Navigating the New Frontier: Understanding and Mitigating Risks of AI-Generated Clinical Content in the NHS

Why this topic matters

AI's promise in healthcare includes improving efficiency, enhancing diagnostics, and personalising treatment. For instance, AI can assist in drafting clinical notes, summarising patient records, generating treatment plan options, or even aiding in research synthesis. While these applications can free up valuable clinical time and potentially improve care quality, the integrity and reliability of the AI-generated output are paramount. Using inaccurate, biased, or inappropriate clinical content can have serious repercussions, ranging from misdiagnosis and delayed treatment to erosion of patient trust and significant medico-legal complexities. As AI tools become more sophisticated and accessible, a proactive and informed approach to risk management is essential for any NHS organisation considering their deployment.

Practical explanation

AI-generated clinical content refers to any text, image, or data output by an AI system that is intended to be used in a clinical context. This could include, but is not limited to:

• Clinical documentation: Drafts of discharge summaries, clinic letters, referral letters, or ward round notes.
• Decision support aids: AI-suggested diagnostic differentials, treatment pathways, or drug dosage recommendations (always requiring human verification).
• Patient information: AI-generated explanations of conditions, treatments, or aftercare instructions.
• Research and synthesis: Summaries of clinical literature, analysis of data for audit or research projects.

The core challenge lies in the nature of AI models, especially LLMs. They are designed to generate plausible-sounding text based on patterns learned from vast datasets, not necessarily to provide factually accurate or clinically sound information. This distinction is crucial.

Key risk areas

1. Accuracy and Hallucinations: AI models can sometimes generate 'hallucinations' – outputs that are completely fabricated but presented as facts. In a clinical context, such inaccuracies could lead to incorrect diagnoses or inappropriate treatment plans.
2. Bias and Equity: AI models learn from the data they are trained on. If this data is biased (e.g., under-represents certain demographic groups, contains historical inequities in care), the AI's output can perpetuate or even amplify these biases, leading to health inequalities. This could manifest as different diagnostic probabilities or treatment recommendations for different groups, even when clinically unwarranted.
3. Lack of Transparency and Explainability: Many advanced AI models are 'black boxes,' meaning it's difficult to understand why they produced a particular output. This lack of explainability (or 'interpretability') makes it challenging to scrutinise, validate, or trust their recommendations, especially when clinical reasoning is paramount.
4. Data Security and Privacy (Information Governance): Inputting patient-identifiable data or sensitive clinical information into unapproved AI tools poses significant information governance risks, including breaches of GDPR and NHS data security standards. Even anonymised data can sometimes be re-identified.
5. Accountability and Liability: When an AI system contributes to a clinical error, determining who is accountable – the clinician, the AI developer, the deploying organisation – is a complex legal and ethical question. Clear pathways of responsibility are often ill-defined.
6. Over-reliance and Deskilling: Over-dependence on AI tools can lead to clinicians reducing their own critical thinking and diagnostic skills. This makes them vulnerable when the AI generates incorrect information or fails.
7. Ethical Considerations: Beyond bias, ethical concerns include patient autonomy (do patients know AI is involved in their care?), informed consent (how do we consent for AI use?), and the potential for dehumanisation of care.
8. Disinformation and Misinformation: Malicious actors could potentially manipulate AI models to generate harmful clinical advice, or AI could inadvertently misinterpret complex medical literature, leading to the spread of misinformation.

Common pitfalls

Organisations often stumble when implementing AI by:

• Adopting tools without rigorous clinical validation: Assuming a tool tested in one environment (e.g., a commercial setting) will perform equally well and safely in the NHS without local validation.
• Insufficient staff training: Deploying AI without adequately training clinicians on its capabilities, limitations, and how to critically evaluate its output.
• Bypassing established governance: Failing to engage information governance, clinical safety, audit, and ethics committees from the outset.
• Ignoring the 'human in the loop' principle: Automating decisions completely rather than using AI as a support tool for human clinicians.
• Lack of continuous monitoring: Not implementing systems to track AI performance, detect biases, or identify adverse events over time.
• Procuring 'off-the-shelf' solutions without due diligence: Not verifying the AI’s training data, its robustness, and its adherence to UK regulatory standards (e.g., MHRA if applicable).

Step-by-step approach to safe AI adoption

Implementing AI safely requires a structured, multidisciplinary approach. This framework combines elements of quality improvement, clinical governance, and digital transformation.

1. Define the Problem and AI's Role

• Clearly articulate the clinical problem you are trying to solve. What specific pain point or inefficiency will the AI address? E.g., 'reducing transcription time for clinic letters', 'improving diagnostic accuracy for specific conditions'.
• Determine if AI is the most appropriate solution. Could a simpler process change achieve the same goal?
• Define the scope: What content will the AI generate? Who will use it? What are the expected outputs and their intended use?

2. Establish Robust Governance

• Multidisciplinary Steering Group: Form a group including clinicians, QI leads, information governance, clinical safety officers, ethics committee representation, digital leads, and legal counsel.
• Risk Assessment Framework: Develop or adapt a comprehensive risk assessment, specifically addressing the unique risks of AI-generated content (e.g., Clinical Risk Management through the DCB0129/DCB0160 standards for medical devices, even if the AI isn't formally a medical device).
• Ethical Review: Engage your local ethics committee to discuss the ethical implications, particularly regarding patient consent and algorithmic bias.
• Information Governance (IG) Approval: Ensure full IG approval for data handling, storage, and processing, especially any patient-identifiable data used for training or input.

3. Source and Validate AI Solutions

• Due Diligence: For commercial solutions, demand comprehensive information on the AI's training data, performance metrics, and clinical safety evidence. Scrutinise how it addresses bias and explainability.
• Local Clinical Validation: Do not rely solely on vendor claims. Conduct thorough local validation studies or pilots in your specific clinical context. This must involve clinicians evaluating the quality, accuracy, and safety of the AI-generated content.
• Benchmarking: Compare AI output against established clinical standards and human clinician performance in a controlled environment.
• Data Integrity: If developing in-house, ensure training data is high-quality, representative, and ethically sourced. Consider synthetic data generation where appropriate for privacy.

4. Implement with 'Human in the Loop'

• Mandatory Human Review: Insist on human oversight for all AI-generated clinical content. The AI should serve as a co-pilot, not an autopilot.
• Clear Responsibility: Explicitly define that the clinician using the AI tool remains ultimately responsible for the patient care decisions and the accuracy of the final clinical documentation.
• User Interface Design: Design interfaces that clearly distinguish AI-generated content from human-generated content and highlight areas requiring particular scrutiny.
• Audit Trails: Implement robust audit trails demonstrating who reviewed, modified, and approved AI-generated content.

5. Training and Education

• Comprehensive Training: Provide mandatory training for all staff using AI tools, covering their functionality, limitations, potential risks, and the critical importance of reviewing outputs.
• Promote AI Literacy: Educate staff on the principles of AI, machine learning, and common AI pitfalls like hallucinations and bias.
• Continuous Professional Development: Offer ongoing education as AI technologies evolve.

6. Monitor, Evaluate, and Iterate

• Performance Monitoring: Continuously track the AI's performance using defined metrics (e.g., accuracy against clinical gold standards, error rates, time savings).
• Adverse Event Reporting: Establish clear pathways for reporting any adverse events or 'near misses' related to AI-generated content. Incorporate these into existing clinical governance structures.
• Feedback Loops: Create mechanisms for clinicians to provide direct feedback on AI performance and usability.
• Regular Review: Periodically review the AI's impact on patient outcomes, health inequalities, and clinical workflow. Be prepared to update, modify, or even decommission tools if they prove unsafe or ineffective.

Example in clinical practice

Consider an NHS Trust implementing an AI-powered drafting tool for discharge summaries in an acute medical ward. Instead of immediately deploying it across all wards, the Trust adopts a phased approach:

1. Define: The problem is the time-consuming nature of discharge summaries, often leading to delays and transcription errors. AI could draft initial summaries from EHR data.
2. Govern: A working group, including doctors, nurses, pharmacists, IT, IG, and clinical safety, is formed. They adapt the DCB0129 standard, conduct a DPIA, and get ethical approval for a pilot.
3. Validate: The Trust procures a CE-marked AI tool and sets up a pilot on one ward. They run it 'shadow mode' initially, comparing AI-drafted summaries for 50 patients against human-written ones for accuracy, completeness, and clinical safety. Senior clinicians review every AI-drafted summary.
4. Implement: After a successful shadow phase, the tool is enabled, but every AI-generated summary still requires explicit sign-off by a consultant. Trainees use it as a first draft, but are taught to critically appraise every sentence. A clear disclaimer is added stating, 'AI-generated draft – full clinical review and validation required by responsible clinician.'
5. Train: All pilot ward staff receive dedicated training on using the tool, understanding its limitations, and identifying potential errors or 'hallucinations'. They are taught to verify patient details, medication lists, and follow-up plans independently.
6. Monitor: A dashboard tracks the percentage of AI-generated summaries requiring significant edits, reported errors, and any associated patient safety incidents. Regular feedback sessions are held with pilot ward staff. Learning from this pilot informs a potential wider rollout, with continuous iteration on the tool and processes.

This iterative, governance-heavy approach minimises risk while allowing the Trust to explore the benefits of AI.

How Lazomis can help

Lazomis provides a structured environment to support your NHS organisation in navigating AI adoption safely:

• QI Project Setup: Use Lazomis to define and manage your AI pilot projects, including setting clear objectives, identifying key stakeholders, and tracking progress through PDSA cycles. This helps embed a quality improvement methodology into your AI implementation.
• Risk Registers: Utilise Lazomis's integrated risk management tools to document, assess, and monitor specific risks associated with AI-generated content, linking them to mitigation strategies and responsible individuals.
• Audit and Evaluation Templates: Our configurable templates can assist in structuring your local validation studies, capturing data on AI performance, accuracy, and clinical safety outcomes. This supports rigorous monitoring and evaluation.
• Reporting and Dashboards: Create dashboards to visualise key performance indicators for your AI initiatives, allowing you to monitor accuracy rates, clinician feedback, and adherence to governance requirements for continuous improvement.

This resource supports, but does not replace, clinical judgement. Local policy, formulary and specialist advice should be followed. NHS organisational approval, clinical governance, information governance, and CQC registration are mandatory for any AI deployment in clinical settings.

Key takeaways

• AI in healthcare offers significant opportunities but carries substantial risks, particularly with AI-generated clinical content.
• Key risks include inaccuracies ('hallucinations'), bias, lack of transparency, information governance issues, and unclear accountability.
• Adopting AI requires robust, multidisciplinary governance, including clinical, information governance, and ethical review.
• Always maintain a 'human in the loop'—AI should support, not replace, clinical judgment.
• Thorough local clinical validation, comprehensive staff training, and continuous monitoring are critical for safe implementation.
• Lazomis tools can support the structured planning, risk management, and evaluation of your AI adoption projects.

Related Resources

• Developing a Robust Clinical Governance Framework
• PDSA Cycles: A Practical Guide for NHS Teams
• Information Governance in Digital Health Projects
• Understanding and Mitigating Bias in Healthcare Data
• Introduction to Clinical Safety (DCB0129/0160) for Digital Health

Relevant Lazomis Tools

• Lazomis QI Project Setup
• Lazomis Risk Register
• Lazomis Data Collection Forms
• Lazomis Dashboards