Will AI Tools in Hospitals Change How Medical Malpractice Claims Arise?
The NHS is testing an AI tool to speed up hospital discharges. While it may ease admin pressures, it also raises new questions around liability, risk, and medical indemnity as AI becomes part of clinical decision-making.
October 8, 2025
A recent Guardian article describes how the NHS is trialling an artificial intelligence (AI) tool at a London trust to speed up hospital discharges. The software is designed to ingest patient records, compile diagnoses, test results, and other relevant data, and automatically draft a discharge summary (or suggest onward referrals), subject to clinician review.
At first glance, this seems like a welcome innovation, reducing administrative burden, freeing up clinician time, and potentially reducing delays. But as with any technology overlaid on clinical pathways, it carries implications for liability, risk allocation, and Medical Indemnity.
Below, we explore how this development may reshape the medical malpractice insurance landscape, what new exposures might arise, and how clinicians and organisations should think about their cover.
How AI-assisted Discharge Tools Impact Medical Malpractice Insurance.
1. Shifting roles, shifting liability
When part of the documentation or decision support is generated by AI, it complicates who is responsible for the content. Is the clinician just “rubber stamping” an AI draft? Did the AI omit or misinterpret a test result? In the event of a harm (e.g. a prematurely discharged patient who deteriorates), the role of the AI tool in contributing error or omission will likely be scrutinised.
From an exposure perspective, this raises questions:
- Should the AI vendor or system carry part of the liability (via errors, bugs, model drift, data input errors)?
- Is the trust/clinic contractually indemnified by the AI provider for errors through AI?
- Does the physician remain fully responsible even if the AI had a significant influence in drafting the summary or decision support?
These are not theoretical. In other technology-adjacent fields, we already see claims where a ‘decision support’ tool is alleged to have misled the professional user.
2. Changes in exposure and frequency
By speeding up discharges, AI tools might lead to a (modest) increase in risk of premature discharge or inadequate follow-up. E.g:
- A patient’s abnormal lab value may have been overlooked by the AI in the summary, and physician oversight misses it.
- The tool may suggest a referral pathway that is not fully tailored, leading to delays in correct specialist follow-up.
- Edge cases, comorbidities, or rare diagnoses may not be well handled by the AI model, especially when trained on “typical” data.
Such edge-case blind spots are classic for AI systems, especially in medicine, where rare disease or overlapping risks may be underrepresented in training data.
Conversely, the automation of documentation might reduce documentation error omissions (e.g. leaving out key data), which is a known area of claims risk.
Therefore, the severity of claims may increase (because the AI could introduce systematic error), even if the frequency remains low.
3. New trigger points and coverage ambiguity
Medical indemnity policies are typically triggered by an act, omission, or error in clinical care (i.e. clinical negligence). But with AI tools in the loop, new boundary questions arise:
- If the AI tool’s ‘error’ is algorithmic (not strictly ‘clinical’), would a professional indemnity policy respond, or is that a technology/IT liability issue?
- If the AI provider has a contractual liability to the hospital, there may be indemnity or subrogation rights.
- The policy must be clear about where coverage ends. For example, does the indemnity cover negligent oversight of AI output, or only pure clinician-originated errors?
Policy holder should know:
- Does the policy cover decision support systems, AI or algorithmic assistance used in patient care or documentation?
- Is there an exclusion for errors arising from software or automated systems? As is typical (through medical malpractice exclusion) on standard Tech PI policies.
- What sublimit or additional premiums apply?
Key Considerations When Arranging Medical Malpractice Policies in the AI Era
Below are practical pointers and strategic questions for Medicas’ clients (and prospective insureds) to consider when assessing or renewing coverage.
| Consideration | What to Ask / Negotiate | Why It Matters |
|---|---|---|
| AI / software inclusion clause | Ensure the policy explicitly includes coverage for "use, oversight or review of algorithmic decision support, AI, machine learning, or software-assisted documentation" | Prevents denial of claims on the basis of an "automation exclusion" |
| Exclusions & carve-outs | Check for any clauses excluding liability arising from "software malfunction," "electronic systems," or "computer errors" | Some policies may treat software errors as excluded "IT risks" |
| Sublimits / endorsements | Negotiate higher (or stand-alone) limits for technology-enabled systems, or avoid small sublimits that could hamper recovery | A serious AI-related claim could exceed modest sublimits |
| Vendor indemnities / subrogation | Ensure that, when procuring AI systems, your contracts with vendors include adequate indemnity and liability backstops (and your insurer is aware) | Enables your insurer to recoup loss from vendor, and reduces net liability |
| Data input errors / garbage in, garbage out | Clarify whether coverage extends when the underlying error was in data input (e.g. miskeyed lab values) and not the AI algorithm itself | Some disputes may hinge on whether the clinician vs. the software "caused" the error |
| Regulatory compliance & standards | Confirm that the AI system complies with applicable medical device / AI regulation (UK's MHRA, UKCA, etc.) | If the system is non-compliant, insurers may argue contributory negligence or denial |
| Clinical oversight / human in the loop | Keep explicit in policy that a clinician's approval is required over AI output, and that this oversight cannot be fully delegated | This helps maintain clear lines of responsibility and reduces "automation bias" |
| Prior acts / retroactive cover | Because AI adoption is emerging, insureds should ask for retroactive cover for claims arising from AI-enabled care during past periods | Helps cover 'latent' claims that manifest later |
| Risk management / incident monitoring | Require use of incident logs, audits of AI output, monitoring for AI drift, validation & calibration processes, and protocols for overrides | Insurers may offer premium incentives or insist on these practices |
| Premium / underwriting adjustments | Be prepared that premiums may increase or that the underwriter will require more precise quantification of the AI's role or assurance of safety architecture (e.g. transparency, explainability) | AI is a new risk vector; underwriters may demand more diligence |
How We Can Help You
At Medicas, we recognise that the intersection of AI and medicine is a rapidly evolving space. We offer:
- Bespoke assessments of your indemnity exposure in settings where AI/algorithmic tools are used.
- Tailored policy endorsements or wording negotiations to ensure clarity over ‘AI-enabled risk.
- Advice on vendor indemnity strategies and how to structure contracts to optimise insurer subrogation.
- Risk management consultation (audit schedules, AI output review protocols, calibration oversight).
If you’re currently using or planning to deploy AI or algorithmic decision support in your practice or facility and want to understand how that impacts your medical malpractice cover, contact us for a review.
Disclaimer: This article is provided by Medicas for educational purposes only. It does not constitute legal advice, and practitioners should seek appropriate legal or professional counsel regarding their specific circumstances.
