Adverse Drug Events Information Research Articles

A study on user willingness to participate in an adverse drug event information sharing system

BackgroundTo prevent the recurrence of Adverse Drug Events (ADEs), particularly drug allergies, it is essential to avoid re-exposure to causative drugs. Awareness of previous ADEs is crucial for patients because they can share accurate information with healthcare providers (HCPs). This study aims to assess users’ willingness to share ADE information and evaluate the factors related to this willingness by utilizing a prospective ADE information-sharing system currently under consideration in South Korea.MethodsIn September 2023, a self-administered questionnaire was collected from a sex-, age-, and regionally stratified nationwide convenience sample of adults recruited through a commercial panel in South Korea. Factors contributing to the willingness to share ADE information and create electronic ADE cards (e-ADE cards) were investigated using multivariate logistic regression analysis.ResultsAmong the 1,000 respondents, 458 (45.8%) were willing to share ADE information, and 521 (52.1%) were willing to create e-ADE cards. The willingness to share personal ADE information and create e-ADE cards was positively associated with the perceived benefits of sharing ADE, trust in HCPs and positive experiences. Notably, older adult patients demonstrated a higher willingness to share information and use e-ADE cards, with rates of 56% and 62%, respectively.ConclusionsOur findings indicate that the approach to sharing personal ADE information should be distinct from that of sharing comprehensive health information. Notably, users are likely to willingly disclose their personal information even if they are not anonymized, owing to the significant perceived benefits of sharing. The findings of this study can enhance awareness about sharing personal ADE information and contribute to the successful establishment of an ADE information-sharing system, thereby improving the patient safety environment.

Developing large language models to detect adverse drug events in posts on X

ABSTRACT Adverse drug events (ADEs) are one of the major causes of hospital admissions and are associated with increased morbidity and mortality. Post-marketing ADE identification is one of the most important phases of drug safety surveillance. Traditionally, data sources for post-marketing surveillance mainly come from spontaneous reporting system such as the Food and Drug Administration Adverse Event Reporting System (FAERS). Social media data such as posts on X (formerly Twitter) contain rich patient and medication information and could potentially accelerate drug surveillance research. However, ADE information in social media data is usually locked in the text, making it difficult to be employed by traditional statistical approaches. In recent years, large language models (LLMs) have shown promise in many natural language processing tasks. In this study, we developed several LLMs to perform ADE classification on X data. We fine-tuned various LLMs including BERT-base, Bio_ClinicalBERT, RoBERTa, and RoBERTa-large. We also experimented ChatGPT few-shot prompting and ChatGPT fine-tuned on the whole training data. We then evaluated the model performance based on sensitivity, specificity, negative predictive value, positive predictive value, accuracy, F1-measure, and area under the ROC curve. Our results showed that RoBERTa-large achieved the best F1-measure (0.8) among all models followed by ChatGPT fine-tuned model with F1-measure of 0.75. Our feature importance analysis based on 1200 random samples and RoBERTa-Large showed the most important features are as follows: “withdrawals”/”withdrawal”, “dry”, “dealing”, “mouth”, and “paralysis”. The good model performance and clinically relevant features show the potential of LLMs in augmenting ADE detection for post-marketing drug safety surveillance.

An external facilitation intervention to increase uptake of an adverse drug event reporting intervention.

Adverse drug events (ADEs) are a leading cause of emergency department visits and hospital admissions in Canada. ActionADE prevents repeat ADEs by enabling clinicians to document and communicate standardized ADE information across care settings. We used an external facilitation intervention to promote the uptake of ActionADE in four hospitals in British Columbia, Canada. This study examined whether, how and in what context external facilitation influenced the uptake of ActionADE. In this convergent-parallel mixed-methods study, an external facilitator used a four-step iterative process to support site champions using context-specific implementation strategies to increase the ADE reporting rate at their sites. We extracted archival data to assess implementation determinants before and after the implementation of the external facilitation and implementation strategies. We also retrieved data on the mean monthly counts of reported ADEs for each user from the ActionADE server. Zero-inflated Poisson models were used to examine changes in mean monthly counts of reported ADEs per user between pre-intervention (June 2021 to October 2021) and intervention (November 2021 to March 2022) periods. The external facilitator and site champions co-created three functions: (1) educate pharmacists about what and how to report in ActionADE, (2) educate pharmacists about the impact of ActionADE on patient outcomes, and (3) provide social support for pharmacists to integrate ADE reporting into clinical workflows. Site champions used eight forms to address the three functions. Peer support and reporting competition were the two common strategies used by all sites. Sites' responses to external facilitation varied. The rate of mean monthly counts of reported ADEs per user significantly increased during the intervention period compared to the pre-intervention period at LGH (RR: 3.74, 95% CI 2.78 to 5.01) and RH (RR: 1.43, 95% CI 1.23 to 1.94), but did not change at SPH (RR: 0.68, 95% CI: 0.43 to 1.09) and VGH (RR: 1.17, 95% CI 0.92 to 1.49). Leave of absence of the clinical pharmacist champion and failure to address all identified functions were implementation determinants that influenced the effectiveness of external facilitation. External facilitation effectively supported researchers and stakeholders to co-create context-specific implementation strategies. It increased ADE reporting at sites where clinical pharmacist champions were available, and where all functions were addressed.

Using ActionADE to create information continuity to reduce re-exposures to harmful medications: study protocol for a randomized controlled trial

BackgroundRepeat exposures to culprit medications are a common cause of preventable adverse drug events. Health information technologies have the potential to reduce repeat adverse drug events by improving information continuity. However, they rarely interoperate to ensure providers can view adverse drug events documented in other systems. We designed ActionADE to enable rapid documentation of adverse drug events and communication of standardized information across health sectors by integrating with legacy systems.We will leverage ActionADE’s implementation to conduct two parallel, randomized trials: patients with adverse drug reactions in the main trial and those diagnosed with non-adherence in a secondary trial. Primary objective of the main trial is to evaluate the effects of providing information continuity about adverse drug reactions on culprit medication re-dispensations over 12 months. Primary objective of the secondary trial is to evaluate the effect of providing information continuity on adherence over 12 months.MethodsWe will conduct two parallel group, triple-blind randomized controlled trials in participating hospitals in British Columbia, Canada. We will enroll adults presenting to hospital with an adverse drug event to prescribed outpatient medication. Clinicians will document the adverse drug event in ActionADE. The software will use an algorithm to determine patient eligibility and allocate eligible patients to experimental or control. In the experimental arm, ActionADE will transmit information to PharmaNet, where adverse drug event information will be displayed in community pharmacies when re-dispensations are attempted. In the control arm, ActionADE will retain information in the local record. We will enroll 3600 adults with an adverse drug reaction into the main trial. The main trial’s primary outcome is re-dispensation of a culprit or same-class medication within 12 months; the secondary trial’s primary outcome will be adherence to culprit medication. Secondary outcomes include health services utilization and mortality.DiscussionThese studies have the potential to guide policy decisions and investments needed to drive health information technology integrations to prevent repeat adverse drug events. We present an example of how a health information technology implementation can be leveraged to conduct pragmatic randomized controlled trials.Trial registrationClinicalTrials.gov NCT04568668, NCT04574648. Registered on 1 October 2020.

Patient Perspectives on Health Data Privacy and Implications for Adverse Drug Event Documentation and Communication: Qualitative Study.

BackgroundAdverse drug events are unintended and harmful effects of medication use. Using existing information and communication technologies (ICTs) to increase information sharing about adverse drug events may improve patient care but can introduce concerns about data privacy.ObjectiveThis study aims to examine the views of patients and their caregivers about data protection when using ICTs to communicate adverse drug event information to improve patient safety.MethodsWe conducted an exploratory qualitative study. A total of 4 focus groups were held among patients who had experienced or were at risk of experiencing an adverse drug event, their family members, and their caregivers. We recruited participants through multiple avenues and iteratively analyzed the data using situational analysis.ResultsOf the 47 participants recruited, 28 attended our focus groups. We identified 3 primary themes. First, participants felt that improved information sharing about adverse drug events within their circle of care would likely improve care. Second, participants were concerned about data handling and inappropriate access but believed that the benefits of information sharing outweighed the risks of privacy breaches. Finally, participants were more concerned about data privacy in the context of stigmatized health conditions.ConclusionsCurrent conditions for maintaining health data privacy are consistent with participants’ preferences, despite the fact that health data are susceptible to breaches and mismanagement. Information sharing that increases patient safety may justify potential privacy risks. Greater attention to patient concerns and the effect of social and contextual concerns in the design and implementation of health information technologies may increase patient confidence in the privacy of their information.

Ensemble method–based extraction of medication and related information from clinical texts

Accurate and complete information about medications and related information is crucial for effective clinical decision support and precise health care. Recognition and reduction of adverse drug events is also central to effective patient care. The goal of this research is the development of a natural language processing (NLP) system to automatically extract medication and adverse drug event information from electronic health records. This effort was part of the 2018 n2c2 shared task on adverse drug events and medication extraction. The new NLP system implements a stacked generalization based on a search-based structured prediction algorithm for concept extraction. We trained 4 sequential classifiers using a variety of structured learning algorithms. To enhance accuracy, we created a stacked ensemble consisting of these concept extraction models trained on the shared task training data. We implemented a support vector machine model to identify related concepts. Experiments with the official test set showed that our stacked ensemble achieved an F1 score of 92.66%. The relation extraction model with given concepts reached a 93.59% F1 score. Our end-to-end system yielded overall micro-averaged recall, precision, and F1 score of 92.52%, 81.88% and 86.88%, respectively. Our NLP system for adverse drug events and medication extraction ranked within the top 5 of teams participating in the challenge. This study demonstrated that a stacked ensemble with a search-based structured prediction algorithm achieved good performance by effectively integrating the output of individual classifiers and could provide a valid solution for other clinical concept extraction tasks.

Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0).

This work describes the Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 challenge for extracting medication, indication, and adverse drug events (ADEs) from electronic health record (EHR) notes. The goal of MADE is to provide a set of common evaluation tasks to assess the state of the art for natural language processing (NLP) systems applied to EHRs supporting drug safety surveillance and pharmacovigilance. We also provide benchmarks on the MADE dataset using the system submissions received in the MADE 2018 challenge. The MADE 1.0 challenge has released an expert-annotated cohort of medication and ADE information comprising 1089 fully de-identified longitudinal EHR notes from 21 randomly selected patients with cancer at the University of Massachusetts Memorial Hospital. Using this cohort as a benchmark, the MADE 1.0 challenge designed three shared NLP tasks. The named entity recognition (NER) task identifies medications and their attributes (dosage, route, duration, and frequency), indications, ADEs, and severity. The relation identification (RI) task identifies relations between the named entities: medication-indication, medication-ADE, and attribute relations. The third shared task (NER-RI) evaluates NLP models that perform the NER and RI tasks jointly. In total, 11 teams from four countries participated in at least one of the three shared tasks, and 41 system submissions were received in total. The best systems F1 scores for NER, RI, and NER-RI were 0.82, 0.86, and 0.61, respectively. Ensemble classifiers using the team submissions improved the performance further, with an F1 score of 0.85, 0.87, and 0.66 for the three tasks, respectively. MADE results show that recent progress in NLP has led to remarkable improvements in NER and RI tasks for the clinical domain. However, some room for improvement remains, particularly in the NER-RI task.

Extracting Adverse Drug Event Information with Minimal Engineering.

In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.

BackgroundMedication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data.ObjectiveTo unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations.MethodsWe have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types.ResultsOur results show that the SVM model achieved the best average F1-score of 89.1% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72%) as well as the rule induction baseline system (F1-score of 7.47%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35%.ConclusionsIt shows that classical learning models (SVM) remains advantageous over deep learning models (RNN variants) for clinical relation identification, especially for long-distance intersentential relations. However, RNNs demonstrate a great potential of significant improvement if more training data become available. Our work is an important step toward mining EHRs to improve the efficacy of drug safety surveillance. Most importantly, the annotated data used in this study will be made publicly available, which will further promote drug safety research in the community.

The Marginal Costs of Adverse Drug Events Associated With Exposures to Anticoagulants and Hypoglycemic Agents During Hospitalization.

Anticoagulants and hypoglycemic agents are 2 of the most challenging drug classes for medical management in the hospital resulting in many adverse drug events (ADEs). Estimating the marginal cost (MC) of ADEs associated with anticoagulants and hypoglycemic agents for adults in 5 patient groups during their hospital stay and the total annual ADE costs for all patients exposed to these drugs during their stay. Data are from 2010 to 2013 Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases and Medicare Patient Safety Monitoring System (MPSMS). Deidentified patients were linked using probabilistic matching in the same hospital and year for 5 patient groups. ADE information was obtained from the MPSMS using retrospective structured record review. Costs were derived using HCUP cost-to-charge ratios. MC estimates were made using Extended Estimating Equations controlling for patient characteristics, comorbidities, hospital procedures, and hospital characteristics. MC estimates were applied to the 2013 HCUP National Inpatient Sample to estimate annual ADE costs. Adjusted MC estimates were smaller than unadjusted measures with most groups showing estimates that were at least 50% less. Adjusted anticoagulant ADE costs added >45% and Hypoglycemic ADE costs added >20% to inpatient costs. The 2013 hospital cost estimates for ADEs associated with anticoagulants and hypoglycemic agents were >$2.5 billion for each drug class. This study demonstrates the importance of accounting for confounders in the estimation of ADEs, and the importance of separate estimates of ADE costs by drug class.

Patient experiences with informational discontinuity of care: Bridging the gaps

Informational continuity of care is a central component of effective health care delivery and patient management but is often not achieved, leaving patients to compensate for or suffer from the burden of informational discontinuity of care. Fragmentation and siloed care delivery and communication within and across care settings present significant challenges to informational continuity of care. This is especially the case in the context of adverse drug events (ADEs), the harmful and unanticipated outcomes of medication use. This paper introduces a project called ActionADE that is intended to bridge informational gaps related to ADEs by enabling the documentation and communication of patient-specific ADE information. It contributes to literature concerned with information sharing in healthcare through a focus on patient views about sharing medication information through the design of an ADE reporting system, and explores implications for system design within a sociotechnical framework.

Adverse Events in Pediatric Patients Receiving Long-term Oral and Intravenous Antibiotics.

Children receiving long-term antibiotic therapy (LTAT) at Children's Hospital Colorado (CHCO) are treated with both oral and intravenous (i.v.) agents and often experience complications not comprehensively described by the literature. We sought to describe adverse drug events (ADEs) and venous access complications (VACs) in pediatric patients managed with oral and i.v. antibiotics so as to inform clinical decision-making, drug monitoring, and patient counseling at CHCO. We conducted a retrospective review of children receiving LTAT through the CHCO infectious disease service from 2006 to 2012. Demographic, microbiologic, diagnostic data, ADEs, and VACs were recorded for each patient. From 2006 to 2012, 521 patients received 1876 courses, accounting for 71,306 days of antimicrobial therapy. A total of 219 patients (42%) developed an ADE with discontinuation of the offending agent in 65% of courses associated with an ADE. The most common ADEs were neutropenia, rash, and diarrhea. Central lines were placed in 376 patients with 106 (28%) experiencing ≥1 VACs. I.v. agents were associated with a fourfold increase in the rate of ADEs compared with oral agents, and a fivefold increase when VACs were included. Practitioners may make more informed decisions and risk assessments by using descriptive ADE information for specific agents and mode of drug delivery to mitigate risk, thereby improving the quality of care. Patients should be counseled regarding risks of LTAT, including increased risk with i.v. therapy, and actively monitored for side effects.

P026: Pilot-testing an adverse drug event documentation form prior to its implementation in an electronic health record

Introduction: Adverse drug events (ADEs), harmful and unintended consequences of medications, account for 1.7M emergency department (ED) visits in Canada each year. Up to 30% are due to unintentional re-prescribing of culprit drugs, partly due to lack of accessible, succinct, and comprehensible ADE information at the time of prescribing. Through a systematic review and workshops with physicians and pharmacists, we designed new ADE documentation fields. Our objective was to pilot-test the fields to anticipate and address problems prior to their integration into an electronic medical record (EMR). Methods: We seek to introduce structured ADE documentation into an EMR and PharmaNet, BC’s medication-dispensing database, to generate patient-level alerts when attempts to re-prescribe culprit drugs are made. We conducted this qualitative study in the EDs and on the wards of two BC hospitals. The ADE fields collect information about the culprit drug, its effect on the patient, treatment and outcome. We recruited a convenience sample of pharmacists, and distributed paper forms with the ADE fields to them before data collection shifts. We recorded how pharmacists evaluated patients for ADEs and completed the forms. We collected completed forms, and conducted semi-structured interviews for feedback. We analyzed data for common themes using inductive reasoning and constant comparison methods. Results: We observed 6 pharmacists documenting 24 ADEs. The field design was perceived as simple, clear, with sufficient detail to capture ADE information. Users identified fields to be omitted (e.g., excess details of culprit drug), modified (e.g., reporting options), or needing clarification (e.g., treatment details). Users were uncertain about what to report when the differential diagnosis included an ADE, but diagnostic uncertainty remained. Thus, ADE fields should enable communication about suspected events and potential alternative diagnoses. Pharmacists required follow-up in some cases to complete their determination (e.g., C. difficile toxin assay), emphasizing the need to be able to modify an ADE report. Conclusion: Paper-based pilot testing uncovered barriers to ADE documentation, and allowed us to plan for modifications and required linkages between electronic systems. In order to be functional, electronic ADE documentation must be dynamic, representing a departure from previous reporting platforms.

Identifying adverse drug event information in clinical notes with distributional semantic representations of context

For the purpose of post-marketing drug safety surveillance, which has traditionally relied on the voluntary reporting of individual cases of adverse drug events (ADEs), other sources of information are now being explored, including electronic health records (EHRs), which give us access to enormous amounts of longitudinal observations of the treatment of patients and their drug use. Adverse drug events, which can be encoded in EHRs with certain diagnosis codes, are, however, heavily underreported. It is therefore important to develop capabilities to process, by means of computational methods, the more unstructured EHR data in the form of clinical notes, where clinicians may describe and reason around suspected ADEs. In this study, we report on the creation of an annotated corpus of Swedish health records for the purpose of learning to identify information pertaining to ADEs present in clinical notes. To this end, three key tasks are tackled: recognizing relevant named entities (disorders, symptoms, drugs), labeling attributes of the recognized entities (negation, speculation, temporality), and relationships between them (indication, adverse drug event). For each of the three tasks, leveraging models of distributional semantics – i.e., unsupervised methods that exploit co-occurrence information to model, typically in vector space, the meaning of words – and, in particular, combinations of such models, is shown to improve the predictive performance. The ability to make use of such unsupervised methods is critical when faced with large amounts of sparse and high-dimensional data, especially in domains where annotated resources are scarce.

Similarity-based modeling applied to signal detection in pharmacovigilance.

One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.

Evaluation of Adverse Drug Event Information in US Manufacturer Labels

Pharmaceutical manufacturer labels are an important source of adverse drug event (ADE) information. The study objective was to determine the sufficiency of ADE reporting in US drug labels. A sample of 50 labels was evaluated from the top 200 drugs dispensed in the US. Electronic copies of labels were obtained and reviewed by 2 pharmacists for ADE incidence and discontinuation data. ADE incidence data were provided in 86% of labels. However, discontinuation rates due to ADEs and ADE incidence by dose were only reported in 60%. ADE incidence reporting by age (46%) or gender (18%) was also low. ADEs that occurred in less than 2% of the population were rarely reported. Incidence rates were based on small populations (median of 794) and short term studies (median of 84 days for chronic conditions). Labels for 19 drugs used chronically had no long term study data. Methods for collecting ADE data were stated in only 12% of labels. Adverse drug event and drug discontinuation data is under-reported in US labels. More information on adverse events causing discontinuation (especially serious events) and those related to dose, age, and gender is needed in labels to ensure safe prescribing and dispensing of drugs.

Understanding pharmacist decision making for adverse drug event (ADE) detection

Manual chart review is an effective but expensive method for adverse drug event (ADE) detection. Building an expert system capable of mimicking the human expert's decision pathway, to deduce the occurrence of an ADE, can improve efficiency and lower cost. As a first step to build such an expert system, this study explores pharmacist's decision-making processes for ADE detection. Think-aloud procedures were used to elicit verbalizations as pharmacists read through ADE case scenarios. Two types of information were extracted, firstly pharmacists' decision-making strategies regarding ADEs and secondly information regarding pharmacists' unmet information needs for ADE detection. Verbal protocols were recorded and analysed qualitatively to extract ADE information signals. Inter-reviewer agreement for classification of ADE information signals was calculated using Cohen's kappa. We extracted a total of 110 information signals, of which 73% consisted of information that was interpreted by the pharmacists from the case scenario and only about half (53%, n = 32) of the information signals were considered relevant for the detection of the ADEs. Excellent reliability was demonstrated between the reviewers for classifying signals. Fifty information signals regarding unmet information needs were extracted and grouped into themes based on the type of missing information. Pharmacists used a forward reasoning approach to make implicit deductions and validate hypotheses about possible ADEs. Verbal protocols also indicated that pharmacists' unmet information needs occurred frequently. Developing alerting systems that meet pharmacists' needs adequately will enhance their ability to reduce preventable ADEs, thus improving patient safety.

Adverse-drug-event rates for high-cost and high-use drugs in the intensive care unit

The rates of adverse drug events (ADEs) associated with high-cost and high-use drugs in the intensive care unit (ICU) were studied. This retrospective analysis was conducted from October 1997 through June 2001 in a 647-bed academic medical center with over 120 ICU beds. Adult patients with a documented ADE occurring in the ICU were included in the analysis. ADE information, including suspected medication, causality, preventability, and severity, was extracted from the institutional ADE database. Published definitions of ADEs and published scales for causality and severity assessments were used. High-cost medications were those in the top 50% of cumulative ICU medication costs, and high-use medications accounted for the upper 50% of all medications used in the ICU. Between-group comparisons of ADE rates, preventability, and severity associated with high-cost and high-use medications were conducted. Of the 17 medications that were considered high cost, 9 (53%) were associated with ADEs. Of the 15 medications that met the criteria for high-use drugs, 12 (80%) were associated with ADEs. The rates of ADEs associated with high-cost and high-use drugs did not significantly differ (43% versus 75%, respectively; p = 0.098). ADEs associated with high-cost and high-use medications were categorized as mild (15% versus 10%, respectively), moderate (52% versus 50%, respectively), and severe (33% versus 40%, respectively) (p > 0.05). The frequency, severity, and preventability of ADEs in the ICU were not associated with a drug's cost or frequency of use. Monitoring priorities of the critical care pharmacist should not be dictated by cost alone but should include frequency of use and the potential for causing an ADE.

Quantifying adverse drug events : are systematic reviews the answer?

Quantifying adverse drug events (ADEs) is critical to clinicians, consumers and policy makers. Most ADE information comes from large clinical trials. Systematic reviews have become a popular tool in quantifying the efficacy of different therapeutic interventions and ADE data collected in randomised trials may be helpful in quantifying the risk associated with a specific pharmacological agent. However, clinicians who are interested in conducting systematic reviews of ADEs may face many challenges. These challenges are geared towards two main areas: poor quality of ADE reporting in randomised trials and poor indexing of ADEs in medical databases. In this review, we will discuss these challenges in detail using some examples from the literature. Where possible, we also discuss strategies that may overcome these problems. More rigourous standards of reporting ADEs in randomised trials, as well as better indexing of ADE terminology in medical databases, could one day make systematic reviews of ADEs a powerful tool for practising clinicians.

A National Survey of Hospital Adverse Drug Event Monitoring and Reporting Systems

The purpose of this research was to collect information concerning the systems which hospitals have developed to monitor and report adverse drug events (ADE). In addition, the attitudes and perceptions of those coordinating the program toward postmarketing surveillance of such events were investigated. The sample was stratified by hospital size. Questionnaires were sent to 725 Joint Commission for Accreditation of Health Care Organization accredited hospitals (305 small, 275 medium, and 145 large). Completed questionnaires were received from 306 respondents, representing a 42% overall response rate. Differences among the three groups were seen in the professional personnel involvement in the monitoring activities, reporters of ADEs, and dissemination of ADE information. Pharmacists had the biggest role in implementing this function and concurrent review methods generated the greatest percentage of ADEs. Overall, the systems were similar among hospitals of different sizes.