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Updated: May 26, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Improving adherence to research protocol drug exclusions using a clinical alerting system.
James J Cimino1, Lincoln Farnum, Gary E DiPatrizio
1Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD, USA.
Electronic health record (EHR) systems can now warn prescribers about drug conflicts with research protocols. This method helps prevent subject harm and ensures research integrity by identifying excluded drugs.
Area of Science:
- Clinical Informatics
- Biomedical Research
- Pharmacovigilance
Background:
- Clinical research protocols frequently exclude specific drugs to ensure subject safety and data integrity.
- Nonadherence to these drug exclusion criteria can lead to adverse events, compromised study results, and subject disqualification.
- Existing electronic health record (EHR) systems lack robust mechanisms to automatically flag potential drug-protocol conflicts.
Purpose of the Study:
- To develop a generalizable method for leveraging EHR alerting functions to prevent drug order conflicts with research protocols.
- To enhance patient safety and research integrity by proactively identifying and warning prescribers about excluded medications.
Main Methods:
- Analyzed a sample of National Institutes of Health (NIH) clinical research protocols to determine the prevalence of drug exclusions.
- Developed a data model to represent drug exclusions and exemptions, integrating it with the NIH's Biomedical Translational Research Information System (BTRIS) terminology.
- Created a medical logic module (MLM) for EHR systems to match ordered drug terms against protocol-defined drug concepts.
Main Results:
- Fifty percent of sampled protocols contained specific drug exclusions.
- The developed data model effectively represented exclusion and exemption concepts.
- The MLM successfully detected orders for excluded drugs in a test environment, providing informative alerts to users regarding exclusion context and applicability.
Conclusions:
- Drug exclusions are common in research protocols, necessitating automated detection to prevent violations.
- The proposed approach, utilizing a medical logic module (MLM), knowledge base, and controlled terminology, effectively detects and prevents potential protocol violations.
- Further refinement of the MLM is recommended to incorporate additional exclusion parameters like timing and comorbidities for enhanced accuracy.
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