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LLM Hallucination Risk in Clinical Decision Support Used within NHS Contexts

Deployment of large language models within clinical decision support systems presents acute patient safety challenges arising from generative hallucinations and evidence misgrounding. While retrieval-augmented generation improves factual fidelity across guideline-based tasks, persistent failure modes and alignment biases necessitate standardised evaluation frameworks and domain-specific knowledge integration across NHS infrastructure.

Goal of work

To evaluate hallucination risks and mitigation strategies in large language models deployed for clinical decision support within NHS secondary care environments.

Methodology

Desk-based systematic narrative synthesis of recent peer-reviewed clinical natural language processing studies, architectural benchmarks, and governance standards.

Scientific novelty

Synthesises retrieval-augmented mitigation failures with affective misgrounding dynamics specifically mapped to NHS clinical workflows and ontology integration.

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Research Article

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LLM Hallucination Risk in Clinical Decision Support Used within NHS Contexts

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First M. Last

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Dr. First Last

City, 2026

Contents

Abstract
Introduction
Mechanisms and Typologies of Large Language Model Hallucinations in Healthcare
Clinical Natural Language Processing and Knowledge Integration in NHS Secondary Care
Evaluation of Retrieval-Augmented Generation for Hallucination Mitigation in Clinical Workflows
Affective and Semantic Misgrounding in Instruction-Tuned Clinical Models
Clinical Safety Vulnerabilities and Diagnostic Risks under NHS Digital Governance
Standardised Validation Frameworks and Quality Assurance Mechanisms
Conclusion
Bibliography

Introduction

Large language models in clinical decision support systems introduce substantial operational capabilities alongside critical clinical safety challenges, predominantly driven by ungrounded or factually incorrect generative hallucinations [2]. Within the National Health Service, integrating natural language processing into hospital platforms requires stringent alignment with structured terminologies and clinical pathways to maintain reliability across secondary care specialties [3].

Despite advances in inference-time architectural mitigations such as retrieval-augmented generation, generative models remain susceptible to retrieval errors, residual hallucinations, and affective misgrounding [2, 6]. Instruction-tuning and alignment objectives often introduce systematic distortions where conversational fluency or superficial empathy precedes evidentiary grounding, compounding diagnostic risks [6].

This paper examines the structural causes and clinical implications of language model hallucinations within NHS operational environments, synthesising recent mitigation architectures and evidence grounding strategies [2, 3]. By assessing systemic vulnerabilities across clinical language pipelines, the review establishes rigorous validation benchmarks necessary for safe clinical deployment.

Clinical Safety Vulnerabilities and Diagnostic Risks under NHS Digital Governance

The deployment of retrieval-augmented generation in clinical settings substantially enhances factual grounding relative to unconstrained language architectures, particularly across protocol-driven secondary care tasks [2]. However, embedding these neural systems into NHS clinical workflows demonstrates that retrieval augmentation alone does not eliminate hallucination risks [2, 3]. Residual hallucinations persist due to downstream synthesis failures, where language models integrate retrieved passages selectively or misinterpret complex multi-document clinical guidelines [2]. Furthermore, standard model alignment techniques designed to optimise conversational empathy introduce subtle affective misgrounding, prioritising conversational compliance over strict evidentiary fidelity [6]. In acute and ambulatory care contexts, such distortions can generate erroneous diagnostic recommendations cloaked in persuasive, fluent language [2, 6]. Consequently, technical mitigation strategies must be coupled with rigorous hospital-level data curation platforms and standardised terminology mappings, such as SNOMED CT frameworks, to ensure that generated outputs remain strictly bounded by validated medical records and local institutional guidelines [3]. Without prospective validation and robust clinical governance, high-stakes decision support remains vulnerable to systematic generative error [2].

References

  1. Evaluating Hallucinations in Large Language Models(LLM):Metrics and Mitigation
    Thrilok. Koll, Tina Babu, Rekha R Nair
    DOI Link
  2. Retrieval-Augmented Large Language Models for Clinical Decision Support: A Systematic Review of Hallucination Mitigation and Evidence Grounding
    Sumit Barua, Charles Barnabas Rodgers
    DOI Link
  3. Natural language processing data services for healthcare providers
    Joshua Au Yeung, Anthony Shek, Thomas Searle et al.
    DOI Link
  4. Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
    Xinyu Lyu, Beitao Chen, Lianli Gao et al.
  5. Trustworthiness of large language models: hallucinations
    Nicolò Brunello
  6. Affective Misgrounding in Aligned Language Models: A Quantitative Analysis of Emotional Hallucination in LLM Responses
    Som Subhro Nath

Bibliography

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