AI for UK Healthcare Organisations | OpenKit

AI for UK healthcare organisations.

AI is moving into UK healthcare faster than most teams know how to assess it. We help you map where it fits in your organisation, and build bespoke systems where the work calls for it. Across clinics, specialist groups, GP federations, MedTech SMEs and NHS trusts on approved-supplier routes.

ISO 27001 certified DSP Toolkit aligned Clinician-supervised
A person smiling while looking at a phone, using a healthcare service
1 in 4
UK GPs already use generative AI in clinical practice.
Source: Pulse Today, 2024 UK GP survey

OpenKit is a UK AI consulting firm that helps healthcare organisations find where AI fits and builds bespoke systems where the work calls for it. OpenKit delivers AI audits and clinician-supervised builds for UK clinics, specialist groups, GP federations, MedTech SMEs, and NHS trusts via approved-supplier routes. OpenKit holds ISO 27001, ISO 9001, and Cyber Essentials certifications, and aligns to the NHS DSP Toolkit. OpenKit does not replace clinical judgement.

OpenKit is a UK AI consulting firm that helps healthcare organisations find where AI fits and builds bespoke systems where the work calls for it. OpenKit delivers AI audits and clinician-supervised builds for UK clinics, specialist groups, GP federations, MedTech SMEs, and NHS trusts via approved-supplier routes. OpenKit has ISO 27001, ISO 9001, and Cyber Essentials certifications and works with clients across the United Kingdom from a base in Cambridge.

What we keep hearing
from healthcare leaders.

Four patterns we see in every healthcare audit, whether the conversation starts with a clinical lead, a practice manager, an IT director, or a MedTech founder. Each one is also why most quick-win AI rollouts stall before they hit production.

89%
of non-using GPs cite professional liability as the primary barrier to AI adoption.

Professional liability blocks adoption.

Senior clinicians know AI can help but cannot defend its use without a documented workflow, an audit trail and a sign-off model. Without those, the safer call is to keep doing it manually.

Source: Pulse Today UK GP survey, 2024.
PATTERN 01

Unsupervised, fragmented adoption.

Staff across clinical, admin and operational roles are already using public AI tools at work, with no audit trail and no policy. The liability sits on the individual; the organisation has no visibility on what data has been pasted into a public model.

Pulse Today UK GP survey · gen-AI study, PMC 12647557
PATTERN 02

Regulatory gap on AI-as-Medical-Device.

The line between a clinical workflow tool and a software-as-medical-device classification is not always obvious. Most teams hit the question late, when the build is already half-shipped.

MHRA 2026 AIaMD framework · AI Airlock pilot findings
PATTERN 03

Patient data cannot leave the controlled environment.

UK GDPR, Caldicott and the DSP Toolkit all expect the controlled environment to stay controlled. That rules out half the SaaS AI tooling the team gets pitched in any given week.

UK GDPR Art. 9 · Caldicott principles · NHS DSP Toolkit

Where AI tends to fit
in healthcare.

These are the areas that come up most often in audits. We don't ship them off the shelf. Every implementation is shaped to your team, your systems, and what is safe to defend in your environment.

01 — Clinical decision support

Assistive, never autonomous.

Always-assistive systems with confidence scores, uncertain-case routing to a clinician, and a full audit log on every inference. The model recommends; the clinician decides.

  • Differential diagnosis support over patient context
  • Triage prioritisation with explainable scoring
  • Drug-interaction checking against full medication history
  • Audit log on every inference, retained per ICO guidance
Close-up of a stethoscope on a clinician's desk
02 — Private knowledge systems

Searchable, cited answers from your own knowledge base.

Assistants that answer questions from your own materials (guidelines, SOPs, prior cases) and cite where every answer came from. Deployed inside your controlled environment so the content and the queries never leave it.

  • Search across NICE guidance, local SOPs, and prior decisions
  • Imaging and notes search with redaction-aware retrieval
  • Citations on every answer, traceable to source
  • Runs inside your controlled environment
03 — Patient-journey automation

Drafts that wait for clinician sign-off.

Intake triage, documentation, follow-up communications, and post-visit care plans. Drafted by the model, reviewed and signed off by the clinician before anything reaches the patient.

  • Voice-to-text consultation notes with SOAP formatting
  • Post-visit care plan generation from session notes
  • Follow-up reminders and patient education drafts
  • Always queues for sign-off, never sends autonomously
A clinician at a desk reading a notebook of patient notes
04 — Admin & operations

The wins outside clinical decision-making.

Rota optimisation, coding, claims, referral management, prior authorisation. Most of the healthcare AI value lives outside clinical decisions. Lower regulatory burden, faster to ship.

  • Coding and claims drafting with human-in-the-loop review
  • Referral letter drafting from structured patient context
  • Rota and resource scheduling against demand patterns
  • Prior authorisation paperwork at draft-and-review speed

An example of what this can look like in practice.

A clinician-supervised documentation assistant. Drafts the consultation note from the recorded session, cites prior history, and queues for clinician sign-off. We shape work like this around your existing workflow rather than ship it off the shelf.

A clinician reviewing notes on a tablet
Draft only · Clinician sign-off required The model never sends to a patient. It never updates a record. The clinician of record signs every output before it leaves the firm.

The model drafts. The clinician signs off. The log keeps both.

Every clinician-touching assistant we build follows the same shape. The model never autoreplies, never updates a patient record on its own, never sends communications without sign-off. The audit log retains the prompt, the model output, the clinician's edits, and the final, so the responsible-clinician-of-record is unambiguous at every step.

  • Recorded session in, structured SOAP-formatted draft out
  • Cited against patient history, NICE guidelines, and local SOPs
  • Always queues for clinician sign-off, never autoreplies
  • Audit log retained per ICO guidance and DSP Toolkit
  • Runs inside your controlled environment

Systems we work across
in UK healthcare.

We build on your existing clinical and operational systems. No rip-and-replace. Below is a sample of what we routinely integrate with. We work across many other systems too, so bring us your stack.

/ clinical systems

  • SystmOne (TPP)
  • EMIS Web
  • Vision (Cegedim)
  • Cerner Millennium (UK)
  • Accurx · iPlato

/ documents & imaging

  • PACS · DICOM
  • Document management (DMS)
  • M365 · SharePoint
  • Egress · OneDrive
  • NHS Mail

/ identity & access

  • NHS Smartcard
  • Microsoft Entra ID
  • SAML · OIDC
  • Role-based access control
  • Full audit logging

/ LLM deployment

  • Claude UK/EU region
  • OpenAI on Azure UK/EU
  • Open-weights on private GPU
  • On-prem for SaMD-class
  • VPC for everything else
REGULATED CONTROLS: ISO 27001 ISO 9001 Cyber Essentials UK GDPR

Engagements we have run
in healthcare and life sciences.

A sample of recent healthcare work. Some clients are named with permission; others stay anonymised. Outcomes are described without internal artefacts.

— 01

MyPain

Chronic pain triage Multi-agent Langfuse-traceable Named with permission
A clinician-supervised AI triage system for chronic pain. Built from an early MVP through to a production multi-agent architecture, now live with clinicians signing off every patient journey. Every inference is observable in Langfuse.
Outcome Now live at mypain.ai. Every patient journey traceable end-to-end. Clinician-in-the-loop on every triage decision.
— 02

EMQN

Clinical genomics Quality assurance Discovery sprint Named with permission
An AI discovery sprint with the European Molecular Genetics Quality Network. Mapped where AI fits across the assessment and reporting workflow for clinical genomics labs, with regulatory and data-handling constraints documented up-front.
Outcome Prioritised opportunities list. Sequenced rollout plan. Build path scoped for follow-on engagement.
— 03

A UK private healthcare group

Multi-site Admin automation Pilot Anonymised
Operational AI pilot across coding, referral letter drafting, and prior authorisation paperwork. The clinical workflow stayed out of scope intentionally. The brief was to take the admin load off clinicians so they could spend more time on the work AI does not touch.
Outcome Pilot shipped. Draft-and-review workflow adopted. Clinical scope deferred to a follow-on audit.

The regulatory floor
we build on.

What we hold and what we operate to. We surface gaps and propose mitigations. We are not a regulatory certifying body.

CERTIFIED

ISO 27001

Information security management. Independent third-party audited.

CERTIFIED

ISO 9001

Quality management. Independent third-party audited.

CERTIFIED

Cyber Essentials

UK NCSC baseline cyber-hygiene certification.

COMPLIANT

UK GDPR

Data processing register maintained per ICO guidance.

We hold ISO 27001, ISO 9001, and Cyber Essentials certifications independently, and operate to UK GDPR. We are not a clinical-safety auditing body. For software-as-medical-device candidates we work alongside your regulatory and clinical-safety leads.

Questions practice managers
and clinical leads ask.

What does an AI consultancy for UK healthcare actually do?
We run an audit of where AI fits across your clinical and operational workflows, configure the workflows on your existing stack, train your team, and stay on retainer for the regulatory paperwork. Every workflow is clinician-supervised — the model recommends or drafts, the clinician decides or signs off.
Will the AI replace clinical judgement?
No. Everything we build is clinician-supervised by default. Clinical-decision-support systems are always assistive — they recommend with a confidence score and route uncertain cases to a clinician. Documentation and patient communications draft to a clinician's queue and require sign-off before sending. We do not build autonomous clinical decision-makers.
How does this work with the DSP Toolkit and Caldicott?
The audit phase includes a DSP Toolkit alignment review and a Caldicott principles check as standard. Every workflow we configure has a documented lawful basis under UK GDPR, a data flow map, and an audit log. We are not a certifying body for the DSP Toolkit — we surface gaps and propose mitigations that your information governance lead can take to renewal.
Is your work regulated by MHRA as a medical device?
Most healthcare workflows we configure are not software-as-medical-device. Where a workflow crosses the line — diagnostic decision support, triage that affects clinical pathway, autonomous patient communications about care — we flag it during the audit and either redesign to stay below the SaMD threshold or scope a Bespoke Build alongside your regulatory and clinical-safety leads under the MHRA AIaMD framework. We are not a notified body.
Can we keep patient data on-prem?
Yes. We deploy inside your controlled environment by default. Standard options: your VPC, your tenant on Azure UK/EU, or on-prem GPU for sovereign and SaMD-class workloads. Patient data does not leave the room. The model and the audit log sit on the same side of the boundary as the data.
Does this integrate with SystmOne, EMIS, or Vision?
Yes — those three plus Cerner Millennium (UK), Accurx, iPlato, your DMS, PACS for imaging, and M365 / SharePoint for documents. We build on what you already run. The audit phase maps the integration points and flags where API access is the blocker before we commit to a build.
What about clinical liability if AI makes a mistake?
Clinician-supervised by default means the responsible clinician signs the work. We document the workflow, the audit trail, and the model's role in writing so the responsible-clinician-of-record is unambiguous. Where the workflow crosses into territory that affects clinical liability arrangements, we recommend that the practice's medical-defence union is consulted before go-live. We do not carry clinical indemnity.
How long does a healthcare AI engagement take?
Audit only is two weeks. Audit plus Transformation Block (configured workflows + training) is four weeks end-to-end. Bespoke Builds — including SaMD-class systems — are scoped per project after the audit, typically 8 to 16 weeks for a production build.
What does it cost for an SME healthcare organisation?
Audit-only is low five figures, fixed fee. Audit plus Transformation Block is in the high four to mid five figures depending on scope. The Embedded AI Lead retainer is low four figures per month, rolling and cancellable. Bespoke Builds are quoted after the audit. There is no retainer required to access the audit.
Have you delivered similar work in healthcare?
Yes — MyPain (clinician-supervised AI triage for chronic pain, now live at mypain.ai with full Langfuse traceability), EMQN (AI discovery sprint in clinical genomics QA), and a UK private healthcare group on operational and admin automation. We can share named references on request.
UK HEALTHCARE
/ start here

Want to find where AI fits
in your organisation?

Start with the audit. Fixed fee, fixed scope, a written report you can take to a board, a CCG, or hand to an internal team, and a prioritised plan for what to build first.

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