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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)., Competing Interests: None declared., (Thieme. All rights reserved.)

Objective To conduct research to show the value of text mining for automatically identifying suspected bleeding adverse drug events (ADEs) in the emergency department (ED). Methods A corpus of ED admission notes was manually annotated for bleeding ADEs. The notes were taken for patients ≥ 65 years of age who had an ICD-9 code for bleeding, the presence of hemoglobin value ≤ 8 g/dL, or were transfused > 2 units of packed red blood cells. This training corpus was used to develop bleeding ADE algorithms using Random Forest and Classification and Regression Tree (CART). A completely separate set of notes was annotated and used to test the classification performance of the final models using the area under the ROC curve (AUROC). Results The best performing CART resulted in an AUROC on the training set of 0.882. The model's AUROC on the test set was 0.827. At a sensitivity of 0.679, the model had a specificity of 0.908 and a positive predictive value (PPV) of 0.814. It had a relatively simple and intuitive structure consisting of 13 decision nodes and 14 leaf nodes. Decision path probabilities ranged from 0.041 to 1.0. The AUROC for the best performing Random Forest method on the training set was 0.917. On the test set, the model's AUROC was 0.859. At a sensitivity of 0.274, the model had a specificity of 0.986 and a PPV of 0.92. Conclusion Both models accurately identify bleeding ADEs using the presence or absence of certain clinical concepts in ED admission notes for older adult patients. The CART model is particularly noteworthy because it does not require significant technical overhead to implement. Future work should seek to replicate the results on a larger test set pulled from another institution., Competing Interests: Conflict of Interest: None.

Objective: We conducted a survey of nursing home physicians to learn about (1) the laboratory value thresholds that clinical event monitors should use to generate alerts about potential adverse drug events (ADEs); (2) the specific information to be included in the alerts; and (3) the communication modality that should be used for communicating them., Methods: Nursing home physician attendees of the 2010 Conference of AMDA: The Society for Post-Acute and Long-Term Care Medicine., Results: A total of 800 surveys were distributed; 565 completed surveys were returned and seven surveys were excluded due to inability to verify that the respondents were physicians (a 70% net valid response rate). Alerting threshold preferences were identified for eight laboratory tests. For example, the majority of respondents selected thresholds of ≥5.5 mEq/L for hyperkalemia (63%) and ≤3.5 without symptoms for hypokalemia (54%). The majority of surveyed physicians thought alerts should include the complete active medication list, current vital signs, previous value of the triggering lab, medication change in the past 30 days, and medication allergies. Most surveyed physicians felt the best way to communicate an ADE alert was by direct phone/voice communication (64%), followed by email to a mobile device (59%)., Conclusions: This survey of nursing home physicians suggests that the majority prefer alerting thresholds that would generally lead to fewer alerts than if widely accepted standardized laboratory ranges were used. It also suggests a subset of information items to include in alerts, and the physicians' preferred communication modalities. This information might improve the acceptance of clinical event monitoring systems to detect ADEs in the nursing home setting.