HIPAA-conscious AI engineering for hospitals, health systems, payers, life sciences, and pharma. Production models built for the bar clinical work requires, with the audit trail to prove it.
A misclassified movie recommendation costs a click. A misclassified patient costs something you can't get back. Healthcare is where the gap between an impressive demo and a production system is widest, and where being wrong is hardest to walk back.
Our work in clinical and life-science settings includes peer-reviewed deep-learning research on early-disease detection. That research goes into every engagement, not as a credential, but as a working baseline for the patterns that actually transfer to clinical data.
The practice covers clinical decision support, patient-outcome modelling, claims and document understanding, retrieval-augmented LLMs over clinical guidelines, and operational AI for staffing, scheduling, and revenue cycle. Every system ships with an audit trail and a clinician-friendly explanation layer.
A representative slice of the work we do across providers, payers, and life sciences.
Sepsis prediction, deterioration risk, readmission scoring, behavioural-health triage. Deep-learning classifiers built on EHR data and validated against clinician-labelled outcomes, with thresholds tuned to the false-positive cost the clinical team actually accepts.
Layout-aware extraction over discharge summaries, prior-auth packets, and claim PDFs, ICD-10 / CPT codes, dosages, named entities, signatures. Pipelines that combine traditional NLP with LLMs only where the marginal value is real.
Retrieval-augmented assistants over your formulary, guidelines, protocols, and internal policies. Citations on every output, prompt-injection defences, evaluation harness with clinician-graded ground truth, and full audit trail of prompt → retrieved context → output.
Cohort discovery, propensity matching, survival analysis, and synthetic-control studies for life sciences and population health. Reproducible, pre-registered analysis plans, no p-hacking, calibrated uncertainty on every effect estimate.
Census forecasting, OR utilisation, no-show prediction, denial-likelihood scoring, and prior-auth turnaround optimisation. Practical operations AI that reduces avoidable cost and gives leadership real visibility, without touching the clinical pathway.
Image classification, segmentation, and quality-control models for radiology, pathology, and dermatology workflows. Always human-in-the-loop for clinical use; model output framed as decision support, not autonomous diagnosis.
Healthcare AI fails for predictable reasons: PHI handling that doesn't survive a security review, models that hallucinate clinical claims, eval sets that don't reflect the deployed population, no clinician feedback loop. These six rules keep us out of those traps.
We design our engagements to operate inside HIPAA-compliant environments and we sign a Business Associate Agreement before any PHI is shared. Our engineering practices (encryption in transit and at rest, audit logging, minimum-necessary access, breach notification procedures) align with the HIPAA Security Rule.
Yes, on every healthcare engagement before kickoff. The BAA covers our team, any subcontractors, and any cloud services we use on your behalf.
We work with your clinicians to define ground truth, hold out validation cohorts that mirror your patient population, threshold-tune for the clinical tolerance (sensitivity vs specificity), and track calibration over time. Models don't ship to clinicians until they pass the validation criteria you set.
Both. For research and modelling, de-identified data is preferred wherever possible. For deployed systems that operate on live patient data, we sign a BAA and work with PHI inside your environment under HIPAA-compliant controls.
Yes. Our peer-reviewed work in early-disease detection has been validated against expert diagnoses, and our healthcare engagements have shipped clinical decision support, document understanding, and operational AI into hospital and payer environments. Every system ships with an audit trail for clinician review.
Tell us the clinical or operational outcome you're after, the data you can share under BAA, and the regulatory environment you operate in. We'll come back with a one-page architecture and a realistic timeline, or a clear “this isn't an AI problem yet.”