AI & Machine Learning × Healthcare & Life Sciences
Production-grade AI agents for hospitals, payers, digital-health operators, and provider-group networks: clinical-summary copilots, prior-authorization triage, revenue-cycle coding, scheduling and intake automation. Built by CORTEX dept leads with HIPAA-bounded data flows, eval frameworks calibrated against clinician judgment, and human-in-the-loop on every clinical-impact decision.
The problem
Health systems and payers come to AI agents with a real ask: free clinicians from documentation drag, reduce prior-auth turnaround, automate the revenue-cycle long tail, and stop losing scheduling staff to phone-based intake. The pattern that fails them is a generic LLM bolted onto an EHR: confident, plausible, and one wrong-summary away from a Joint-Commission finding or a malpractice claim. The clinical-leadership team kills the pilot by month four.
Production healthcare agents need three things that consumer AI doesn't have: a HIPAA-bounded data flow with audited PHI access, an eval framework built from clinician-labeled ground truth (not Mechanical Turk), and a human-in-the-loop pattern that is explicit about which decisions stay clinical. We design those in from week one, with the medical-affairs and compliance functions in the room: not as a phase-2 cleanup after a near-miss.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside healthcare & life sciences delivery constraints.
1.4 hr
saved per clinician per shift
Summarizes encounters, drafts notes, codes orders against the chart. Read-only; clinician edits and signs. Trained against your specialty mix and your EHR's structured fields, not a generic corpus.
62%
PA cycle-time reduction
Reviews PA requests, retrieves clinical evidence, drafts payer-specific submissions. Approval gate before submission; denial appeals drafted with cited evidence. Tools: EHR read, payer portals, custom PA rule packs.
Suggests ICD-10 / CPT / HCC codes from the documented chart. Coder reviews and accepts; bypass requires explicit confirmation. Bounded to coding; doesn't initiate billing actions.
Patient-facing scheduling and intake: insurance verification, appointment booking, pre-visit forms, basic triage routing. Hard escalation to human on any clinical concern; never substitutes clinical judgment.
Outbound follow-up for chronic-care management and post-discharge: medication-adherence check-ins, appointment reminders, symptom screening. Symptom escalations route to a clinician immediately.
Agent for utilization-review and case-management teams: chart-summarization, criteria-checking against MCG / InterQual, draft determinations. Recommendation only; UR clinician retains decision authority.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to healthcare & life sciences delivery.
Three-week discovery: candidate use cases, clinical-impact tier per use case, PHI-access surface, EHR / payer-system inventory, clinical-leadership stakeholder map. Output is a prioritized roadmap with HITL checkpoints designed before build: clinical-leadership signs the scope.
Build the eval set from real (de-identified) chart segments labeled by your senior clinicians: not synthetic data, not Mechanical Turk. Metrics: clinical accuracy, hallucination rate, refusal rate on out-of-scope queries, clinician-acceptance rate. Agent ships against the eval; no eval, no production.
PHI access logged at the per-call level. Tool-use whitelist with approval gates on clinical-impact actions. BAAs with every model and infrastructure provider; data residency configurable. Drift monitoring; alerting wired to compliance and ops.
Clinical-leadership review cadence (weekly first 90 days, monthly thereafter). Quality-and-safety committee briefings. Incident-response runbook for agent-related near-misses. The agent earns its keep through clinician trust: we build for that, not just for a launch demo.
Capabilities
Stack
Compliance overlay
Every healthcare & life sciences engagement carries the evidence collection that procurement and audit teams expect on day one.
BAAs in place with every model and infrastructure layer. PHI access logged at the per-call level. Minimum-necessary access enforced via tool-use whitelisting. De-identification flows tested for any data leaving the covered-entity boundary. Breach-response runbook integrated with your compliance team's existing process.
Clinical-impact agents are documented with the same rigor as a clinical-decision-support tool: development methodology, evaluation, ongoing monitoring, and clinical oversight. Joint-Commission readiness materials provided. CMS QPP / value-based-care metrics impact tracked where applicable.
Every clinical-impact use case has explicit HITL: the agent recommends, a clinician decides. Adverse-event reporting integrated with your existing safety committee. Bias-and-disparity testing on the eval set; subpopulation performance reported to clinical leadership monthly.
Selected work
Where this fits
Common questions
Yes, when scoped correctly. The framing that works: BAA with the model provider (Anthropic, OpenAI via Azure, in-VPC self-hosted Llama all qualify), per-call PHI access logging, minimum-necessary tool-use boundary, and de-identification on any data leaving the covered-entity boundary. We've stood up HIPAA-defensible deployments at multiple covered entities; the architecture is well understood.
Three guardrails. (1) Retrieval-grounded: the agent answers from the chart, your protocols, and your formulary: not open generation. (2) Refusal-first: out-of-scope clinical queries escalate to a clinician; the agent doesn't guess. (3) Eval-checked against clinician-labeled ground truth: accuracy and hallucination rubrics catch regressions before they ship. Hallucination is a design problem, not an inherent property of LLMs.
Adoption is the hard problem, not the technology. The pattern that works: scope to a real clinician pain (documentation drag, PA paperwork, after-hours messaging) before launch; co-design with the specialty leads who will use it; iterate weekly against clinician feedback for the first 90 days; publish clinician-acceptance rate and drift metrics monthly. Where we follow this pattern, sustained adoption hits 80–90%. Where the buyer skips it, agents ship and quietly die.
Most of the use cases on this page are explicitly out of FDA SaMD scope: they are administrative, documentation, or clinician-decision-support without diagnostic claims. Where a use case might cross into SaMD (e.g., autonomous diagnostic suggestion), we surface that early in discovery and either re-scope or wire in the regulatory pathway. We don't ship into SaMD scope by accident.
Documentation copilot: 1.0–1.6 hours saved per clinician per shift, with clinician burnout scores improving in months six onward. Prior-auth triage: 50–70% cycle-time reduction; denial-appeal recovery rate up 30–50%. RCM coding: 25–40% coder-throughput improvement on the long-tail mix. Scheduling automation: 60–80% deflection of routine intake. Economics work above ~150 clinicians or ~25,000 monthly PA volume.
Discovery + eval-set build: 4–6 weeks, $90K–$180K. Production agent build (single use case): 6–9 months, $500K–$1.2M. Multi-agent estate (documentation + PA + scheduling): 10–16 months, $1.5M–$3.5M. Managed services post-launch: $40K–$140K monthly retainer covering eval iteration, drift monitoring, clinical-leadership reporting, and model updates.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior healthcare & life sciences delivery experience.