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Updated: May 13, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

AI-Based Automation for Medication Reconciliation: Scoping Review.

Juan Pablo Tabja Bortesi1,2, Maria P Becerra1, Jonathan Ranisau1

  • 1Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.

Journal of Medical Internet Research
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for automating medication reconciliation (MedRec) tasks, primarily focusing on medication history extraction. Future research should address discrepancy resolution and real-world implementation to enhance patient safety.

Keywords:
AIartificial intelligencecontinuity of carehealth ITmachine learningmedication reconciliationpatient safety

Related Experiment Videos

Last Updated: May 13, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Patient Safety

Background:

  • Medication reconciliation (MedRec) is crucial for patient safety, ensuring accurate medication information across care transitions.
  • MedRec involves creating medication histories, identifying discrepancies, and resolving them.
  • Artificial intelligence (AI) offers potential to improve MedRec efficiency and accuracy.

Purpose of the Study:

  • To systematically review and map the application of AI in MedRec tasks and subtasks.
  • To assess the level of automation achieved by AI in MedRec processes.
  • To identify research gaps and future directions for AI in MedRec.

Main Methods:

  • A comprehensive scoping review was conducted, searching major databases (MEDLINE, Embase, Web of Science, IEEE Xplore, Compendex).
  • Studies were screened for AI application in MedRec tasks, excluding rule-based systems.
  • A 4-stage human information processing model guided the assessment of automation levels.

Main Results:

  • 94 studies met inclusion criteria, all addressing medication history creation.
  • Only 2.1% of studies addressed discrepancy identification, with limited automation beyond information acquisition.
  • Most studies utilized electronic health record text data and machine learning models, with a focus on model development using public datasets.

Conclusions:

  • Current AI applications in MedRec are preliminary, primarily focused on information extraction.
  • Significant gaps exist in automating discrepancy identification and resolution.
  • Future efforts should address data challenges, implement AI models in practice, and evaluate real-world usability.