified in the first year. Nearly three fourths of legitimate CDDIAs were deemed acceptable under specific circumstances. Most contraindicated drug-drug interaction alerts from a commercial knowledge base were inappropriately categorized and could be downgraded. Some contraindicated drug combinations are permissible under specific circumstances. Alerts suggesting that certain drugs should never be used together, but their use together is sometimes acceptable, contribute to alert fatigue.

Acceptance of drug–drug interaction alerts and alert system design

In an outstanding paper, Zwart-van Rijkom et al.[1] address a challenging research area with an important clinical relevance. They have set out to measure the frequency and nature of drug–drug interaction (DDI) alerts in a Dutch university hospital. They retrospectively analyzed all prescription records of patients hospitalized in 2006 who were prescribed at least one medication. Because there was no record of which alerts were actually given to the providers, they reconstructed these alerts by combining the professional guideline for the management of DDIs (G-standard) with the historical prescriptions. The study quantifies, for the first time, the frequency and nature of (possible) DDIs in Dutch hospitals, and contributing to the body of evidence known about DDIs and DDI alerts. The authors also offer an insightful explanation for their finding that the 10 most frequently encountered DDIs for adults in hospital were associated with medications that are used and initiated on a large scale in the community setting. The explanation is that the G-standard itself has primarily been developed for use within community pharmacies. The authors conclude that more sophisticated clinical decision support systems can improve the specificity of the alerts to combat ‘alert fatigue’. Computerized physician medication order entry with decision support capabilities can indeed have a positive impact on hospitalized patients [2] and we second the authors' conclusion. We would like to draw attention to an important observation, not explicitly made in the paper, nor in the earlier work on drug–drug interactions that it cites, concerning the acceptance status of the alerts. One cannot reconstruct all the real generated alerts from the historical prescriptions in the setting of this study. In fact any accepted alert is missed in the simulation simply because it either resulted in the cancellation of the prescription or in prescribing alternate medication: the original attempted prescription is no longer there to reconstruct the alert. This means that the objective ‘to measure the frequency and nature of DDI alerts’ is, strictly speaking, not attainable. The study should hence be viewed as an attempt to ‘measure the frequency and nature of ignored DDI alerts’. We will not know the proportion and nature of the accepted alerts and hence statements such as ‘10% of all prescriptions generated a DDI alert’ should be taken to set a lower limit on the true proportion of all generated alerts by the system. Targeting the ignored alerts, however, is easily motivated: one may be interested in the potential harm to patients that was not successfully intercepted by the system. This, however, means that the reported frequencies and nature of DDIs strongly depend on the effectiveness of the alerts and the underlying drug–drug interaction database used. If the accepted alerts are considered as well then the frequencies of important DDIs in the study may change. In consequence, for hospitals not using the G-index, developers of alerting systems may have somewhat different DDIs to target. What is more, accepted alerts may play an additional (serendipitous) important role in alert design within decision support systems: relatively frequently accepted alerts (e.g. with acceptance rate of 40%) can empirically signify DDIs perceived as genuinely important by the providers. Hence these interactions may merit higher priority to be targeted (in, say, the rest of cases in which they are ignored) than other DDIs. This may reduce the number of alerts and alert fatigue and boost the effectiveness of the targeted alerts. In summary, both ignored and accepted alerts merit investigation but knowledge of their acceptance status bears different meanings of the DDIs found as well as different implications for the design of alerts in computerized decision support systems.

Frequency and nature of drug–drug interactions in a Dutch university hospital

Drug-drug interactions (DDIs) may lead to often preventable adverse drug events and health damage. Especially within hospitals, this might be an important factor, as patients are severely ill and multiple medications may be prescribed simultaneously. The objective of this study was to measure the frequency and nature of DDI alerts in a Dutch university hospital.All patients hospitalized in the University Medical Centre Utrecht in 2006 who were prescribed at least one medication were included. The frequency of DDIs was calculated as: (i) the percentage of patients experiencing at least one DDI, and (ii) the percentage of prescriptions generating a DDI alert. Based on the national professional guideline, DDIs were classified into categories of potential clinical outcome, management advice, clinical relevance (A-F) and available evidence (0-4).Of the 21 277 admissions included, 5909 (27.8%) encountered at least one DDI. Overall, the prescribing physician received a DDI alert in 9.6% of all prescriptions. The most frequently occurring potential clinical consequence of the DDIs was an increased risk of side-effects such as increased bleeding risk (22.0%), hypotension (14.9%), nephrotoxicity (12.6%) and electrolyte disturbances (10.5%). Almost half (48.6%) of the DDIs could be managed by monitoring laboratory values.Computerized DDI alerts may be a useful tool to prevent adverse drug events within hospitals, but they may also result in 'alert fatigue'. The specificity of alerts could significantly improve by the use of more sophisticated clinical decision support systems taking into account, for example, laboratory values.

372: Do Computerized Medication Alerts Change Medication Orders In the Emergency Department

One reported advantage of electronic medical record (EMR) and computerized provider order entry (CPOE) systems is the availability and use of automated clinical decision support alerts to reduce medication errors. The objective of this study was to assess the frequency of EMR automated medication alerts, such as patient allergy and drug-drug interaction alerts, and to determine if such alerts result in changes in CPOE medication orders and patient care in the emergency department (ED). We hypothesized that emergency physicians would change their CPOE medication order in the majority of cases where a medication alert occurred, and more frequently with allergy alerts.

Decision Support Alerts for Medication Ordering in a Computerized Provider Order Entry (CPOE) System

We sought to determine the frequency and type of decision support alerts by location and ordering provider role during Computerized Provider Order Entry (CPOE) medication ordering. Using these data we adjusted the decision support tools to reduce the number of alerts. Retrospective analyses were performed of dose range checks (DRC), drug-drug interaction and drug-allergy alerts from our electronic medical record. During seven sampling periods (each two weeks long) between April 2006 and October 2008 all alerts in these categories were analyzed. Another audit was performed of all DRC alerts by ordering provider role from November 2008 through January 2009. Medication ordering error counts were obtained from a voluntary error reporting system. MEASUREMENTRESULTS: Between April 2006 and October 2008 the percent of medication orders that triggered a dose range alert decreased from 23.9% to 7.4%. The relative risk (RR) for getting an alert was higher at the start of the interventions versus later (RR= 2.40, 95% CI 2.28-2.52; p< 0.0001). The percentage of medication orders that triggered alerts for drug-drug interactions also decreased from 13.5% to 4.8%. The RR for getting a drug interaction alert at the start was 1.63, 95% CI 1.60-1.66; p< 0.0001. Alerts decreased in all clinical areas without an increase in reported medication errors. We reduced the quantity of decision support alerts in CPOE using a systematic approach without an increase in reported medication errors.

Frequency and nature of drug–drug interactions in a Dutch university hospital

Drug-drug interactions (DDIs) may lead to often preventable adverse drug events and health damage. Especially within hospitals, this might be an important factor, as patients are severely ill and multiple medications may be prescribed simultaneously. The objective of this study was to measure the frequency and nature of DDI alerts in a Dutch university hospital. All patients hospitalized in the University Medical Centre Utrecht in 2006 who were prescribed at least one medication were included. The frequency of DDIs was calculated as: (i) the percentage of patients experiencing at least one DDI, and (ii) the percentage of prescriptions generating a DDI alert. Based on the national professional guideline, DDIs were classified into categories of potential clinical outcome, management advice, clinical relevance (A-F) and available evidence (0-4). Of the 21 277 admissions included, 5909 (27.8%) encountered at least one DDI. Overall, the prescribing physician received a DDI alert in 9.6% of all prescriptions. The most frequently occurring potential clinical consequence of the DDIs was an increased risk of side-effects such as increased bleeding risk (22.0%), hypotension (14.9%), nephrotoxicity (12.6%) and electrolyte disturbances (10.5%). Almost half (48.6%) of the DDIs could be managed by monitoring laboratory values. Computerized DDI alerts may be a useful tool to prevent adverse drug events within hospitals, but they may also result in 'alert fatigue'. The specificity of alerts could significantly improve by the use of more sophisticated clinical decision support systems taking into account, for example, laboratory values.

Successful strategy to improve the specificity of electronic statin–drug interaction alerts

A considerable weakness of current clinical decision support systems managing drug-drug interactions (DDI) is the high incidence of inappropriate alerts. Because DDI-induced, dose-dependent adverse events can be prevented by dosage adjustment, corresponding DDI alerts should only be issued if dosages exceed safe limits. We have designed a logical framework for a DDI alert-system that considers prescribed dosage and retrospectively evaluates the impact on the frequency of statin-drug interaction alerts. Upper statin dose limits were extracted from the drug label (SPC) (20 statin-drug combinations) or clinical trials specifying the extent of the pharmacokinetic interaction (43 statin-drug combinations). We retrospectively assessed electronic DDI alerts and compared the number of standard alerts to alerts that took dosage into account. From among 2457 electronic prescriptions, we identified 73 high-risk statin-drug pairs. Of these, SPC dosage information classified 19 warnings as inappropriate. Data from pharmacokinetic trials took quantitative dosage information more often into consideration and classified 40 warnings as inappropriate. This is a significant reduction in the number of alerts by 55% compared to SPC-based information (26%; p < 0.001). This retrospective study of pharmacokinetic statin interactions demonstrates that more than half of the DDI alerts that presented in a clinical decision support system were inappropriate if DDI-specific upper dose limits are not considered.

Consider the Content of Drug-Drug Interaction Alerts—Reply

Consider the Content of Drug-Drug Interaction Alerts—Reply

Consider the Content of Drug-Drug Interaction Alerts

Consider the Content of Drug-Drug Interaction Alerts

Drug safety alert generation and overriding in a large Dutch university medical centre

To evaluate numbers and types of drug safety alerts generated and overridden in a large Dutch university medical centre. A disguised observation study lasting 25 days on two internal medicine wards evaluating alert generation and handling of alerts. A retrospective analysis was also performed of all drug safety alerts overridden in the hospital using pharmacy log files over 24 months. In the disguised observation study 34% of the orders generated a drug safety alert of which 91% were overridden. The majority of alerts generated (56%) concerned drug-drug interactions (DDIs) and these were overridden more often (98%) than overdoses (89%) or duplicate orders (80%). All drug safety alerts concerning admission medicines were overridden.Retrospective analysis of pharmacy log files for all wards revealed one override per five prescriptions. Of all overrides, DDIs accounted for 59%, overdoses 24% and duplicate orders 17%. DDI alerts of medium-level seriousness were overridden more often (55%) than low-level (22%) or high-level DDIs (19%). In 36% of DDI overrides, it would have been possible to monitor effects by measuring serum levels. The top 20 of overridden DDIs accounted for 76% of all DDI overrides. Drug safety alerts were generated in one third of orders and were frequently overridden. Duplicate order alerts more often resulted in order cancellation (20%) than did alerts for overdose (11%) or DDIs (2%). DDIs were most frequently overridden. Only a small number of DDIs caused these overrides. Studies on improvement of alert handling should focus on these frequently-overridden DDIs.

Unintended consequences of reducing QT-alert overload in a computerized physician order entry system

PurposeAfter complaints of too many low-specificity drug-drug interaction (DDI) alerts on QT prolongation, the rules for QT alerting in the Dutch national drug database were restricted in 2007 to obviously QT-prolonging drugs. The aim of this virtual study was to investigate whether this adjustment would improve the identification of patients at risk of developing Torsades de Pointes (TdP) due to QT-prolonging drug combinations in a computerized physician order entry system (CPOE) and whether these new rules should be implemented.MethodsDuring a half-year study period, inpatients with overridden DDI alerts regarding QT prolongation and with an electrocardiogram recorded before and within 1 month of the alert override were included if they did not have a ventricular pacemaker and did not use the low-risk combination cotrimoxazole and tacrolimus. QT-interval prolongation and the risk of developing TdP were calculated for all patients and related to the number of patients for whom a QT-alert would be generated in the new situation with the restricted database.ResultsForty-nine patients (13%) met the inclusion criteria. In this study population, knowledge base-adjustment would reduce the number of alerts by 53%. However, the positive predictive value of QT alerts would not change (31% before and 30% after) and only 47% of the patients at risk of developing TdP would be identified in CPOEs using the adjusted knowledge base.ConclusionThe new rules for QT alerting would result in a poorer identification of patients at risk of developing TdP than the old rules. This is caused by the many non-drug-related risk factors for QT prolongation not being incorporated in CPOE alert generation. The partial contribution of all risk factors should be studied and used to create clinical rules for QT alerting with an acceptable positive predictive value.

Clinically relevant QTc prolongation due to overridden drug–drug interaction alerts: a retrospective cohort study

To investigate whether, in patients in whom drug-drug interaction (DDI) alerts on QTc prolongation were overridden, the physician had requested an electrocardiogram (ECG), and if these ECGs showed clinically relevant QTc prolongation. For all patients with overridden DDI alerts on QTc prolongation during 6 months, data on risk factors for QT prolongation, drug class and ECGs were collected from the medical record. Patients with ventricular pacemakers, patients treated on an outpatient basis, and patients using the low-risk combination of cotrimoxazole and tacrolimus were excluded. The magnitude of the effect on the QTc interval was calculated if ECGs before and after overriding were available. Changes of the QTc interval in these cases were compared with those of a control group using one QTc-prolonging drug. In 33% of all patients with overridden QTc alerts an ECG was recorded within 1 month. ECGs were more often recorded in patients with more risk factors for QTc prolongation and with more QTc overrides. ECGs before and after the QTc override were available in 29% of patients. Thirty-one percent of patients in this group showed clinically relevant QTc prolongation with increased risk of torsades de pointes or ventricular arrhythmias. The average change in QTc interval was +31 ms for cases and -4 ms for controls. Overriding the high-level DDI alerts on QTc prolongation rarely resulted in the preferred approach to subsequently record an ECG. If ECGs were recorded before and after QTc overrides, clinically relevant QTc prolongation was found in one-third of cases. ECG recording after overriding QTc alerts should be encouraged to prevent adverse events.

Pharmacist Workload and Pharmacy Characteristics Associated With the Dispensing of Potentially Clinically Important Drug-Drug Interactions

Drug-drug interactions (DDIs) are preventable medical errors, yet exposure to DDIs continues despite systems that are designed to prevent such exposures. The purpose of this study was to examine pharmacy characteristics that may be associated with dispensed potential DDIs. This study combined survey data from community pharmacies in 18 metropolitan statistical areas with pharmacy claims submitted to 4 pharmacy benefit managers (PBMs) over a 3-month period from January 1, 2003 to March 31, 2003. Pharmacy characteristics of interest included prescription volume, the number of full-time equivalent pharmacists and pharmacy staff, computer software programs, and the ability to modify those programs with respect to DDI alerts, the use of technologies to assist in receiving, filling and dispensing medication orders, and prescription volume. The dependent variable in this study was the rate of dispensed medications that may interact. A total of 672 pharmacies were included in the analysis. On average (+/-SD), the respondents filled 1375 +/- 691 prescriptions per week, submitted 17,948 +/- 23,889 pharmacy claims to the participating PBMs, had 1.2 +/- 0.3 full-time equivalent pharmacists per hour open, and 545 (81%) were affiliated with a chain drug store organization. Factors significantly related to an increased risk of dispensing a potential DDI included pharmacist workload (odds ratio [OR] 1.03; 95% confidence interval [CI] 1.028-1.048), pharmacy staffing (OR 1.10; 95% CI: 1.09-1.11), and various technologies (eg, sophisticated telephone systems, internet receipt of orders, and refill requests) that assist with order processing, and the ability to modify DDI alert-screening sensitivity and detailed pharmacological information about DDIs. This study found that there was an increase in the risk of dispensing a potential DDI with higher pharmacist and pharmacy workload, use of specific automation, and dispensing software programs providing alerts and clinical information.

Practitioners’ Views on Computerized Drug–Drug Interaction Alerts in the VA System

ObjectivesTo assess Veterans Affairs (VA) prescribers’ and pharmacists’ opinions about computer-generated drug–drug interaction (DDI) alerts and obtain suggestions for improving DDI alerts. DesignA mail survey of 725 prescribers and 142 pharmacists from seven VA medical centers across the United States. MeasurementsA questionnaire asked respondents about their sources of drug and DDI information, satisfaction with the combined inpatient and outpatient computerized prescriber order entry (CPOE) system, attitude toward DDI alerts, and suggestions for improving DDI alerts. ResultsThe overall response rate was 40% (prescribers: 36%; pharmacists: 59%). Both prescribers and pharmacists indicated that the CPOE system had a neutral to positive impact on their jobs. DDI alerts were not viewed as a waste of time and the majority (61%) of prescribers felt that DDI alerts had increased their potential to prescribe safely. However, only 30% of prescribers felt DDI alerts provided them with what they needed most of the time. Both prescribers and pharmacists agreed that DDI alerts should be accompanied by management alternatives (73% and 82%, respectively) and more detailed information (65% and 89%, respectively). When asked about suggestions for improving DDI alerts, prescribers most preferred including management options whereas pharmacists most preferred making it more difficult to override lethal interactions. Prescribers and pharmacists reported primarily relying on electronic references for general drug information (62% and 55%, respectively) and DDI information (51% and 79%, respectively). ConclusionRespondents 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.

The Role of Technicians in Managing Computerized Drug–Drug Interaction Alerts in Community Pharmacies and the Relationship to Pharmacist Managers' Attitudes

Background: Community pharmacies are where identification and prevention of drug–drug interactions (DDIs) typically occur. Technicians have been shown to play some role in the initial screening of DDIs in community pharmacies. Objectives: To examine the role of technicians in the management of DDI alerts in community pharmacies and the possible relationship to the attitudes of pharmacy managers toward DDI alerts. Methods: A national survey of pharmacist managers in 3,000 community pharmacies was conducted. Data collected included demographics, workload issues, handling of DDIs, and pharmacists' attitudes toward computerized DDI alerts. Results: Of questionnaires returned, 736 could be used. Technicians were more often allowed or sometimes allowed to override lower level DDI alerts without prior review by a pharmacist. However, some pharmacies (2.1%) allowed or sometimes allowed technicians to override interactions with the highest potential clinical significance. Stores with the highest use of technology were less likely to allow technicians to override insignificant interactions. Stores that allow technicians to override clinically significant interactions were less confident in their program's ability to provide meaningful alerts and more likely to agree that alerts are a waste of time and that the volume of alerts makes differentiating important from unimportant DDIs more difficult. Conclusions: Pharmacy technicians play a limited role in the management of DDI alerts, but could be used to help ensure that a patient's profile of medications is up-to-date and determine whether the patient had already been receiving the combination without notable problem.