Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support.

OBJECTIVE: Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts. METHODS: We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework. RESULTS: Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts. CONCLUSION: Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

Accuracy of adverse-drug-event reports collected using an automated dispensing system.

PURPOSE: The accuracy of adverse-drug-event (ADE) reports collected using an automated dispensing system was evaluated. METHODS: ADE reports were collected by requiring nurses on five units in a tertiary care facility to select a reason for removing two tracer drugs (dextrose injection 50% [D50] and naloxone) from an automated dispensing system (Medstation 2000, Pyxis, San Diego, CA). The accuracy of the ADE reports during a period of 4.5 months was evaluated through retrospective chart review. The sensitivity, specificity, positive predictive value, and negative predictive value of the reports were calculated. RESULTS: A review of 61 D50 transactions found that the appropriate reason for removal was selected by nursing staff 62% of the time. Twenty-seven transactions were recorded as occurring due to an ADE, and 70% of these were confirmed in the medical record. The sensitivity and specificity of the ADE reports for D50 were 55.9% (95% confidence interval [CI], 39.2-72.6%) and 70.4% (95% CI, 53.2-87.6%), respectively. A review of 32 naloxone transactions found that nurses correctly selected the reason for removal 88% of the time. Twenty-three transactions were recorded as occurring due to an ADE, and 87% of these were confirmed in the medical record. The sensitivity and specificity of the ADE reports for naloxone were 95.2% (95% CI, 86.1-104.4%) and 72.7% (95% CI, 46.4-99.1%), respectively. CONCLUSION: A Pyxis ADE reporting mechanism using the tracer drugs D50 and naloxone increased the overall reporting of ADEs.