Lost in Translation: No Effect of a High‐Profile Publication on the Concomitant Use of Interacting Drugs

Abstract We sought to assess whether a high‐profile publication that demonstrated serious clinical consequences of specific drug‐drug interactions (DDIs) reduced the concomitant use of those drugs. We conducted a quasi‐experimental study using 2000–2008 prescription claims from a commercial health insurer to examine trends in the dispensing of the interacting drug pairs (angiotensin‐converting enzyme inhibitors[ACEI] + potassium‐sparing diuretic, digoxin + clarithromycin, and glyburide + cotrimoxazole) and control drug pairs previously reported in a top‐tier general medicine journal. We examined prepublication and postpublication dispensing trends using Poisson regression. ACEI + potassium‐sparing diuretic use did not differ postpublication vs. prepublication (P = 0.11). Digoxin + clarithromycin use decreased minimally postpublication vs. prepublication (relative rate = 0.9996: 95% confidence interval [CI] = 0.9993–0.9998). Glyburide + cotrimoxazole use increased minimally postpublication vs. prepublication (relative rate = 1.0220; 95% CI = 1.0187–1.0254). Therefore, the high‐profile DDI publication had minimal to no measurable effect in reducing the concomitant use of the interacting drugs studied. We believe that better strategies are needed to translate knowledge about DDIs into clinical practice

Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records

Background: High override rates for drug-drug interaction (DDI) alerts in electronic health records (EHRs) result in the potentially dangerous consequence of providers ignoring clinically significant alerts. Lack of uniformity of criteria for determining the severity or validity of these interactions often results in discrepancies in how these are evaluated. The purpose of this study was to identify a set of criteria for assessing DDIs that should be used for the generation of clinical decision support (CDS) alerts in EHRs. Methods: We conducted a 20-year systematic literature review of MEDLINE and EMBASE to identify characteristics of high-priority DDIs. These criteria were validated by an expert panel consisting of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Results: Forty-four articles met the inclusion criteria for assessing characteristics of high-priority DDIs. The panel considered five criteria to be most important when assessing an interaction- Severity, Probability, Clinical Implications of the interaction, Patient characteristics, and the Evidence supporting the interaction. In addition, the panel identified barriers and considerations for being able to utilize these criteria in medication knowledge bases used by EHRs. Conclusions: A multi-dimensional approach is needed to understanding the importance of an interaction for inclusion in medication knowledge bases for the purpose of CDS alerting. The criteria identified in this study can serve as a first step towards a uniform approach in assessing which interactions are critical and warrant interruption of a provider’s workflow

Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems—I-MeDeSA

Background Medication-related decision support can reduce the frequency of preventable adverse drug events. However, the design of current medication alerts often results in alert fatigue and high over-ride rates, thus reducing any potential benefits. Methods The authors previously reviewed human-factors principles for relevance to medication-related decision support alerts. In this study, instrument items were developed for assessing the appropriate implementation of these human-factors principles in drug-drug interaction (DDI) alerts. User feedback regarding nine electronic medical records was considered during the development process. Content validity, construct validity through correlation analysis, and inter-rater reliability were assessed. Results The final version of the instrument included 26 items associated with nine human-factors principles. Content validation on three systems resulted in the addition of one principle (Corrective Actions) to the instrument and the elimination of eight items. Additionally, the wording of eight items was altered. Correlation analysis suggests a direct relationship between system age and performance of DDI alerts (p=0.0016). Inter-rater reliability indicated substantial agreement between raters (κ=0.764). Conclusion The authors developed and gathered preliminary evidence for the validity of an instrument that measures the appropriate use of human-factors principles in the design and display of DDI alerts. Designers of DDI alerts may use the instrument to improve usability and increase user acceptance of medication alerts, and organizations selecting an electronic medical record may find the instrument helpful in meeting their clinicians' usability need

Recommendations for Providers on Person-Centered Approaches to Assess and Improve Medication Adherence

Medication non-adherence is a significant clinical challenge that adversely affects psychosocial factors, costs, and outcomes that are shared by patients, family members, providers, healthcare systems, payers, and society. Patient-centered care (i.e., involving patients and their families in planning their health care) is increasingly emphasized as a promising approach for improving medication adherence, but clinician education around what this might look like in a busy primary care environment is lacking. We use a case study to demonstrate key skills such as motivational interviewing, counseling, and shared decision-making for clinicians interested in providing patient-centered care in efforts to improve medication adherence. Such patient-centered approaches hold considerable promise for addressing the high rates of non-adherence to medications for chronic conditions

Patient-centered interventions to improve medication management and adherence: A qualitative review of research findings

Patient-centered approaches to improving medication adherence hold promise, but evidence of their effectiveness is unclear. This review reports the current state of scientific research around interventions to improve medication management through four patient-centered domains: shared decision-making, methods to enhance effective prescribing, systems for eliciting and acting on patient feedback about medication use and treatment goals, and medication-taking behavior

PhD

dissertationManual chart review is used as the gold standard in many adverse drug event (ADE) detection studies. Owing to large resource utilization and expense this method is generally reserved for research studies. Building an expert system capable of mimicking the human expert's decision pathway would increase the efficiency of ADE detection. The first step in building such an expert system was to identify the expert for the task of detecting ADEs in manual chart-review. A systematic review and meta-analysis of studies using chart review as the method of detection of ADEs, was conducted. Results showed that pharmacists were capable of detecting higher incidence rates than other clinical specialties. The next step was to evaluate the decision-making processes used by the pharmacists for AIDE detection. Think-aloud analysis was used to identify signals pharmacists looked for while using the method of chart-review. Verbal protocol analysis also gave an insight into the gaps that exist between pharmacists' information needs and existing clinical information systems. The textual signals extracted using think-aloud analyses were limited in their scope because they represented only the case-scenarios that were presented in the focus groups. In order to make these signals generalizable, the use of the method of propositional analysis to evaluate the semantic structure of the think-aloud protocols, was proposed. A proposition for the detection of ADEs in the clinical notes consists of two types of information, first, the concepts representing the `adverse event' and those representing the drugs'. A second type of information needed would be the relationship expressed between the drug and the adverse event. A comparison of text-based techniques for identifying the first type of information represented in the proposition was conducted. This study evaluated the feasibility of using propositional analysis for identification of ADEs in clinical notes. This study used a combination of methodologies from the domains of cognitive science and artificial intelligence for detecting ADEs in clinical notes. Future work will focus on two specific directions. First, the automated extraction of propositions for ADE detection. Second, the development of rules that combine textual signals with medicatio