Barriers to Adoption of Tailored Drug–Drug Interaction Clinical Decision Support

Tianyi Zhang; Sheila M. Gephart; Vignesh Subbian; Richard D. Boyce; Lorenzo Villa-Zapata; Malinda S. Tan; John Horn; Ainhoa Gomez-Lumbreras; Andrew V. Romero; Daniel C. Malone

doi:10.1055/s-0043-1772686

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2023; 14(04): 779-788
DOI: 10.1055/s-0043-1772686

Research Article

Barriers to Adoption of Tailored Drug–Drug Interaction Clinical Decision Support

Authors

Tianyi Zhang

¹Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, Arizona
Sheila M. Gephart

²Advanced Nursing Practice and Science Division, College of Nursing, University of Arizona, Tucson, Arizona
Vignesh Subbian

¹Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, Arizona
Richard D. Boyce

³Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
Lorenzo Villa-Zapata

⁴Clinical and Administrative Pharmacy, College of Pharmacy, University of Georgia, Athens, Georgia
Malinda S. Tan

⁵Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, Utah
John Horn

⁶Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, Washington
Ainhoa Gomez-Lumbreras

⁵Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, Utah
Andrew V. Romero

⁷Department of Pharmacy, Tucson Medical Center, Tucson, Arizona
Daniel C. Malone

⁵Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, Utah

Abstract

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).

Keywords

drug–drug interaction - clinical decision support systems - electronic health records - implementation science

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