AI-Based Automation for Medication Reconciliation: Scoping Review
Clinical Snapshot
PICO Framework
| P — Population | Healthcare systems and clinical settings implementing medication reconciliation processes |
| I — Intervention | Artificial intelligence-based automation tools for medication reconciliation tasks |
| C — Comparator | Traditional manual medication reconciliation processes or rule-based systems |
| O — Outcomes | Extent of automation achieved across medication reconciliation tasks (best possible medication history creation, discrepancy identification, discrepancy resolution) |
Bottom Line
This comprehensive scoping review reveals that while 94 studies have explored AI applications for medication reconciliation, current development remains largely at the proof-of-concept stage. Most research focuses on extracting medication information from clinical notes or images, with virtually no progress toward automating the critical tasks of identifying and resolving medication discrepancies. The predominant use of benchmark datasets rather than real-world implementation studies highlights a significant research-practice gap. For Australian clinicians, these findings suggest that AI-based medication reconciliation tools are not yet ready for routine clinical deployment. Healthcare systems should continue with established manual processes while monitoring this rapidly evolving field. Future research priorities should include real-world validation studies, usability testing, and addressing infrastructural barriers such as incomplete medication data sources. The review provides valuable guidance for prioritising development efforts toward the more complex but clinically critical tasks of discrepancy identification and resolution.
Key Findings
P Value: Not applicable for scoping review
Effect Size: 97.9% of studies automated only information acquisition stage
Primary Outcome: Extent of automation achieved across medication reconciliation tasks
Nnt Or Sensitivity: Only 2.1% of studies addressed discrepancy identification; 0% addressed discrepancy resolution
Confidence Interval: Not applicable for scoping review
Clinical Application
Current evidence suggests limited readiness for clinical implementation, with most tools remaining at proof-of-concept stage Relevant to Australian hospitals implementing National Safety and Quality Health Service Standards for medication reconciliation, though TGA approval pathways for AI-based tools remain unclear Healthcare systems implementing electronic health records with medication reconciliation requirements
Abstract
BACKGROUND: Medication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved. OBJECTIVE: This scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved. METHODS: We searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model. RESULTS: A total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies. CONCLUSIONS: This is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts.
References
- 1.Bortesi, T., Becerra, J. P., Ranisau, M. P., Wen, J., Nadesan, B., Devereaux, P. J., McGillion, M., & Petch, J. (2026). AI-Based Automation for Medication Reconciliation: Scoping Review. Journal of Medical Internet Research. https://doi.org/10.2196/86760
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