HIPAA-aware AI development for healthcare companies. From clinical documentation to patient intake automation — built with compliance from day one.
Healthcare AI Solutions
AI extraction and structuring turns unstructured clinical notes into structured data. Automatic coding, summarization, and integration with your EHR system.
70% reduction in documentation time
Intelligent intake automation handles patient forms, insurance verification, and eligibility checks. Patients complete intake digitally, data flows directly into your system.
5x faster intake processing
LLM + OCR pipeline liberates data from scanned documents, lab reports, and medical records. Structured extraction with audit trails for compliance.
95%+ extraction accuracy
Services for Healthcare
We build healthcare AI with compliance baked in from architecture to deployment. Not an afterthought — a foundation.
Generic AI vendor lists are easy to find. What's harder is finding a team that has actually shipped HIPAA-compliant production AI inside real US healthcare environments. Here's the use-case shape of healthcare engagements NKKTech has delivered since 2018 — drawn from real client work, presented at the category level because individual project specifics are under BAA.
Ambient AI scribes that listen to a clinician-patient conversation and produce a structured SOAP note ready for EHR ingestion. Integration with major EHR systems (Epic, Cerner/Oracle Health, athenahealth). HIPAA BAA in place with all LLM providers in the data path; audio never leaves the BAA boundary. Outcome pattern: 60-75% reduction in clinician documentation time, 90%+ accuracy on medical coding suggestions.
AI workflow that intakes prior-auth requests, pulls clinical context from EHR and payer policy databases, generates the supporting submission, and predicts approval likelihood before submission. Human-in-the-loop architecture — AI drafts, clinician approves. Outcome pattern: 4-6x throughput per coding/PA staff member, 30-50% reduction in approval cycle time.
Pipeline for processing scanned medical records, lab reports, and faxed referrals — extracting structured data (patient demographics, ICD-10 codes, lab values, medications) with audit trails for every extracted field. Used in revenue cycle management and care coordination workflows. Outcome pattern: 95%+ field accuracy, 30 sec average per document vs 4-8 minutes manual.
Digital intake forms with AI eligibility verification, insurance benefit interpretation in plain language for patients, and automated routing of pre-visit clinical questionnaires. Patient PHI flows through HIPAA-eligible processors only. Outcome pattern: 5x faster intake completion, 40-60% reduction in benefit-related no-shows.
Internal Q&A systems over institutional clinical protocols, drug formulary, and care pathway documentation. Clinicians ask in natural language, the system retrieves the relevant institutional document with citations, the LLM grounds its answer to those sources only. No hallucination on clinical facts — refusal pattern when sources don't cover the question. Outcome pattern: 70-80% reduction in time-to-answer for protocol/policy questions.
Symptom-checker style triage that routes patients to the right level of care (telehealth, in-person, ER) based on natural-language symptom description. Human clinician oversight on edge cases. Designed to meet FDA Software-as-a- Medical-Device (SaMD) classification requirements where applicable. Outcome pattern: 25-40% appropriate-routing improvement vs phone-based triage.
We default to a specific architecture stack for HIPAA-touching AI because not every provider supports the BAA + audit + residency requirements healthcare needs.
Azure OpenAI Service (BAA-eligible) for production PHI-touching workloads; AWS Bedrock with BAA as the secondary option; Anthropic Claude via Bedrock or enterprise tier with BAA. Never the standard OpenAI public API for PHI — no BAA available on standard accounts.
For non-PHI-eligible LLM providers, a privacy gateway strips identifiers upstream (name → PATIENT_X, MRN → REF_001), runs inference on de-identified data, then re-identifies in the secure layer for delivery. The LLM provider never sees actual PHI. Implementation pattern detailed in our AI Compliance Pillar (link below).
Every PHI access logged with timestamp, user identity, patient identifier affected, data accessed, data generated. Tamper-evident storage (S3 object lock, signed log entries). 6-year retention per HIPAA. Logs queryable for breach response and audit support.
FHIR-based integration where possible (Epic, Cerner/Oracle Health, athenahealth all support FHIR R4). Legacy HL7 v2 interfaces where needed. Read-only by default; write access strictly scoped to the workflow that requires it, logged and reviewed.
Deep Dive
For the full technical implementation guide covering HIPAA-specific patterns alongside other frameworks, our 5,000-word pillar guide walks through the architecture decisions and operational requirements engineers actually need.
Read the compliance playbookBook a free 30-minute consultation. We'll assess your healthcare AI opportunity with compliance in mind.