Performance of community pharmacy drug interaction software

Clinical decision support systems in community pharmacies: a scoping review

OBJECTIVE

Clinical decision support systems (CDSS) were implemented in community pharmacies over 40 years ago. However, unlike CDSS studies in other health settings, few studies have been undertaken to evaluate and improve their use in community pharmacies, where billions of prescriptions are filled every year. The aim of this scoping review is to summarize what research has been done surrounding CDSS in community pharmacies and call for rigorous research in this area.

MATERIALS AND METHODS

Six databases were searched using a combination of controlled vocabulary and keywords relating to community pharmacy and CDSS. After deduplicating the initial search results, 2 independent reviewers conducted title/abstract screening and full-text review. Then, the selected studies were synthesized in terms of investigational/clinical focuses.

RESULTS

The selected 21 studies investigated the perception of and response to CDSS alerts (n = 7), the impact of CDSS alerts (n = 7), and drug-drug interaction (DDI) alerts (n = 8). Three causes of the failures to prevent DDIs of clinical importance have been noted: the perception of and response to a high volume of DDI alerts, a suboptimal performance of CDSS, and a dearth of sociotechnical considerations for managing workload and workflow. Additionally, 7 studies emphasized the importance of utilizing CDSS for a specific clinical focus, ie, antibiotics, diabetes, opioids, and vaccinations.

CONCLUSION

Despite the range of topics dealt in the last 30 years, this scoping review confirms that research on CDSS in community pharmacies is limited and disjointed, lacking a comprehensive approach to highlight areas for improvement and ways to optimize CDSS utilization.

Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

Performance Assessment of Software to Detect and Assist Prescribers with Antimicrobial Drug Interactions: Are all of them Created Equal?

Background: Detecting and managing antimicrobial drug interactions (ADIs) is one of the facets of prudent antimicrobial prescribing. Our aim is to compare the capability of several electronic drug–drug interaction (DDI) checkers to detect and report ADIs. Methods: Six electronic DDI checking platforms were evaluated: Drugs.com^®, Medscape^®, Epocrates^®, Medimecum^®, iDoctus^®, and Guía IF^®. Lexicomp^® Drug Interactions was selected as the gold standard. Ten ADIs addressing different mechanisms were evaluated with every electronic DDI checker. For each ADI, we assessed five dimensions and calculated an overall performance score (maximum possible score: 10 points). The explored dimensions were sensitivity (capability to detect ADI), clinical effect (type and severity), mechanism of interaction, recommended action(s), and documentation (quality of evidence and availability of references). Results: The electronic DDI checkers did not detect a significant proportion of the ADI assessed. The overall performance score ranged between 4.4 (Medimecum) and 8.8 (Drugs.com). Drugs.com was the highest ranked platform in four out of five dimensions (sensitivity, effect, mechanism, and recommended action). Conclusions: There is significant variability in the performance of the available platforms in detecting and assessing ADI. Although some ADI checkers have proven to be very accurate, others missed almost half of the explored interactions.

Drug-drug interactions involving CYP3A4 and p-glycoprotein in hospitalized elderly patients

Polypharmacy is very common in older patients and may be associated with drug-drug interactions. Hepatic cytochrome P450 (notably 3A4 subtype, CYP3A4) is a key enzyme which metabolizes most drugs; P-glycoprotein (P-gp) is a transporter which significantly influences distribution and bioavailability of many drugs. In this study, we assess the prevalence and patterns of potential interactions observed in an hospitalized older cohort (Registro Politerapia Società Italiana di Medicina Interna) exposed to at least two interacting drugs involving CYP3A4 and P-gp at admission, during hospitalization and at discharge. Individuals aged 65 and older (N-4039; mean age 79.2; male 48.1%), hospitalized between 2010 and 2016, were selected. The most common combinations of interacting drugs (relative frequency > 5%) and socio-demographic and clinical factors associated with the interactions were reported. The prevalence of interactions for CYP3A4 was 7.9% on admission, 10.3% during the stay and 10.7% at discharge; the corresponding figures for P-gp interactions were 2.2%, 3.8% and 3.8%. The most frequent interactions were amiodarone-statin for CYP3A4 and atorvastatin-verapamil-diltiazem for P-gp. The prevalence of some interactions, mainly those involving cardiovascular drugs, decreased at discharge, whereas that of others, e.g. those involving neuropsychiatric drugs, increased. The strongest factor associated with interactions was polypharmacy (OR 6.7, 95% CI 5.0-9.2). In conclusion, hospital admission is associated with an increased prevalence, but also a changing pattern of interactions concerning CYP3A4 and P-gp in elderly. Educational strategies and appropriate use of dedicated software seem desirable to limit drug interactions and the inherent risk of adverse events in older patients.

Clinical relevance of alerts from a decision support system, PHARAO, for drug safety assessment in the older adults

Background

PHARAO is a decision support system developed to evaluate the risk for a set of either common or serious side-effects resulting from a combination of pharmacodynamic effects from a patient’s medications. The objective of this study was to investigate the validity of the risk scores for the common side-effects generated by PHARAO in older patients.

Methods

Side-effects included were sedation, constipation, orthostatic symptoms, anticholinergic and serotonergic effects. The alerts generated by PHARAO were tested in 745 persons ≥65 years old. Dispensed prescriptions retrieved from the Swedish prescribed drug register were used to generate the pharmacological risk scores of patients’ medications. Symptoms possibly related to side-effects were extracted from medical records data.

Results

The PHARAO system generated 776 alerts, most often for the risk of anticholinergic symptoms. The total specificity estimates of the PHARAO system were 0.95, 0.89 and 0.78 for high, intermediate and low risk alerts, respectively. The corresponding sensitivity estimates were between 0.12 and 0.37. The negative predictive value was 0.90 and the positive predictive value ranged between 0.20–0.25.

Conclusions

The PHARAO system had a high specificity and negative predictive value to detect symptoms possibly associated with the of patients’ medications, while the sensitivity and positive predictive value were low. The PHARAO system has the potential to minimise the risk of over-alerts in combination with a drug-drug interaction alert system, but should be used in connection with a medical evaluation of the patient.

Electronic supplementary material

The online version of this article (10.1186/s12877-019-1179-y) contains supplementary material, which is available to authorized users.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Interventions to reduce the prescription of inappropriate medicines in older patients

OBJECTIVE:

Identify and critically evaluate systematic reviews addressing the effectiveness of interventions to reduce the number of prescriptions of potentially inappropriate medication to older patients.

METHODS:

This is an overview of systematic reviews. The studies were searched and selected from Medline, Cochrane Library, Embase, CINAHL, Virtual Health Library, and Web of Science databases, combining the terms aged, prescriptions, inappropriate prescribing and potentially inappropriate medication list with their entry terms and other related descriptors, published by June 2017. This study included systematic reviews with or without meta-analysis that addressed the effectiveness of any intervention or combined interventions to reduce the number of prescriptions of potentially inappropriate medications to older patients, without restriction in terms of design, language or date of publication of primary studies. AMSTAR – A MeaSurement Tool to Assess systematic Reviews – was used to evaluate the methodological quality of selected systematic reviews. Study selection and the methodological quality evaluation were performed by two independent evaluators, who resolved any divergence by consensus. The main findings were grouped into thematic categories, defined after a content analysis and discussed qualitatively as narrative synthesis.

RESULTS:

This study analyzed 24 systematic reviews. In terms of study design and methodological quality evaluation, most were systematic reviews of randomized controlled clinical trials and studies of moderate quality, respectively. The interventions were analyzed in five thematic categories: medication review services, pharmaceutical interventions, computerized systems, educational interventions, and others. The interventions analyzed showed good results and most of them helped reduce the number of prescriptions of potentially inappropriate medication to older patients.

CONCLUSIONS:

The systematic reviews included in this overview showed potential benefits of different interventions. However, it was not possible to determine the most effective intervention. Combined interventions are likely to provide better results than isolated interventions.

Evaluation of community pharmacists' knowledge about drug-drug interaction in Central Saudi Arabia

Introduction

Although all implemented and ongoing initiatives, drug-drug interactions (DDIs) are still a global problem. Most published studies about DDIs in Saudi Arabia are carried out in hospital settings. In addition, assessing the knowledge of drug interactions in Saudi Arabia is limited. The aim of our study is to evaluate the knowledge of potential common drug-drug interactions among community pharmacists particularly in Saudi Arabia.

Methodology

A crosses-sectional study utilizing a self- administered questionnaire was conducted among community pharmacy in Riyadh city Saudi Arabia. DDIs' knowledge was assessed by 26 drug pairs. Community pharmacists were asked to select the DDIs as “contraindication”, “may be used together with monitoring”, “no interaction” and “not sure”.

Results

A total of 283 of community pharmacists completed the survey with response rate of 80.9%. Among the 26 drug pairs only 5 of them were identified correctly by most of the participants. To add more 3 out of the 5 pairs had a cutoff of less than 10% between the correct and wrong answer, meaning there still a majority that couldn't identify the correct answer. All the 26 pairs had a statistically significant difference between the correct and incorrect answer.

Conclusion

The results of this study showed that knowledge of community pharmacists about DDIs was inadequate. Community pharmacist should have specific courses in drug interactions to cover the most possible interactions that can be seen in this setting.

Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support

Objective

To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice.

Methods

Drugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support.

Results

The KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8-99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions.

Conclusion

The commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list.

Potential Drug-Drug Interactions among Patients prescriptions collected from Medicine Out-patient Setting

Objective:

To identify and evaluate the frequency, severity, mechanism and common pairs of drug-drug interactions (DDIs) in prescriptions by consultants in medicine outpatient department.

Methods:

This cross sectional descriptive study was done by Pharmacology department of Bahria University Medical & Dental College (BUMDC) in medicine outpatient department (OPD) of a private hospital in Karachi from December 2015 to January 2016. A total of 220 prescriptions written by consultants were collected. Medications given with patient's diagnosis were recorded. Drugs were analyzed for interactions by utilizing Medscape drug interaction checker, drugs.com checker and stockley`s drug interactions index. Two hundred eleven prescriptions were selected while remaining were excluded from the study because of unavailability of the prescribed drugs in the drug interaction checkers.

Results:

In 211 prescriptions, two common diagnoses were diabetes mellitus (28.43%) and hypertension (27.96%). A total of 978 medications were given. Mean number of medications per prescription was 4.6. A total of 369 drug-drug interactions were identified in 211 prescriptions (175%). They were serious 4.33%, significant 66.12% and minor 29.53%. Pharmacokinetic and pharmacodynamic interactions were 37.94% and 51.21% respectively while 10.84% had unknown mechanism. Number wise common pairs of DDIs were Omeprazole-Losartan (S), Gabapentine- Acetaminophen (M), Losartan-Diclofenac (S).

Conclusion:

The frequency of DDIs is found to be too high in prescriptions of consultants from medicine OPD of a private hospital in Karachi. Significant drug-drug interactions were more and mostly caused by Pharmacodynamic mechanism. Number wise evaluation showed three common pairs of drugs involved in interactions.

Community health integration through pharmacy process and ergonomics redesign (CHIPPER)

As the expansion and utilisation of community pharmacy systems increases, so does the risk for an adverse drug event to occur. In attempts to mitigate this risk, many community pharmacies implement health information technology (IT); however, there are challenges in integrating the wider systems components necessary for a successful implementation with minimal unintended consequences. The purpose of this paper is to introduce a Community Health Integration through Pharmacy Process and Ergonomics Redesign (CHIPPER) framework, which explores the multiple angles of health IT integration to support medication delivery processes in community pharmacy systems. Specifically, CHIPPER identifies the information flows that occur between different parts of the system (initiation, upstream, midstream and downstream) with varying end-users and tasks related to medication delivery processes. In addition to the justification and presentation of the CHIPPER model, this paper reviews several broad applications for CHIPPER and presents two example studies that demonstrate the CHIPPER framework. Practitioner Summary: Most medication delivery in the US occurs through outpatient-based community pharmacy practice. Community pharmacies are challenged by inconsistent and incomplete information flow and technology integration between providers, pharmacy practitioners and patients. This paper presents a framework for improved healthcare systems engineering analysis of pharmacy practice, with case study examples.

An Assessment of Free, Online Drug-Drug Interaction Screening Programs (DSPs)

Objective

This study evaluated the content and usability of four free, online drug-drug interaction screening programs (DSPs) and one proprietary DSP (CD-ROM format) requiring an annual fee.

Methods

Free, online DSPs that obtain their drug interaction information from unique providers were located through a comprehensive search of the Internet. For comparison purposes, a well-established, fee-based DSP was also included in the study. A tool for evaluating the DSPs for usability and quality of content was created. The usability characteristics assessed included ease of use, ability to support more than two drug entries at the same time, speed, and multifunctionality. Evaluation of content was based on accuracy, completeness, references, drug interaction management, and readability. A five-member panel of clinical pharmacists and drug information specialists in academia and the pharmaceutical industry validated the survey tool.

Results

Based on overall usability, Drkoop.com (Multum) rated higher than the other free DSPs, but below the comparator, DrugReax (Micromedex). Walgreens.com (Medi-Span) and Medscape (First Data-Bank) ranked lowest among all the DSPs in terms of usability. Based on content, Walgreens ranked first and Clinical Pharmacology 2000 (Gold Standard Multimedia) ranked last (P= 0.015, ANOVA).

Conclusion

All of the evaluated DSPs were useful. Some were better than others depending on the assessment category. Walgreens rated the highest in terms of accuracy, completeness, and drug interaction management information. Drkoop.com rated the highest with regard to comprehensive references and pharmacist-assessed, consumer-friendly language. All of the free, online DSPs could improve their speed of information retrieval.

Recommendations for selecting drug-drug interactions for clinical decision support

PURPOSE

Recommendations for including drug-drug interactions (DDIs) in clinical decision support (CDS) are presented.

SUMMARY

A conference series was conducted to improve CDS for DDIs. A work group consisting of 20 experts in pharmacology, drug information, and CDS from academia, government agencies, health information vendors, and healthcare organizations was convened to address (1) the process to use for developing and maintaining a standard set of DDIs, (2) the information that should be included in a knowledge base of standard DDIs, (3) whether a list of contraindicated drug pairs can or should be established, and (4) how to more intelligently filter DDI alerts. We recommend a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization. We outline key DDI information needed to help guide clinician decision-making. We recommend judicious classification of DDIs as contraindicated and more research to identify methods to safely reduce repetitive and less-relevant alerts.

CONCLUSION

An expert panel with a centralized organizer or convener should be established to develop and maintain a standard set of DDIs for CDS in the United States. The process should be evidence driven, transparent, and systematic, with feedback from multiple stakeholders for continuous improvement. The scope of the expert panel's work should be carefully managed to ensure that the process is sustainable. Support for research to improve DDI alerting in the future is also needed. Adoption of these steps may lead to consistent and clinically relevant content for interruptive DDIs, thus reducing alert fatigue and improving patient safety.

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.

A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness

Objective:

Drug–drug interactions (DDIs) can cause failure in treatment and adverse events. DDIs screening software is an important tool to aid clinicians in the detection and management of DDIs. However, clinicians should be aware of the advantages and limitations of these programs. We compared the ability of five common DDI programs to detect clinically important DDIs.

Methods:

Lexi-Interact, Micromedex Drug Interactions, iFacts, Medscape, and Epocrates were evaluated. The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting DDIs. The accuracy of each program was identified using 360 unknown pair interactions, taken randomly from prescriptions, and forty pairs of clinically important ones. The major reference was a clinical pharmacist alongside the Stockley's Drug Interaction and databases including PubMed, Scopus, and Google Scholar. Comprehensiveness of each program was determined by the number of components in the drug interaction monograph. The aggregate score for accuracy and comprehensiveness was calculated.

Findings:

Scoring 250 out of possible 400 points, Lexi-Interact and Epocrates, provided the most accurate software programs. Micromedex, Medscape, and iFacts ranked third, fourth, and fifth, scoring 236, 202, and 191, respectively. In comprehensiveness test, iFacts showed the highest score, 134 out of possible 134 points, whereas Lexi-Interact rated second, with a score of 120. Scoring 370 and 330 out of possible 534 points, Lexi-Interact and Micromedex, respectively, provided the most competent, complete, and user-friendly applications.

Conclusion:

Lexi-Interact and Micromedex showed the best performances. An increase in sensitivity is possible by the combination of more than one programs and expert pharmacist intervention.

Toward a complete dataset of drug-drug interaction information from publicly available sources

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

Assessing the safety features of electronic patient medication record systems used in community pharmacies in England

Aims

To evaluate the ability of electronic patient medication record (ePMR) systems used in community pharmacies in England to detect and alert users about clinical hazards, errors and other safety problems.

Methods

Between September 2012 and November 2012, direct on-site observational data about the performance of ePMR systems were collected from nine sites. Twenty-eight scenarios were developed by consensus agreement between a general practitioner and two community pharmacists. Each scenario was entered into the ePMR system, and the results obtained from the assessment of six unique systems in nine sites, in terms of the presence or absence of an alert, were recorded onto a prespecified form.

Results

None of the systems produced the correct responses for all of the 28 scenarios tested. Only two systems provided an alert to penicillin sensitivity. No dose or frequency check was observed when processing a prescription for methotrexate. One system did not warn about nonsuitability of aspirin prescribed to a child of 14 years of age. In another system, it was not possible to record a patient's pregnancy status. None of the six systems provided any warning for diclofenac overdose, high initiation dose of morphine sulfate or significant dose increase. Only one of the systems did not produce any spurious alerts.

Conclusions

The performance of the ePMR systems tested was variable and suboptimal. The findings suggest the need for minimum specifications and standards for ePMR systems to ensure consistency of performance.

Identifying and Communicating Clinically Meaningful Drug-Drug Interactions

OBJECTIVE

Providing care to patients with comorbid medical problems may result in complicated, multiple drug therapy regimens, increasing the risk of clinically meaningful drug-drug interactions (DDIs). The purpose of this article is to describe the prevalence of DDIs and provide examples on how to identify and intervene on DDIs.

METHODS

We described DDI data from the Utah Drug Regimen Review Center, where adult Medicaid patients were reviewed by pharmacists from 2005 to 2009. Patients were selected by the number of prescriptions filled per month (>7) or having a high RxRisk score.

SUMMARY

A total of 8860 patients were reviewed, and 16.6% had at least 1 clinically meaningful DDI. Patients with DDIs were slightly younger (mean age 45.2 vs 48.2), more likely to be female (75.0% vs 68.9%), and had more prescriptions per month (13.4 vs 12.5) compared to patients without (P < .001). Pharmacodynamic DDIs were more prevalent (80.2%) than pharmacokinetic. Pharmacodynamic DDIs mainly occurred with drugs used to treat psychiatric/seizure/sleep disorders (69.4%) and pain/migraine (56.6%). Pharmacokinetic DDIs mainly occurred with drugs used to treat psychiatric/seizure/sleep disorders (53.2%), cardiovascular diseases (46.3%), and infectious diseases (29.6%).

CONCLUSIONS

Clinically meaningful DDIs are common in patients with complex medication regimens. A systematic approach for identifying DDIs, determining clinical significance, formulating patient-specific recommendations, and communicating recommendations is important in pharmacy practice.

Safety of medication use in primary care

BACKGROUND

Medication errors are one of the leading causes of harmin health care. Review and analysis of errors have often emphasized their preventable nature and potential for reoccurrence. Of the few error studies conducted in primary care to date, most have focused on evaluating individual parts of the medicines management system. Studying individual parts of the system does not provide a complete perspective and may further weaken the evidence and undermine interventions.

AIM AND OBJECTIVES

The aim of this review is to estimate the scale of medication errors as a problem across the medicines management system in primary care. Objectives were: To review studies addressing the rates of medication errors, and To identify studies on interventions to prevent medication errors in primary care.

METHODS

A systematic search of the literature was performed in PubMed (MEDLINE), International Pharmaceutical Abstracts (IPA), Embase, PsycINFO, PASCAL, Science Direct, Scopus, Web of Knowledge, and CINAHL PLUS from 1999 to November, 2012. Bibliographies of relevant publications were searched for additional studies.

KEY FINDINGS

Thirty-three studies estimating the incidence of medication errors and thirty-six studies evaluating the impact of error-prevention interventions in primary care were reviewed. This review demonstrated that medication errors are common, with error rates between <1% and >90%, depending on the part of the system studied, and the definitions and methods used. The prescribing stage is the most susceptible, and that the elderly (over 65 years), and children (under 18 years) are more likely to experience significant errors. Individual interventions demonstrated marginal improvements in medication safety when implemented on their own.

CONCLUSION

Targeting the more susceptible population groups and the most dangerous aspects of the system may be a more effective approach to error management and prevention. Co-implementation of existing interventions at points within the system may offer time- and cost-effective options to improving medication safety in primary care.

Detection of potential drug-drug interactions for outpatients across hospitals

The National Health Insurance Administration (NHIA) has adopted smart cards (or NHI-IC cards) as health cards to carry patients' medication histories across hospitals in Taiwan. The aims of this study are to enhance a computerized physician order entry system to support drug-drug interaction (DDI) checking based on a patient's medication history stored in his/her NHI-IC card. For performance evaluation, we developed a transaction tracking log to keep track of every operation on NHI-IC cards. Based on analysis of the transaction tracking log from 1 August to 31 October 2007, physicians read patients' NHI-IC cards in 71.01% (8,246) of patient visits; 33.02% (2,723) of the card reads showed at least one medicine currently being taken by the patient, 82.94% of which were prescribed during the last visit. Among 10,036 issued prescriptions, seven prescriptions (0.09%) contained at least one drug item that might interact with the currently-taken medicines stored in NHI-IC cards and triggered pop-up alerts. This study showed that the capacity of an NHI-IC card is adequate to support DDI checking across hospitals. Thus, the enhanced computerized physician order entry (CPOE) system can support better DDI checking when physicians are making prescriptions and provide safer medication care, particularly for patients who receive medication care from different hospitals.

Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients

BACKGROUND

Clinical decision support software (CDSS) solutions can automatically identify drug interactions and thereby aim to improve drug safety. However, data on the comparative performance of different CDSS to detect and appropriately classify interactions in real-life prescription datasets is limited.

OBJECTIVE

The aim of this study was to compare the results from two different CDSS analysing the pharmacotherapy of a large population of psychiatric inpatients for drug interactions.

METHODS

We performed mass analyses of cross-sectional patient-level prescriptions from 84,625 psychiatric inpatients using two CDSS - MediQ and ID PHARMA CHECK(®). Interactions with the highest risk ratings and the most frequent ratings were reclassified according to the Zurich Interaction System (ZHIAS), a multidimensional classification that incorporates the OpeRational ClassificAtion of Drug Interactions (ORCA) and served as a reference standard.

RESULTS

MediQ reported 6,133 unique interacting combinations responsible for 270,617 alerts affecting 63,454 patients. ID PHARMA CHECK(®) issued 5,400 interactions and 157,489 alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programmes, but overlap increased with higher risk rating. MediQ reported high-risk interactions in 2.5 % of all patients, compared with 5 % according to ID PHARMA CHECK(®). The positive predictive value for unique major alerts to be (provisionally) contraindicated according to ORCA was higher for MediQ (0.63) than for either of the two ID PHARMA CHECK(®) components (0.42 for hospINDEX and 0.30 for ID MACS). MediQ reported more interactions, and ID PHARMA CHECK(®) tended to classify interactions into a higher risk class, but overall both programmes identified a similar number of (provisionally) contraindicated interactions according to ORCA criteria. Both programmes identified arrhythmia as the most frequent specific risk associated with interactions in psychiatric patients.

CONCLUSIONS

CDSS can be used for mass-analysis of prescription data and thereby support quality management. However, in clinical practice CDSS impose an overwhelming alert burden on the prescriber, and prediction of clinical relevance remains a major challenge. Only a small subset of yet to be determined alerts appears suitable for automated display in clinical routine.

The evidence for the effectiveness of safety alerts in electronic patient medication record systems at the point of pharmacy order entry: a systematic review

BACKGROUND

Electronic Patient Medication Record (ePMR) systems have important safety features embedded to alert users about potential clinical hazards and errors. To date, there is no synthesis of evidence about the effectiveness of these safety features and alerts at the point of pharmacy order entry. This review aims to systematically explore the literature and synthesise published evidence about the effectiveness of safety features and alerts in ePMR systems at the point of pharmacy order entry, in primary and secondary care.

METHODS

We searched MEDLINE, EMBASE, Inspec, International Pharmaceutical Abstracts, PsycINFO, CINHAL (earliest entry to March 2012) and reference lists of articles. Two reviewers examined the titles and abstracts, and used a hierarchical template to identify comparative design studies evaluating the effectiveness of safety features and alerts at the point of pharmacy order entry. The two reviewers independently assessed the quality of the included studies using Cochrane Collaboration's risk of bias tool.

RESULTS

Three randomised trials and two before-after studies met our criteria. Four studies involved integrated care facilities and one was hospital-based. The studies were all from the United States (US). The five studies demonstrated statistically significant reduction in medication errors in patients with renal insufficiency, pregnant women dispensed US Food Drug and Administration (FDA) risk category D (evidence of fetal risk but therapeutic benefits can outweigh the risk) or X (evidence suggests that risk to the fetus outweighs therapeutic benefits) medication, first dispensing of inappropriate medications in patients aged 65 and above, co-dispensing of interacting drugs, and adverse drug events related to hyperkalaemia.

CONCLUSIONS

This systematic review shows that the safety features of ePMR systems are effective in alerting users about potential clinical hazards and errors during pharmacy order entry. There are however, problems such as false alerts and inconsistencies in alert management. More studies are needed from other countries and pharmacy practice settings to assess the effectiveness of electronic safety features and alerts in preventing error and reducing harm to patients.

Clinically significant interactions between antiretroviral and co-prescribed drugs for HIV-infected children: profiling and comparison of two drug databases

BACKGROUND

Drug-drug interactions are an important therapeutic challenge among human immunodeficiency virus-infected patients. Early recognition of drug-drug interactions is important, but conflicts do exist among drug compendia on drug interaction information. We aimed to evaluate the consistencies of two drug information resources with regards to the severity rating and categorization of the potential interactions between antiretroviral and co-prescribed drugs.

METHODS

We reviewed the case files of human immunodeficiency virus-infected children who were receiving treatment at the human immunodeficiency virus (HIV) clinic of the Lagos University Teaching Hospital, Idi Araba, between January 2005 and December 2010. All of the co-prescribed and antiretroviral drug pairs were screened for potential interactions using the Medscape Drug Interaction Checker and the Monthly Index of Medical Specialties Interaction Checker. Drug-drug interaction (DDI) severity and categorization were rated on a scale of A (no known interaction); B (minor/no action needed); C (moderate/monitor therapy); D (major/therapy modification); and X (contraindicated/avoid combination).

RESULTS

A total of 280 patients were at risk of 596 potential DDIs. The databases showed discrepancies, with Medscape database identifying 504 (84.6%) and USA MIMS database identifying 302 (50.7%) potential DDIs. Simultaneous identification of DDIs by both databases occurred for only 275 (46.1%) listed interactions. Both databases have a weak correlation on the severity rating (rs = 0.45; P < 0.001). The most common DDIs identified by the databases were nevirapine and artemisinin-based combination therapy (170; 28.5%), nevirapine and fluconazole (58; 9.7%), and zidovudine and fluconazole (55; 9.2%). There were 272 (45.6%) interaction severity agreements between the databases.

CONCLUSION

Discrepancies occurred in DDI listings between Medscape and USA MIMS databases. Health care professionals may need to consult more than one DDI information database to ensure safe concomitant prescribing for HIV patients.

Assessing cardiovascular drug safety for clinical decision-making

Optimal therapeutic decision-making requires integration of patient-specific and therapy-specific information at the point of care, particularly when treating patients with complex cardiovascular conditions. The formidable task for the prescriber is to synthesize information about all therapeutic options and match the best treatment with the characteristics of the individual patient. Computerized decision support systems have been developed with the goal of integrating such information and presenting the acceptable therapeutic options on the basis of their effectiveness, often with limited consideration of their safety for a specific patient. Assessing the safety of therapies relative to each patient is difficult, and sometimes impossible, because the evidence required to make such an assessment is either imperfect or does not exist. In addition, many of the alerts sent to prescribers by decision-support systems are not perceived as credible, and 'alert fatigue' causes warnings to be ignored putting patients at risk of harm. The CredibleMeds.org and BrugadaDrugs.org websites are prototypes for evidence-based sources of safety information that rank drugs for their risk of a specific form of drug toxicity-in these cases, drug-induced arrhythmias. Broad incorporation of this type of information in electronic prescribing algorithms and clinical decision support could speed the evolution of safe personalized medicine.

Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records

OBJECTIVE

Alert fatigue represents a common problem associated with the use of clinical decision support systems in electronic health records (EHR). This problem is particularly profound with drug-drug interaction (DDI) alerts for which studies have reported override rates of approximately 90%. The objective of this study is to report consensus-based recommendations of an expert panel on DDI that can be safely made non-interruptive to the provider's workflow, in EHR, in an attempt to reduce alert fatigue.

METHODS

We utilized an expert panel process to rate the interactions. Panelists had expertise in medicine, pharmacy, pharmacology and clinical informatics, and represented both academic institutions and vendors of medication knowledge bases and EHR. In addition, representatives from the US Food and Drug Administration and the American Society of Health-System Pharmacy contributed to the discussions.

RESULTS

Recommendations and considerations of the panel resulted in the creation of a list of 33 class-based low-priority DDI that do not warrant being interruptive alerts in EHR. In one institution, these accounted for 36% of the interactions displayed.

DISCUSSION

Development and customization of the content of medication knowledge bases that drive DDI alerting represents a resource-intensive task. Creation of a standardized list of low-priority DDI may help reduce alert fatigue across EHR.

CONCLUSIONS

Future efforts might include the development of a consortium to maintain this list over time. Such a list could also be used in conjunction with financial incentives tied to its adoption in EHR.

Clinically important drug-drug interactions in primary care

WHAT IS KNOWN AND OBJECTIVE

Drug-drug interactions (DDIs) cause considerable morbidity and mortality worldwide and may lead to hospital admission. Sophisticated computerized drug information and monitoring systems, more recently established in many of the emerging economies, including Malaysia, are capturing useful information on prescribing. Our aim is to report on an investigation of potentially serious DDIs, using a university primary care-based system capturing prescription records from its primary care services.

METHODS

We retrospectively collected data from two academic years over 20 months from computerized databases at the Universiti Sains Malaysia (USM) from users of the USM primary care services.

RESULTS AND DISCUSSION

Three hundred and eighty-six DDI events were observed in a cohort of 208 exposed patients from a total of 23,733 patients, representing a 2-year period prevalence of 876·4 per 100,000 patients. Of the 208 exposed patients, 138 (66·3%) were exposed to one DDI event, 29 (13·9%) to two DDI events, 15 (7·2%) to three DDI events, 6 (2·9%) to four DDI events and 20 (9·6%) to more than five DDI events. Overall, an increasing mean number of episodes of DDIs was noted among exposed patients within the age category ≥70 years (P=0·01), an increasing trend in the number of medications prescribed (P<0·001) and an increasing trend in the number of long-term therapeutic groups (P<0·001).

WHAT IS NEW AND CONCLUSION

We describe the prevalence of clinically important DDIs in an emerging economy setting and identify the more common potentially serious DDIs. In line with the observations in developed economies, a higher number of episodes of DDIs were seen in patients aged ≥70 years and with more medications prescribed. The easiest method to reduce the frequency of DDIs is to reduce the number of medications prescribed. Therapeutic alternatives should be selected cautiously.

Evaluation of drug-drug interaction screening software combined with pharmacist intervention

BACKGROUND

Drug-drug interactions (DDI) in hospitalized patients are highly prevalent and an important source of adverse drug reactions. DI computerized screening system can prevent the occurrence of some of these events.

OBJECTIVE

To evaluate the impact of drug-drug interaction (DDI) screening software combined with active intervention in preventing drug interactions.

SETTING

The study was conducted at General Hospital of Vitória da Conquista (HGVC), Brazil.

METHOD

A quasi-experimental study was used to evaluate the impact of IM-Pharma, a locally developed drug-drug interaction screening system, coupled with pharmacist intervention on adverse drug events in the hospital setting.

MAIN OUTCOME MEASURE

The proportion of patients co-prescribed two interacting drugs were measured in two phases, prior the implementation of IM-Pharma and during the intervention period. DDI rates per 100 patient days were calculated before and after implementation. Risk ratios were estimated by Poisson regression models.

RESULTS

A total of 6,834 instances of drug-drug interactions were identified; there was an average of 3.3 DDIs per patient in phase one and 2.5 in phase two, a reduction of 24 % (P = 0.03). There was a 71 % reduction in high-severity drug-drug interaction (P < 0.01). The risk for all DDIs decreased 50 % after the implementation of IM-Pharma (P < 0.01), and for those with high-severity, the reduction was 81 % (P < 0.01).

CONCLUSION

The performance of IM-Pharma combined with pharmacist intervention was positive with an expressive reduction in the risk of DDIs.

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients

PURPOSE

The comparative evaluation of clinical decision support software (CDSS) programs regarding their sensitivity and positive predictive value for the identification of clinically relevant drug interactions.

METHODS

In this research, we used a cross-sectional study that identified potential drug interactions using the CDSS MediQ and the ID PHARMA CHECK in 484 neurological inpatients. Interactions were reclassified according to the Zurich Interaction System, a multidimensional classification that incorporates the Operational Classification of Drug Interactions.

RESULTS

In 484 patients with 2812 prescriptions, MediQ and ID PHARMA CHECK generated a total of 1759 and 1082 alerts, respectively. MediQ identified 658 unique potentially interacting combinations, 8 classified as "high danger," 164 as "average danger," and 486 as "low danger." ID PHARMA CHECK detected 336 combinations assigned to one or several of 12 risk and management categories. Altogether, both CDSS issued alerts relating to 808 unique potentially interacting combinations. According to the Zurich Interaction System, 6 of these were contraindicated, 25 were provisionally contraindicated, 190 carried a conditional risk, and 587 had a minimal risk of adverse events. The positive predictive value for alerts having at least a conditional risk was 0.24 for MediQ and 0.48 for ID PHARMA CHECK.

CONCLUSIONS

CDSS showed major differences in the identification and grading of interactions, and many interactions were only identified by one of the two CDSS. For both programs, only a small proportion of all identified interactions appeared clinically relevant, and the selected display of alerts that imply management changes is a key issue in the further development and local setup of such programs.

Impact of a drug-drug interaction intervention on pharmacy and medical students' knowledge and attitudes: a 1-year follow-up

BACKGROUND

There have been many interventions aimed at improving retention of drug-drug interaction (DDI) knowledge of health care professionals. Much less is known about their retention of such knowledge for extended periods of time after an educational intervention.

OBJECTIVES

To evaluate pharmacy and medical students' knowledge retention and attitudes 1 year after participating in an educational session on DDIs.

METHODS

This study used a pre-post design with an assessment of DDI knowledge and attitude by pharmacy and medical students before and after the final didactic year of their professional education. The intervention was a 1-hour program.

RESULTS

A total of 74 of 193 students (38%) completed the pre, post, and final questionnaire. The median numbers of correctly identified DDIs before the program were 8 and 7 for pharmacy and medical students, respectively, out of a possible score of 15. One year after, the median identification knowledge scores were 12 and 8, respectively, for pharmacy and medical students. The median difference scores of correctly managed DDIs on this evaluation 1 year after the program were -4 and -8 for pharmacy and medical students, respectively (P<.05).

CONCLUSION

This study found that the ability to identify important DDIs is poor among both pharmacy and medical students 1 year after being exposed to the educational session.

Pharmacy students' retention of knowledge of drug-drug interactions

OBJECTIVES

To evaluate pharmacy students' drug-drug interaction (DDI) knowledge retention over 1 year and to determine whether presenting DDI vignettes increased knowledge retention.

METHODS

A knowledge assessment tool was distributed to fourth-year pharmacy students before and after completing a DDI educational session. The questionnaire was re-administered after 1 year to assess knowledge retention. During the intervening year, students had the option of presenting DDI case vignettes to preceptors and other health professionals as part of their advanced pharmacy practice experiences (APPEs).

RESULTS

Thirty-four of 78 pharmacy students completed both the post-intervention and 1-year follow-up assessments. Students' knowledge of 4 DDI pairs improved, knowledge of 3 DDI pairs did not change, and knowledge of the remainder of DDI pairs decreased. Average scores of the 18 students who completed all tests and presented at least 1 vignette during their APPEs were higher on the 1-year follow-up assessment than students who did not, suggesting greater DDI knowledge retention (p = 0.04).

CONCLUSION

Although pharmacy students' overall DDI knowledge decreased in the year following an educational session, those who presented vignettes to health professionals retained more DDI knowledge, particularly on those DDIs for which they gave presentations. Other methods to enhance pharmacy students' retention of DDI knowledge of clinically important DDIs are needed.

Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System)

PURPOSE

The current study aimed at identifying and quantifying critical drug interactions in neurological inpatients using clinical decision support software (CDSS). Reclassification of interactions with a focus on clinical management aimed to support the development of CDSS with higher efficacy to reduce overalerting and improve medication safety in clinical practice.

METHODS

We conducted a cross-sectional study in consecutive patients admitted to the neurology ward of a tertiary care hospital. We developed a customized interface for mass analysis with the CDSS MediQ, which we used for automated retrospective identification of drug interactions during the first day of hospitalization. Interactions were reclassified according to the Zurich Interaction System (ZHIAS), which incorporates the Operational Classification of Drug Interactions (ORCA). Dose adjustments for renal impairment were also evaluated.

RESULTS

In 484 patients with 2812 prescriptions, MediQ generated 8 "high danger," 518 "average danger," and 1233 "low danger" interaction alerts. According to ZHIAS, 6 alerts involved contraindicated and 33 alerts involved provisionally contraindicated combinations, and 327 alerts involved a conditional and 1393 alerts involved a minimal risk of adverse outcomes. Thirty-five patients (6.2%) had at least one combination that was at least provisionally contraindicated. ZHIAS also provides categorical information on expected adverse outcomes and management recommendations, which are presented in detail. We identified 13 prescriptions without recommended dose adjustment for impaired renal function.

CONCLUSIONS

MediQ detected a large number of drug interactions with variable clinical relevance in neurological inpatients. ZHIAS supports the selection of those interactions that require active management, and the effects of its implementation into CDSS on medication safety should be evaluated in future prospective studies.

Drug interaction and pharmacist

The topic of drug–drug interactions has received a great deal of recent attention from the regulatory, scientific, and health care communities worldwide. Nonsteroidal anti-inflammatory drugs, antibiotics and, in particular, rifampin are common precipitant drugs prescribed in primary care practice. Drugs with a narrow therapeutic range or low therapeutic index are more likely to be the objects for serious drug interactions. Object drugs in common use include warfarin, fluoroquinolones, antiepileptic drugs, oral contraceptives, cisapride, and 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors. The pharmacist, along with the prescriber has a duty to ensure that patients are aware of the risk of side effects and a suitable course of action should they occur. With their detailed knowledge of medicine, pharmacists have the ability to relate unexpected symptoms experienced by patients to possible adverse effects of their drug therapy.

Assessment tool for pharmacy drug-drug interaction software

OBJECTIVES

To assess the performance of pharmacy clinical decision support (CDS) systems for drug-drug interaction (DDI) detection and to identify approaches for improving the ability to recognize important DDIs.

PRACTICE DESCRIPTION

Pharmacists rely on CDS systems to assist in the identification of DDIs, and research suggests that these systems perform suboptimally. The software evaluation tool described here may be used in all pharmacy settings that use electronic decision support to detect potential DDIs, including large and small community chain pharmacies, community independent pharmacies, hospital pharmacies, and governmental facility pharmacies.

PRACTICE INNOVATION

A tool is provided to determine the ability of pharmacy CDS systems to identify established DDIs. It can be adapted to evaluate potential DDIs that reflect local practice patterns and patient safety priorities. Beyond assessing software performance, going through the evaluation processes creates the opportunity to evaluate inadequacies in policies, procedures, workflow, and training of all pharmacy staff relating to pharmacy information systems and DDIs.

CONCLUSION

The DDI evaluation tool can be used to assess pharmacy information systems' ability to recognize relevant DDIs. Suggestions for improvement include determining whether the software allows for customization, creating standard policies for handling specific interactions, and ensuring that drug knowledge database updates occur frequently.

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

PURPOSE

To evaluate and compare the drug-drug interaction (DDI) knowledge of pharmacy, medical, and nurse practitioner (NP) students who are beginning supervised clinical practice.

DATA SOURCES

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.

CONCLUSIONS

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.

IMPLICATIONS FOR PRACTICE

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.

Ability of pharmacy clinical decision-support software to alert users about clinically important drug-drug interactions

OBJECTIVE

Pharmacy clinical decision-support (CDS) software that contains drug-drug 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 pairs-13 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 47-100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23-1.0); median specificity was 1.0 (range 0.83-1.0); median positive predictive value was 1.0 (range 0.88-1.0); and median negative predictive value was 0.75 (range 0.38-1.0).

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.

Functionality test for drug safety alerting in computerized physician order entry systems

PURPOSE

To evaluate the functionality of drug safety alerting in hospital computerized physician order entry (CPOE) systems by a newly developed comprehensive test.

METHODS

Comparative evaluation of drug safety alerting quality in 6 different CPOEs used in Dutch hospitals, by means of 29 test items for sensitivity and 19 for specificity in offices of CPOE system vendors. Sensitivity and specificity were calculated for the complete test, and for the categories "within-order checks", "patient-specific checks", and "checks related to laboratory data and new patient conditions". Qualitative interviews with 16 hospital pharmacists evaluating missing functionality and corresponding pharmacy checks.

RESULTS

Sensitivity ranged from 0.38 to 0.79 and specificity from 0.11 to 0.84. The systems achieved the same ranking for sensitivity as for specificity. Within-order checks and patient-specific checks were present in all systems; alert generation or suppression due to laboratory data and new patient conditions was largely absent. Hospital pharmacists unanimously rated checks on contra-indications (absent in 2 CPOEs) and dose regimens less than once a day (absent in 4 CPOEs) as important. Pharmacists' opinions were more divergent for other test items. A variety of pharmacy checks were used, and clinical rules developed, to address missing functionality.

CONCLUSIONS

Our test revealed widely varying functionality and appeared to be highly discriminative. Basic clinical decision support was partly absent in two CPOEs. Hospital pharmacists did not rate all test items as important and tried to accommodate the lacking functionality by performing additional checks and developing clinical rules.

Potential drug-drug interactions in prescriptions dispensed in community pharmacies in Greece

OBJECTIVES

To evaluate the nature, type and prevalence of potential drug-drug interactions (DDIs) in prescriptions dispensed in community pharmacies in Thessaloniki, Greece. Secondary objectives included the classification of DDIs as per pharmacotherapeutic class of the medications and the investigation of the relationship between medical specialties and the frequency of potential DDIs, as well as the relationship between DDIs and prescription size. Setting DDIs are a common cause of adverse drug reactions (ADRs) among patients using multiple drug therapy. In Greece a reliable computerized surveillance system for monitoring potential DDIs is not yet fully established. As a result, the prevalence of such DDIs in prescriptions dispensed by community pharmacies in Greece is unknown.

METHODS

We conducted a prospective, descriptive study. Over a 3-month period (November 2007-January 2008), a total of 1,553 handwritten prescriptions were collected from three community pharmacies in Thessaloniki, Greece. The prescriptions were processed using the Drug Interactions Checker within the www.drugs.com database. The identified potential DDIs were categorized into two classes, major and moderate, according to their level of clinical significance.

MAIN OUTCOME MEASURES

Overall 213 prescriptions had one or more potential DDIs and a total of 287 major and moderate DDIs were identified. Potential DDIs were identified in 18.5% of all prescriptions. Major DDIs were identified in 1.9% of all prescriptions and represented 10.5% of all DDIs detected, whereas moderate DDIs were identified in 16.6% of all prescriptions and represented 89.5% of all DDIs detected. The rate of DDIs increased with prescription size. The most common drug involved in major DDIs was amiodarone which interacts with potassium-wasting diuretics, digoxin, simvastatin and acenocoumarol.

CONCLUSIONS

Our results indicate that patients in Greece are at risk of ADRs caused by medications due to potential DDIs. An appropriate surveillance system for monitoring such interactions should be implemented and physicians should be more aware of potentially harmful DDIs. Pharmacists can contribute to the detection and prevention of drug-related injuries, especially of clinically meaningful DDIs that pose a potential risk to patient safety.

An evidence-based assessment of the clinical significance of drug-drug interactions between disease-modifying antirheumatic drugs and non-antirheumatic drugs according to rheumatologists and pharmacists

BACKGROUND

Clinically relevant drug-drug interactions (DDIs) must be recognized in a timely manner and managed appropriately to prevent adverse drug reactions or therapeutic failure. Because the evidence for most DDIs is based on case reports or poorly documented clinical information, there is a need for better assessment of their clinical relevance.

OBJECTIVE

This study evaluates the interdisciplinary agreement between rheumatologists and clinical (hospital) pharmacists in assessing the clinical relevance of DDIs with disease-modifying antirheumatic drugs (DMARDs) and non-DMARD medications.

METHODS

Potential DDIs were identified from the medical literature using MEDLINE and EMBASE for the years 1968-2009. The following search terms were used for the key word, title, and abstract sections of the publications: interaction(s), DMARD, disease-modifying antirheumatic drug(s), antirheumatic, rheumatology, rheumatoid arthritis, and the names of the individual DMARDs of interest (abatacept, adalimumab, anakinra, auranofin, aurothioglucose, aurothiomalate, d-penicillamine, etanercept, gold, [hydroxy]-chloroquine, interleukin-1 receptor antagonist, IL1-RA, infliximab, leflunomide, methotrexate, rituximab, and sulfasalazine/sulphasalazine). Reference lists of the retrieved publications were searched for further information on potential DDIs. All pharmacodynamic or pharmacokinetic DDIs between a DMARD and a non-DMARD identified were included in the study, with the exception of evidence regarding DMARD doses higher than used in the treatment of rheumatoid arthritis and interactions with phytotherapeutic or homeopathic preparations. Using a standard information set for each DDI (eg, from product labeling, textbooks, and the medical literature), a group of rheumatologists and a group of clinical pharmacists independently assessed whether the individual drug-DMARD combinations interacted and whether they required immediate intervention. Both groups consisted of 3 members (2 men and 1 woman), aged 40 to 60 years, who had >5 years of clinical experience and were currently involved in clinical practice in large, nonacademic teaching hospitals in the Netherlands.

RESULTS

Forty potential DDIs with DMARDs were retrieved and assessed by the 2 groups. For 30 (75%) of these, rheumatologists and clinical pharmacists agreed about the requirement for immediate intervention. Specifically, 17 drug combinations (43%) were judged to interact and to require immediate intervention, and 13 combinations (33%) were judged either not to interact or to interact but not to require immediate intervention. For 10 combinations (25%), rheumatologists and clinical pharmacists were not in agreement. Overall, agreement between the groups was good (kappa = 0.80) for judging whether the drug combinations were interactions, and agreement was fair (kappa = 0.39) for judging whether immediate intervention was required. Prospective analysis of the data showed that rheumatologists tended to recommend immediate intervention more often when the adverse reaction to the DDI involved an increased risk of toxicity of the DMARD. In contrast, clinical pharmacists more often advocated immediate intervention when the adverse reaction involved decreased effectiveness of the DMARD.

CONCLUSION

For a subset of DMARD-drug combinations, rheumatologists and clinical pharmacists differed in their assessments of clinical relevance.

Computing with evidence Part I: A drug-mechanism evidence taxonomy oriented toward confidence assignment

We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKB's evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.

A clinical evaluation of the Janus Web Application, a software screening tool for drug-drug interactions

PURPOSE

To evaluate the clinical relevance of the Janus Web Application (JWA) in screening for potential drug-drug interactions (DDIs).

METHODS

One hundred and fifty patients taking two drugs or more were studied. Potential DDIs were identified by the JWA. Interviewing the patient and looking into his/her medical records provided complementing information. A clinical pharmacologist judged which potential DDIs were clinically relevant. Potentially relevant DDIs identified by the JWA were then correlated with clinically relevant DDIs.

RESULTS

A total of 150 significant potential DDIs were found. Sixteen percent (24/150) were judged to be clinically relevant.

CONCLUSIONS

A very small proportion of DDIs was considered clinically relevant in the specific clinical context. To optimise the software's user-friendliness, the following points need to be considered: the possibility of eliminating trivial potential DDIs, individualising drug alerts, and providing written information, accessible via a hyperlink.

Evaluation of frequently used drug interaction screening programs

OBJECTIVE

Drug-drug interaction (DDI) screening programs are an important tool to check prescriptions of multiple drugs. The objective of the current study was to critically appraise several DDI screening programs.

METHODS

A DDI screening program had to fulfil minimal requirements (information on effect, severity rating, clinical management, mechanism and literature) to be included into the final evaluation. The 100 most frequently used drugs in the State Hospital of Baden, Switzerland, were used to test the comprehensiveness of the programs. Qualitative criteria were used for the assessment of the DDI monographs. In a precision analysis, 30 drugs with and 30 drugs without DDIs of clinical importance were tested. In addition, 16 medical patient profiles were checked for DDIs, using Stockley's Drug Interactions as a reference.

MAIN OUTCOME MEASURE

Suitability of DDI screening program (quality of monographs, comprehensiveness of drug list, statistical evaluation).

RESULTS

Out of nine programs included, the following four fulfilled the above mentioned criteria: Drug Interaction Facts, Drug-Reax, Lexi-Interact and Pharmavista. Drug Interaction Facts contained the smallest number of drugs and was therefore the least qualified program. Lexi-Interact condenses many DDIs into one group, resulting in less specific information. Pharmavista and Drug-Reax offer excellent DDI monographs. In the precision analysis, Lexi-Interact showed the best sensitivity (1.00), followed by Drug-Reax and Pharmavista (0.83 each) and Drug Interaction Facts (0.63). The analysis of patient profiles revealed that out of 157 DDIs found by all programs, only 18 (11%) were detected by all of them. No program found more than 50% of the total number of DDIs. A further evaluation using Stockley's Drug interactions as the gold standard revealed that Pharmavista achieved a sensitivity of 0.86 (vs Drug Interaction Facts, Lexi-Interact and Drug-Reax with a sensitivity of 0.71 each) and a positive predictive value of 0.67.

CONCLUSION

None of the four DDI screening programs tested is ideal, every program has its strengths and weaknesses, which are important to know. Pharmavista offers the highest sensitivity of the programs evaluated with a specificity and positive predictive value in an acceptable range.