Healthcare GenAI in production. Clinical AI, agentic workflows, and RAG that pass evaluation.
Production-grade clinical AI, agentic workflow automation, and RAG-based clinical knowledge retrieval, built on Azure OpenAI, Microsoft Fabric, and Claude API. Designed for health systems, ACOs, healthtech, and revenue cycle teams that need GenAI in production with the evaluation and governance the use case demands.
Why most healthcare GenAI projects stall before production
The pattern is consistent. A pilot demos well in March, the board green-lights expansion in May, and by November the project is quietly redefined as a research initiative. The reason is almost never the model. It is the absence of a clinical evaluation harness, the absence of drift monitoring, the absence of clinician-in-the-loop workflow integration, and the absence of an operating model that holds the system accountable for the outcome it was supposed to produce.
We start engagements assuming the project has to ship to production with an evaluation framework, a governance posture, and a workflow integration that clinicians actually use. The model is the easy part. The other 80 percent of the work is what we specialize in.
Where GenAI delivers in healthcare today
Four use-case families with measurable ROI and clinical evaluation paths that hold up. Each can be deployed in 4 to 8 months with the right architecture.
Clinical documentation AI
Ambient or post-encounter documentation drafting integrated with EHR. Note summarization, problem-list reconciliation, and structured data extraction. Evaluated on note quality, time savings, and clinician satisfaction. Ships with a feedback loop the clinical team owns.
HCC risk adjustment NLP
Pre-visit unaddressed-HCC surfacing from prior clinical notes. Two-stage extraction with V24 and V28 coverage, confidence scoring, audit-trail design for RADV defensibility. ROI measured against per-attributed-life impact on Medicare Advantage and ACO REACH populations.
Agentic workflows for care and revenue cycle
Care coordination triage, prior authorization drafting, denial appeal preparation, RCM coding suggestions. Narrow, human-in-the-loop, evaluated against task-specific KPIs. Deployed where the agent removes friction without removing clinician judgment.
RAG-based clinical knowledge retrieval
Retrieval over your clinical guidelines, internal protocols, payer policies, and reference documents. Grounded responses with citations, evaluated for hallucination risk, governance over what content can be retrieved by whom. Built for clinical decision support and operations support, not autonomy.
How we deliver
Five phases, evaluation-first. The model selection happens after the gold-standard set, not before.
- 01
Use-case and outcome scoping (2 to 3 weeks)
Define the use case, the workflow integration point, the clinical or operational KPI the system must move, and the false-positive tolerance the workflow can absorb. Output: a scoped engagement with a measurable outcome target your clinical and operational leadership signs off on.
- 02
Gold-standard set and model benchmark (3 to 5 weeks)
Build a labeled evaluation set on your real data with clinical informatics involvement. Benchmark Azure OpenAI, Claude, and open-source candidates on precision, recall, and task-specific quality measures. Decide architecture (RAG vs fine-tune vs prompt engineering). Document the choice with the trade-offs.
- 03
Production build and workflow integration (8 to 14 weeks)
Build the data pipeline, model serving layer, prompt or retrieval pipeline, and workflow integration into the EHR or operational tool. Embed evaluation harness, drift monitoring, and audit logging from day one. Soft launch to a defined pilot population.
- 04
Clinical evaluation and full rollout (4 to 6 weeks)
Monthly precision and recall measurement, clinician feedback loop, threshold tuning. Reconcile pilot performance against the original outcome target. Full rollout once the system holds across populations.
- 05
Ongoing operations and governance
Quarterly retraining cadence, drift response, monthly outcome reconciliation, model risk management updates. Optional managed support if your team is small. Clinical AI governance documentation aligned with HIPAA, payer audit, and any applicable state-level AI rules.
What you get
- Production GenAI workflow live against your real data
- Labeled gold-standard evaluation set, owned by you
- Defensible model selection with precision and recall benchmarks
- PHI-safe architecture in BAA-covered cloud zones
- Clinician-in-the-loop workflow integration with your EHR or ops tool
- Continuous evaluation harness with monthly accuracy reporting
- Drift monitoring and escalation runbook
- Outcome reconciliation against the KPI you committed to
- Clinical AI governance documentation aligned with HIPAA and audit
- Optional managed support and quarterly retraining cadence
When to engage us
Your AI pilots are stuck in pilot
If you have demoed clinical AI internally but cannot get production sign-off, the gap is evaluation, governance, and workflow integration. We close that gap.
You are entering a risk-bearing contract
HCC NLP, care coordination automation, and clinical documentation AI are now table stakes for performance under MA, REACH, and ACCESS. Build them before performance year, not during it.
Your RCM team is drowning in denials and prior auth
Agentic workflows for prior authorization, denial appeal preparation, and coding suggestion produce measurable time savings on tasks that drain operational capacity.
You are a healthtech building AI features
Healthcare buyers will not accept demoware. We help healthtech teams ship clinical AI with evaluation harnesses, governance posture, and audit trails buyers actually accept.
Pitfalls we see in healthcare GenAI projects gone sideways
- Picking the model before the gold-standard set. Every model looks great on a hand-picked demo. Without your own evaluation set you cannot compare honestly.
- Treating clinician feedback as optional. A clinical AI that clinicians do not trust does not get used, and a clinical AI that does not get used does not produce outcomes.
- Skipping drift monitoring. Model accuracy degrades silently as data and clinical practice shift. Without drift monitoring you find out at the worst possible moment.
- Underestimating the data pipeline work. The model is 20 percent of the work. The data pipeline that feeds it cleanly and the workflow that consumes its output are 80 percent.
- Vendor-led architecture. Architecture shaped by a model vendor optimizes for the vendor. Architecture shaped by your use case optimizes for your outcome.
Related reading
HCC risk adjustment automation
The clinical NLP use case with the most defensible ROI in healthcare AI today. Pre-visit surfacing on Azure OpenAI and Claude.
HCC risk adjustment NLP, the practitioner guide
Architecture, model selection, accuracy validation, and clinician workflow integration for production HCC NLP.
Workflow automation
Where agentic workflows fit between clinical AI and pure automation. Prior auth, RCM, and care coordination patterns.
Frequently asked questions
What's the difference between clinical AI and clinical AI in production?
About 18 months. A working demo against curated examples is straightforward. A clinically-evaluated, monitored, governance-aligned production system is significantly harder. The gap is evaluation harnesses, clinical-accuracy validation against gold-standard sets, drift monitoring, escalation workflows, and an operating model that maintains all of the above. Most healthcare AI projects stall in that gap. Our work starts with that gap, not after it.
Are agentic workflows actually ready for healthcare, or is this hype?
Both. Agentic workflows are ready for narrow, well-bounded, human-in-the-loop tasks today. Care coordination triage, prior authorization drafting, denial appeal preparation, RCM coding suggestions, ambient documentation drafting. They are not ready for autonomous clinical decision-making, and we will not deploy them that way. The architecture we build assumes a clinician or coder reviews every consequential output. Within that frame, agentic workflows produce measurable time and quality gains.
What evaluation framework do you use?
Three layers. Pre-deployment: precision and recall on a labeled gold-standard set built specifically against your data, with per-category thresholds. Continuous: held-out set scored monthly, drift detection, and escalation when accuracy degrades. Outcome: tied to the workflow KPI (time saved, denial rate, coding accuracy, clinician satisfaction) measured before and after. The gold-standard set is owned by you and travels with you if we ever part ways.
Azure OpenAI, Claude, or open-source. How do you decide?
Three drivers. Data residency and tenancy (Azure OpenAI for Microsoft tenants with strict residency, Claude API for clients on AWS or with cross-cloud needs, open-source for on-premise inference requirements). Clinical reasoning quality (we benchmark on your tasks, not on public leaderboards). Cost and latency at production volume. We model all three and pick the one that wins on your constraints, not on vendor allegiance.
How do you handle PHI in LLM workflows?
PHI never leaves BAA-covered cloud zones. Azure OpenAI in BAA-covered regions, Claude API under their HIPAA-eligible offering, or open-source models inside your VPC depending on architecture. Prompt and response logging is structured to support audit but tokenized to limit incidental exposure. Data classification controls govern what can pass into model context. The architecture is HIPAA-aligned by design rather than retrofitted.
What kind of ROI do healthcare GenAI projects produce?
Highly task-dependent. Documentation AI on physician workflow can produce 30 to 50 percent time savings on note completion. HCC NLP produces $300 to $900 per attributed life per year on Medicare Advantage and ACO REACH populations. Prior authorization automation reduces touch time per case by 40 to 60 percent. RCM AI on coding suggestion reduces denial rates by 10 to 20 percent. We model expected ROI per use case before kickoff and reconcile it annually.
Let's talk about your value-based care project.
Working on a value-based care contract, ACCESS Model application, EHR integration, or AI-enabled clinical workflow project? Book a 20-minute discovery call or email [email protected].