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Artificial Intelligence in Allied Health Practice: Current Evidence, Emerging Applications, And Implications for Patient Safety

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Artificial intelligence (AI) is moving from experimental research into routine healthcare workflows, including diagnostic support, clinical documentation, rehabilitation, laboratory medicine, medical imaging, patient education, and service management. Allied health professionals are increasingly involved in these AI-enabled pathways, yet evidence and implementation guidance are often concentrated in physician-led specialties. This narrative review synthesises current evidence on the use of AI across allied health practice and examines implications for patient safety, professional accountability, education, and health equity. Literature from peer-reviewed journals and authoritative guidance was considered, with emphasis on clinical applications, implementation barriers, bias, explainability, human-AI interaction, and generative AI. Current evidence supports potential benefits such as workflow support, image interpretation, pattern recognition, personalised rehabilitation, laboratory decision support, documentation assistance, and improved access to health information. However, technical performance alone does not establish clinical effectiveness or safety. Dataset shift, hidden bias, automation bias, inaccurate generated content, privacy risks, poor interoperability, insufficient local validation, and unclear responsibility can introduce new failure modes. Safe adoption should include a defined intended use, representative validation, transparent performance monitoring, user training, incident reporting, privacy safeguards, human oversight, and periodic re-evaluation after deployment. The review concludes that AI has meaningful potential across allied health practice, but sustainable adoption depends on evidence-based governance and continuous patient-safety monitoring rather than technology adoption alone.

Improving Inter-Hospital Transfer Documentation Through Standardisation and Accountability: A Quality Improvement Study in a Resource-Limited Sri Lankan Hospital

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Background: Incomplete transfer documentation contributes to communication failures, delayed clinical decision-making, and increased patient safety risks during inter-hospital transfers. These risks are amplified in resource-limited settings due to system inefficiencies, high workload, and lack of standardisation.¹–³

Objective: To improve the completeness, accuracy, and accountability of inter-hospital transfer documentation through structured system-level interventions.

Methods: A single-centre quality improvement study using Plan–Do–Study–Act (PDSA) cycles was conducted at Base Hospital Kinniya, Sri Lanka from January to June 2019 for a period of 6 months. A retrospective audit of 436 inter-hospital transfer forms was conducted using predefined criteria derived from Ministry of Health standards and stakeholder consensus. Two PDSA cycles were implemented: the first introduced staff training and a structured documentation seal; the second introduced a “probable diagnosis” field and a checking officer system. Key indicators included documentation of diagnosis, reason for transfer, vital signs, treatment given, and time of transfer. Descriptive statistics were used to analyse changes in documentation completeness.

Results: Baseline assessment demonstrated major deficiencies in documentation, including diagnosis (61.5%), treatment given (42.2%), time of transfer (14.7%), and oxygen saturation (26.6%). Following PDSA cycle 1, improvements were observed across all parameters. After PDSA cycle 2, near-complete compliance was achieved in most domains, including diagnosis (99.1%), heart rate (100%), blood pressure (100%), and time of transfer (98.6%). Treatment documentation improved to 94.9%, and oxygen saturation to 98.6%. Documentation verification reached 100% following introduction of a checking officer.

Conclusion: System-level interventions incorporating standardisation, training, and accountability significantly improved transfer documentation completeness. This low-cost, scalable approach is potentially applicable to similar resource-limited healthcare settings.