ArticleRisk management and healthcare policy2026
The Role of Artificial Intelligence in Reducing Dispensing Errors for Patient Safety and Quality: A Systems Approach.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Minimum Procedural Volume Thresholds for Surgical Privileging: A Mixed-Methods Validation and Risk Management Framework in a Multi-Specialty Healthcare Network.Risk management and healthcare policy · 2026Article
- Artificial Intelligence and Enhanced Recovery After Surgery as Patient Safety Strategies for Perioperative Care in Resource-Limited Settings.Risk management and healthcare policy · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Dispensing errors, often driven by look-alike/sound-alike medicine names, similar packaging, and complex workflows, pose a persistent threat to patient safety and care quality. Artificial intelligence (AI) offers new opportunities to detect discrepancies and support decision-making in near real time, yet its impact depends on how it is embedded within the wider healthcare system. In this perspective, we use a systems approach to synthesize current AI-enabled strategies for reducing dispensing errors and to outline a roadmap for their safe and effective implementation. We focus in particular on an AI-based natural language processing (NLP) decision-support application as an exemplar, examining how it can be integrated into dispensing workflows to flag high-risk prescriptions and labelling discrepancies before medications reach patients. Using systems thinking, we organise our analysis around four interrelated perspectives: people (training, human-AI teaming, trust), system (interoperability, data pipelines, monitoring), design (human-centred interfaces, uncertainty displays, workflow fit), and risk (ethical oversight, bias assessment, safety assurance, and governance). Across these perspectives, we identify priorities such as multimodal data use, external validation across sites and populations, prospective evaluation with safety and equity metrics, and continuous model monitoring with clear rollback mechanisms. AI can enhance safety, timeliness, and efficiency in dispensing; however, its value depends on disciplined sociotechnical integration and feedback within learning healthcare systems, rather than on standalone algorithmic performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.