AI for Healthcare

Ambient scribing records (with consent) or transcribes a consultation, separates the speakers, and drafts a structured note - history, examination, assessment and plan - in the format your clinicians already use. The draft lands in the EHR as a pending note that the clinician edits and signs; nothing is filed on their behalf. The hard parts are rarely the speech model itself: they are specialty vocabulary, mixed-language consultations, noisy rooms, and mapping free text onto coded fields such as problems, medications and allergies without inventing detail.

We treat hallucination as the main risk. Notes are generated only from what was said, uncertain passages are flagged rather than smoothed over, and each sentence can be traced back to the transcript so a reviewer can check it quickly. Audio retention is configurable - many organisations prefer to delete recordings once the note is signed.

Patient-facing assistants answer questions from your approved content, collect symptoms and history before a visit, and route requests to the right service, clinician or appointment type. For triage we use rules and protocols your clinical team defines, with the language model handling conversation and data capture rather than making the clinical decision. Red-flag symptoms always escalate to a human or direct the patient to urgent care, and the assistant states clearly that it is not a clinician.

On the scheduling side, AI can match appointment types to available slots, fill cancellations from a waitlist, send reminders, and predict likely no-shows so staff can act early. These are high-volume, low-risk tasks where automation usually returns time fastest.

In imaging, AI is most useful as a second pair of eyes and a worklist tool: prioritising studies with suspected urgent findings, measuring and comparing lesions across studies, and drafting report sections for the radiologist to review. Any model that informs a diagnosis or treatment decision may be regulated as a medical device in many markets, so we help you decide early whether a feature is administrative, decision support or regulated, and design the documentation and evaluation that each category needs. The clinician always makes the call.

Patient data is handled under the principles behind HIPAA in the US and GDPR in Europe: minimum necessary access, de-identification where possible, encryption in transit and at rest, role-based access, and audit logs of who saw or changed what. Where a model runs matters - in your own cloud tenancy, on premises, or with a vendor that will sign the agreements your regulations require. We design to your compliance requirements; the compliance determination itself stays with you and your advisors.

Before go-live we evaluate models on your own data, not just published benchmarks, and break results down by age, sex, ethnicity, language and site so performance gaps surface before patients are affected. After launch we monitor for drift as populations, coding practices and equipment change, and keep clinicians in the loop with simple ways to flag a wrong output so it feeds the next evaluation round.

Much of a healthcare AI project is integration plumbing. Our developers use AI tools to draft FHIR resource mappings, HL7 message parsers, de-identification scripts, synthetic test patients and evaluation harnesses, then review, test and harden every piece themselves. That leaves more of the schedule for the work that needs human judgement: clinical workflow design with your staff, safety cases and edge-case testing.