AI & Machine Learning × Healthcare & Life Sciences
Production-grade generative AI, RAG, and medical imaging, engineered for HIPAA-aligned clinical environments with citation tracking, refusal patterns, and audit logs that pass payer and hospital security reviews on day one.
The reality
The pattern repeats across every health system we visit: a slick demo, a clinician champion, an enthusiastic pilot, and then a stall. Sometimes for six months. Sometimes forever. The reason is rarely the model. It's the absence of the things hospital security, compliance, and clinical leadership actually need: a HIPAA-eligible deployment topology, a documented PHI flow, citation tracking that survives an audit, refusal patterns that prevent confident-but-wrong clinical advice, and the audit logging granularity that turns a regulator inquiry into a one-day evidence pull.
Prosigns ships clinical AI that survives those reviews. We design ePHI boundaries before model selection. We build evaluation harnesses against ground-truth datasets curated with clinical SMEs. We instrument every interaction with citation lineage, model version, and user identity. We co-pilot with regulatory and IT security from kick-off, so the production deployment passes the SOC 2, HITRUST, and state-level reviews that a demo never has to.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside healthcare & life sciences delivery constraints.
$4.2M
annual labor savings
Retrieval-augmented answering over guidelines, formularies, and patient context. Citation tracking on every response, refusal for out-of-scope queries, and clinician-in-the-loop checkpoints for high-impact actions.
94%
sensitivity at threshold
Detection, segmentation, and triage models for radiology, pathology, and ophthalmology. DICOM-native pipelines, FDA SaMD-aware design, and on-prem or edge deployment options.
−42%
documentation time
Ambient and post-encounter clinical documentation, ICD/CPT coding suggestions, and discharge summaries: generated with structured templates, source-traceable to the encounter, and human-finalized.
−47%
auth turnaround
Multi-agent workflows that read payer policies, extract clinical evidence from the chart, draft authorization narratives, and route to clinician sign-off. Full audit trail, no autonomous submission.
+38%
intake completion
Conversational intake, symptom triage, and care-navigation copilots: with grounded medical content, refusal patterns for emergency-care language, and explicit escalation to a human.
+31%
high-risk recall
Predictive models for readmission, deterioration, no-show, and chronic-disease progression: with calibrated probabilities, drift monitoring, and equity-aware evaluation across demographic subgroups.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to healthcare & life sciences delivery.
Map every PHI element through the system before any model is selected. Define minimum-necessary at the prompt boundary, decide where ePHI may and may not flow, and produce a data-flow diagram that becomes the architectural and security-review spine.
Select BAA-covered model endpoints (AWS Bedrock under HIPAA-eligible accounts, Azure OpenAI under BAA), or self-host (vLLM, TGI) on customer infrastructure when sovereignty requires it. Encryption boundaries, key management, and access logging settled before code is written.
Ground-truth datasets curated with clinicians. Faithfulness scoring, refusal correctness, latency budgets, and equity-aware subgroup evaluation. Every prompt change runs against the eval set in CI before it can ship.
Every interaction logged with input, output, retrieved citations, model version, and user identity. Continuous drift monitoring against the eval dataset. Quarterly model upgrades gated on A/B harness results, not a vendor announcement.
Capabilities
Stack
Compliance overlay
Every healthcare & life sciences engagement carries the evidence collection that procurement and audit teams expect on day one.
Administrative, physical, and technical safeguards mapped to architectural components. BAA-covered subprocessors only. Documented risk analysis, incident response, workforce training, and access reviews. Minimum-necessary enforced at the prompt and retrieval boundary.
Continuous evidence collection: control activity logs, change-management artifacts, access reviews, and vendor risk management. Produced as a side-effect of operating the system, not assembled at audit time.
FHIR-native data shapes for retrieval and write-back. SMART-on-FHIR launches into Epic, Cerner, and Athena. HL7 v2 and CCDA gateways for legacy interfaces. Consent-aware data flows that respect 42 CFR Part 2, state confidentiality laws, and patient consent directives.
For diagnostic or treatment-decision-support workloads classified as Software as a Medical Device, we co-pilot with regulatory affairs: IEC 62304 lifecycle, traceability matrices, predetermined change-control plans, and validation evidence packages aligned to 510(k) or De Novo submissions.
Electronic record and electronic signature controls for eClinical workloads: audit trails, system access controls, role-based authority, and time-stamped record integrity. Validation documentation calibrated to the customer's GxP framework.
Selected work
$4.2M
annual labor savingsFHIR-aligned retrieval over 12M clinical documents. SOC 2-aligned audit logs, ePHI encryption, citation tracking on every answer, and refusal patterns for out-of-scope queries.
11 months
94%
sensitivity at thresholdDICOM-native pipeline with on-prem inference, FDA SaMD-aligned validation, and a clinician-in-the-loop review queue. Average study triage time fell from 6 minutes to 38 seconds at parity radiologist agreement.
9 months
Where this fits
Common questions
ePHI flows only through BAA-covered components. We use HIPAA-eligible model endpoints (AWS Bedrock, Azure OpenAI under BAA, or self-hosted vLLM/TGI) and BAA-covered vector stores. Prompts are constructed with minimum-necessary in mind: structured redaction of identifiers where the workload allows it, and explicit data-flow boundaries so that ePHI never crosses into a non-covered path. Every prompt and retrieval call is logged with user identity, model version, and retrieved citation IDs for audit defensibility.
Three layers. First, retrieval grounding: every clinical answer cites its source documents, and ungrounded outputs are blocked or flagged for review. Second, evaluation: clinicians curate ground-truth datasets, and we run faithfulness scoring, refusal correctness, and equity-aware subgroup evaluation on every prompt change in CI. Third, refusal patterns. We explicitly engineer for 'I don't know' as an acceptable answer in clinical contexts where confident-but-wrong is dangerous. Production responses include a clinician-in-the-loop checkpoint for any high-impact recommendation.
Yes: for diagnostic or treatment-decision-support workloads classified as Software as a Medical Device, we co-pilot with the customer's regulatory affairs team. We follow the IEC 62304 software lifecycle, build the traceability matrix, draft predetermined change-control plans, and produce the validation evidence package. We deliver engineering and validation artifacts; the customer typically owns the IFU, labeling, and submission. Common collaborations: 510(k), De Novo, and post-market change control.
Cloud is the default for most engagements: BAA-covered Bedrock, Azure OpenAI, or Vertex AI deliver frontier-model quality with HIPAA-eligible compliance and dramatically lower operating burden than self-hosting. Self-hosted (vLLM, TGI on customer GPU infrastructure) is the right call when data-sovereignty rules are non-negotiable: government health agencies, academic medical centers with on-prem mandates, or specific overseas jurisdictions. Self-hosted means you own the GPU bill and the on-call rotation; we'll model both options honestly during discovery.
Equity-aware evaluation is built into the eval harness, not bolted on later. Ground-truth datasets are stratified across the demographic subgroups relevant to the deployment context (race, ethnicity, sex, age, payer mix, language preference). Faithfulness, recall, and refusal correctness are reported per-subgroup, not as a single aggregate number. When subgroup gaps appear, they're surfaced to clinical and product leadership rather than averaged away: fixing them is a release gate, not a backlog item.
Standard pattern: SMART-on-FHIR launch into the EHR, FHIR R4 read for context, and structured write-back through the customer-approved interface (FHIR write, HL7 messaging, or EHR-native API). For Epic specifically, we ship App Orchard-listable applications with Hyperspace context awareness and EHR-tethered authentication. We don't deploy unmonitored screen-scrapers; if the EHR doesn't expose what's needed, we work with the customer's interoperability team to build a sanctioned interface.
Discovery and privacy threat modeling: 4–6 weeks, $50K–$150K. Production build for a single high-leverage use case: 4–9 months, $400K–$1.5M, with a senior engineer plus the CORTEX (AI/ML) lead and a CITADEL (compliance) co-pilot on the engagement. Multi-use-case enterprise deployments (with MLOps, governance, and managed services): $1.5M–$5M+ over multiple quarters. We publish budget brackets honestly so visitors self-qualify before the first call.
Yes: through our Managed Services engagement model. We maintain the eval dataset with clinical SMEs, run continuous faithfulness and equity scoring on production traffic, monitor drift, and evaluate new model releases on a quarterly cadence. When a better model ships, we run an A/B harness against the eval set and a production traffic shadow, and present go/no-go data to clinical and IT stakeholders. Model selection is a recurring decision; we operate the cadence so customers don't have to.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior healthcare & life sciences delivery experience.