--- title: "Healthcare AI: clinical summarization, evals, audit" description: "Production AI for hospitals, EHR add-ons, and clinical platforms. Clinical summarization, evidence-grounded copilots, on-prem deployment, and HIPAA-aware audit trails." source: "https://www.kensink.com/industries/healthcare-ai/" canonical: "https://www.kensink.com/industries/healthcare-ai/" --- ★ Industry vertical · Healthcare AI 06 service items HIPAA-aware engagements INDUSTRY · HEALTHCARE AI · CLINICAL + EHR # Clinical AI that survives the audit. Production AI for hospitals, EHR add-ons, and medical platforms. Clinical summarization, evidence-grounded copilots, on-prem deployment, and an eval suite that names accuracy, safety, and PHI handling as first-class metrics next to latency. [Start a project →](https://www.kensink.com/contact) [View case studies →](https://www.kensink.com/cases) Industry Clinical AI · EHR add-ons Compliance HIPAA-aware · on-prem ready Reliability Eval-gated releases Stack Direct LLM · auditable \[WHAT WE HEAR FROM CMIOs AND CTOs\] ## Three pains every clinical-AI team hits. We have watched these patterns in hospital systems, EHR add-on vendors, and clinical-summarization startups. The shape repeats across geographies. The fix is not a more confident demo. The fix is evidence: an eval suite a CMIO can read, an audit trail compliance can defend, and a deployment shape your security team signs off on. PAIN · 01 01 / 03 ### Clinical AI fails the second hospital. Models tuned on one institution's note style, terminology, and workflow break when deployed at the next. Without a stratified eval, nobody notices until a clinician does. ↓ How we fix it, below. PAIN · 02 02 / 03 ### Compliance is a board-level conversation. PHI handling, audit trails, hallucination risk, FDA posture. The clinical leadership needs answers in the same meeting as the budget. Without an audit-grade stack, the answer is always 'we are working on it.' ↓ How we fix it, below. PAIN · 03 03 / 03 ### Architecture today shapes the EHR for a decade. Hospitals do not rebuild stacks every two years. Vector store, model provider, deployment shape, eval cadence — those choices get inherited by the next CIO and the one after that. ↓ How we fix it, below. \[SIX SERVICE ITEMS · ONE TEAM\] ## Pick the clinical-AI problem. We'll bring the build. Eight-week engagements, eval suite at handoff, deployment shape your security team can sign off on. Bundle two when the problem warrants. SERVICE · 01 / 06 Core clinical AI Clinical summarization engine Discharge summaries, progress notes, and visit summaries that doctors trust enough to sign. Evidence-grounded, hallucination-bounded. - Source-cited summarization with per-claim grounding - Specialty-tuned templates: cardiology, oncology, primary care - Configurable read-back: clinician edits feed the next prompt Anthropic PostgreSQL TypeScript OpenTelemetry SERVICE · 02 / 06 Reliability + safety Eval suite for clinical AI Accuracy, safety, and PHI handling as named metrics. Stratified per specialty, per institution, per population. Gated on every release. - Golden set of clinician-validated summaries per specialty - Hallucination + omission + harm scoring - Stratified drift detection per site and per population LangSmith Promptfoo Python ClickHouse SERVICE · 03 / 06 RAG over medical literature Evidence-grounded copilot A clinical copilot that cites guideline source-of-truth, not blog posts. PubMed, UpToDate-style internal libraries, internal protocols. - RAG over guidelines + internal protocols with citation chain - Refusal patterns when evidence is missing or weak - Tooluse for dose calculators, lab interpretations, contraindications pgvector Anthropic TypeScript Postgres SERVICE · 04 / 06 HIPAA-aware On-prem / VPC deployment Run open-weights models inside your VPC or on your hardware. Air-gapped where required, PHI never leaves the boundary. - Self-hosted Llama / Mistral / Qwen with vLLM throughput - Audit trail and access logging at the proxy layer - BAA-ready vendor selection where SaaS is acceptable vLLM Llama Kubernetes OpenTelemetry SERVICE · 05 / 06 What CMIO sees at 3am Production observability + audit Every prompt, every completion, every clinician edit logged with retention controls. Audit trail SOC 2 + HIPAA defensible. - End-to-end traces with PHI redaction on the wire - Cost-per-encounter metering per facility - Eval-as-monitor: drift alerts before clinicians see it OpenTelemetry Grafana Datadog Sentry SERVICE · 06 / 06 Pre-build wedge Architecture review One-week audit of your clinical-AI stack with written decisions: vector store, model provider, deployment shape, eval cadence, audit posture. - Vector store: Postgres + pgvector or vendor SaaS - Model selection scored on accuracy, latency, BAA, exit cost - Eval cadence: what to ship first, what to defer to v2 ADRs Postgres vLLM Cloudflare Most engagements bundle two: a clinical build (01, 03) paired with the discipline that keeps it auditable (02, 05). Bring the shape closest to your blocker. [Scope your engagement →](https://www.kensink.com/contact) Want to see the K-Framework discipline behind every item? [Read the K-Framework](https://www.kensink.com/k-framework). \[THE STACK · BY LAYER\] ## Audit-grade infrastructure. Clinical results. Boring tools that hospital security teams have already approved. Self-host where required, BAA-backed SaaS where acceptable. LAYER · DATA + RETRIEVAL ### Data + retrieval. The store, the index, the search PostgreSQL pgvector Redis ClickHouse BigQuery OpenSearch LAYER · MODEL LAYER ### Model layer. Embeddings, providers, fallbacks OpenAI Anthropic Cohere Embed Voyage Llama (self-hosted) vLLM LAYER · EVAL + OBSERVABILITY ### Eval + observability. The eval bar, the cost meter, the drift alarm LangSmith Promptfoo OpenTelemetry Datadog Grafana Sentry LAYER · BACKEND + TRANSPORT ### Backend + transport. Type-safe everything TypeScript Next.js Python FastAPI gRPC BullMQ tRPC Zod LAYER · MOBILE ### Mobile. iOS + Android, native or cross React Native Expo Swift Kotlin FCM APNs LAYER · CLOUD + DEPLOYMENT ### Cloud + deployment. Whatever your infra already runs Cloudflare Workers Cloudflare R2 AWS GCP Vercel Fly ✕ WHAT WE DO NOT SHIP ### Direct against the model API. Self-host where compliance requires it. - ✕ No LangChain - ✕ No LlamaIndex - ✕ No agent framework - ✕ No orchestration vendor - ✕ No black-box ML platform \[ PROOF · WHAT THE STACK DELIVERS \] ## Numbers that survive a compliance review. MEASURED · WEIGHTED · 2024–2026 VOLUME 100k + Clinical notes processed in eval sets SAFETY 0 PHI leaks across audited engagements LATENCY p95 / 1.2 s Summarization round-trip target COMPLIANCE HIPAA BAA-ready vendor selection by default HEALTHCARE AI · APPLIED K-FRAMEWORK ## Bring the clinical problem. We'll bring the audit trail. Eight weeks, fixed scope, eval suite + audit log at handoff. Direct LLM engineering on top of the K-Framework. Two Q3 slots remain. [Start a project →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework) CYCLE 8 weeks · problem to live OUTPUT Code · evals · audit trail DEPLOYMENT On-prem or BAA-backed