Healthcare
AI that supports clinical decisions, moves patients faster and cuts the administrative burden in half
You get clinical decision support agents, patient flow optimisation, medical imaging AI triage, drug discovery acceleration, administrative automation, and population health analytics. All running on your cloud, connected to your EHR and clinical systems, with the governance healthcare regulators demand.
Built on your AWS, Azure or GCP tenant. HIPAA and GDPR ready. Clinician-approved.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Your clinicians are drowning in admin. Your patients are waiting too long. Your diagnostics miss things. AI fixes all three.
Physicians spend two hours on paperwork for every hour of patient care. Emergency departments run at 140 percent capacity. Diagnostic errors affect 12 million adults annually in the US alone. Drug discovery takes 10 to 15 years and costs $2.6 billion per approved compound. These are not resource problems. They are decision-speed and data-access problems.
We build AI agents that sit between your clinical data and your operational decisions — supporting clinical judgement with early warning systems, optimising patient flow in real time, triaging imaging studies, accelerating research pipelines, and automating the administrative burden that keeps clinicians away from patients.
reduction in sepsis mortality with AI early warning systems (McKinsey, 2024)
improvement in patient throughput with AI flow optimisation (BCG, 2024)
reduction in clinical documentation time with AI ambient tools (McKinsey QuantumBlack, 2024)
annual administrative waste in US healthcare addressable by AI (McKinsey Global Institute, 2024)
From patient record to clinical decision in one governed workflow
We connect your EHR, PACS, scheduling systems, claims data, and IoT devices to AI agents that support clinical decisions, optimise patient flow, triage imaging, automate documentation, and generate the regulatory filings your compliance team requires
EHR, PACS, scheduling, claims, IoT
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Clinical alerts
- Operational reports
- Regulatory filings
What we deliver for healthcare organisations
From clinical decision support to drug discovery. Governed, auditable, regulator-ready.
Clinical decision support
Flag patient deterioration early and alert care teams.
ROI
Process
Integrate clinical data
Vital signs, lab results, medication records, and clinical notes streamed from EHR and bedside monitors
Detect risk patterns
ML models identify early signs of sepsis, deterioration, drug interactions, and readmission risk
Alert care teams
Clinically validated alerts delivered to physicians and nursing staff through existing clinical workflows
Sources
McKinsey QuantumBlack, 'AI in clinical decision support,' 2024; BCG, 'Scaling AI in healthcare delivery,' 2024 — reproduced for illustrative purposes
Patient flow optimisation
Predict admissions and optimise bed allocation in real time.
ROI
Process
Forecast demand
Historical admission patterns, current census, and external signals used to predict 12-24 hour admission volumes
Optimise bed allocation
AI matches predicted demand against available capacity, recommending bed assignments and discharge priorities
Coordinate transitions
Discharge readiness predictions and transport scheduling automated to accelerate patient transitions
Sources
BCG, 'AI-powered hospital operations,' 2024; McKinsey, 'Transforming healthcare delivery with AI,' 2024 — reproduced for illustrative purposes
Medical imaging AI triage
Pre-read imaging studies and prioritise urgent findings.
ROI
Process
Analyse imaging studies
AI processes CT, MRI, and X-ray images to identify potential findings and classify urgency level
Prioritise worklists
Urgent findings moved to the top of radiologist worklists with AI-generated preliminary observations
Radiologist review
Radiologist reviews AI-flagged findings alongside prior studies, confirms or adjusts, and issues final report
Sources
BCG, 'AI in medical imaging,' 2024; McKinsey, 'Scaling radiology AI,' 2024 — reproduced for illustrative purposes
Drug discovery acceleration
Identify drug targets and compress discovery-to-approval timelines.
ROI
Process
Target identification
AI analyses genomic, proteomic, and literature data to identify and validate novel drug targets
Lead optimisation
Generative models design and score candidate molecules, predicting efficacy, toxicity, and synthesisability
Trial design
AI optimises patient selection, endpoint design, and dosing strategy to increase clinical trial success probability
Sources
McKinsey, 'AI in drug discovery and development,' 2024; BCG, 'Generative AI in pharma R&D,' 2024 — reproduced for illustrative purposes
Administrative automation
Generate clinical notes and discharge summaries automatically.
ROI
Process
Capture clinical encounters
AI listens to clinician-patient conversations and extracts clinically relevant information in real time
Generate structured documents
Draft SOAP notes, discharge summaries, prior auth forms, and referral letters produced in correct clinical formats
Clinician review and sign
Physician reviews, edits if needed, and signs off. Final documents pushed to EHR with full audit trail
Sources
McKinsey QuantumBlack, 'Reducing administrative burden in healthcare,' 2024; Deloitte Insights, 'AI in clinical documentation,' 2024 — reproduced for illustrative purposes
Population health analytics
Stratify populations by risk and target preventive care.
ROI
Process
Aggregate population data
EHR, claims, social determinants, and community health data combined to build comprehensive patient profiles
Stratify and predict
ML models segment populations by risk level and predict disease progression, hospitalisation, and cost trajectories
Target interventions
High-risk patients matched to preventive programmes, care management, and community resources with outcome tracking
Sources
McKinsey, 'AI in population health management,' 2024; BCG, 'Value-based care with predictive analytics,' 2024 — reproduced for illustrative purposes
Operating theatre scheduling
Optimise theatre schedules and reduce surgical cancellations.
ROI
Process
Predict case durations
ML models forecast procedure length based on patient complexity, surgeon history, and procedure type
Optimise schedules
AI builds daily theatre schedules that maximise utilisation while maintaining appropriate buffer times
Reduce cancellations
Patient readiness checks and pre-op compliance tracked to prevent last-minute surgical cancellations
Sources
McKinsey, 'AI in surgical scheduling,' 2024; BCG, 'Optimising hospital operations,' 2024 — reproduced for illustrative purposes
President, Mayo Clinic Platform
John Halamka
"At Mayo Clinic, we are deploying AI models that augment clinical decision-making across our entire practice. The goal is not to replace clinicians but to give them intelligence at the point of care that would take hours to assemble manually. Early warning systems for sepsis and deterioration have materially reduced mortality in our critical care units."
Originally published: Mayo Clinic Platform — reproduced for illustrative purposes
How we work in healthcare environments
Healthcare AI must satisfy clinicians, patients, and regulators simultaneously. We start by identifying the one use case that delivers the most measurable patient or operational impact, then build it with the clinical governance, explainability, and data protection your organisation requires from day one.
Everything runs on your cloud tenant within your existing security perimeter. Patient data never leaves your environment. Your information governance team, clinical safety officers, and regulators can inspect the system directly.
01
Clinical and data mapping
We assess your clinical data landscape, EHR integrations, and regulatory obligations. Then identify the highest-impact use case that satisfies clinical safety, data protection, and operational requirements.
02
30-day proof of concept
A working AI agent on your cloud, connected to your EHR and clinical systems, producing validated output in 30 days.
03
Clinical governance built in
Explainability, bias testing, patient safety review, and audit trails structured to meet CQC, HIPAA, MHRA, or equivalent regulatory standards from day one.
04
Scale in 12-18 weeks
From one validated clinical or operational workflow to measurable impact across your health system, with continuous monitoring and model governance.
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Frequently Asked Questions
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