Process medical records with clinical precision and ironclad compliance โ AI that understands healthcare as deeply as your team does.

Healthcare organizations are drowning in medical records โ discharge summaries, lab results, radiology reports, physician notes, operative records, and insurance correspondence โ that arrive in wildly inconsistent formats across fax, EHR exports, patient portals, and scanned paper.
Clinical staff spend hours manually reviewing charts, extracting diagnoses and procedure codes, reconciling records across providers, and preparing summaries for utilization review or legal proceedings. Errors in this process have real consequences: incorrect coding leads to denied claims and lost revenue, missed clinical details compromise patient safety, and HIPAA violations from mishandled records carry severe penalties. Existing automation tools lack the clinical understanding to parse nuanced medical language, and general-purpose AI raises serious compliance and data security concerns.
๋ค์ ํ๋ก์ ํธ๋ฅผ ์ํ ๋ ๋ง์ ๊ตฌํ ์ฒญ์ฌ์ง์ ๋ฐ๊ฒฌํ์ธ์
MicrocosmWorks๋ ์ข ๋จ ๊ฐ ์ํธํ, BAA๊ฐ ์ ์ฉ๋๋ ํด๋ผ์ฐ๋ ์ธํ๋ผ(AWS GovCloud ๋๋ Azure Healthcare APIs), ๊ทธ๋ฆฌ๊ณ ์น์ธ๋ ์ง์์๊ฒ๋ง PHI ๊ฐ์์ฑ์ ์ ํํ๋ ์ญํ ๊ธฐ๋ฐ ์ ๊ทผ ์ ์ด๋ฅผ ํตํด HIPAA ์ค์ ์๋ฃ ๊ธฐ๋ก ์ด์์คํดํธ๋ฅผ ๊ตฌ์ถํฉ๋๋ค. ๋ชจ๋ AI ์ฒ๋ฆฌ๋ HIPAA ์ค์ ๊ฒฝ๊ณ ๋ด์์ ์ด๋ฃจ์ด์ง๋ฉฐ, ํ์ ๋ฐ์ดํฐ๋ ์ธ๋ถ LLM API๋ก ์ ์ก๋์ง ์์ต๋๋ค. ๋น์ฌ๋ ๊ทํ์ ๋ณด์ ๊ฒฝ๊ณ ๋ด์ ์ ์ฉ ๋ชจ๋ธ ์ธ์คํด์ค๋ฅผ ๋ฐฐํฌํฉ๋๋ค. ์ด ์์คํ ์ ๋ชจ๋ PHI ์ ๊ทผ ์ด๋ฒคํธ์ ๋ํ ํฌ๊ด์ ์ธ ๊ฐ์ฌ ๋ก๊ทธ๋ฅผ ์ ์งํ์ฌ HIPAA ๋ณด์ ๊ท์น์ ๊ธฐ์ ์ ์์ ์ฅ์น ์๊ตฌ ์ฌํญ์ ์ถฉ์กฑํฉ๋๋ค.
MicrocosmWorks๋ ์ํ ์ฉ์ด, ICD-10 ์ฝ๋ ๋ฐ SNOMED CT ์จํจ๋ก์ง๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ํ๋ จ๋ ์์ NLP ๋ชจ๋ธ์ ๋ฐฐํฌํ์ฌ ์์ ํ์ ์์ฌ ๊ธฐ๋ก์์ ์ง๋จ, ์ฝ๋ฌผ, ์์ ๋ฐ ๊ฒ์ฌ ์์น๋ฅผ 90% ์ด์์ ์ ํ๋๋ก ์ถ์ถํฉ๋๋ค. ์ด ์์คํ ์ ์ํ ์ฝ์ด, ๋งฅ๋ฝ์ ๋ถ์ (์: '๊ฐ์ผ ์งํ ์์'), ๊ทธ๋ฆฌ๊ณ ์ฆ์๊ณผ ์น๋ฃ ๊ฐ์ ์๊ฐ์ ๊ด๊ณ๋ฅผ ์ฒ๋ฆฌํฉ๋๋ค. ์ถ์ถ๋ ๋ฐ์ดํฐ๋ FHIR ์ค์ ๋ฆฌ์์ค์ ๋งคํ๋์ด EHR ์์คํ ๊ณผ ์ํํ๊ฒ ํตํฉ๋ฉ๋๋ค.
MicrocosmWorks๋ ์ง๋ฃ ๊ธฐ๋ก, ์๋ฃ ์ ๊ณต์ ๋ฐ ์์ค ์ ๋ฐ์ ํ์ ๋ฐ์ดํฐ๋ฅผ ๊ต์ฐจ ์ฐธ์กฐํ์ฌ ์์ถฉํ๋ ์ฝ๋ฌผ ๋ชฉ๋ก, ์ผ์นํ์ง ์๋ ์๋ ๋ฅด๊ธฐ ๊ธฐ๋ก ๋๋ ์์ดํ ์ง๋จ๊ณผ ๊ฐ์ ๋ชจ์์ ํ์ํ๋ ์์ ์กฐ์ ์์ง์ ๊ตฌํํฉ๋๋ค. ์ด ์์คํ ์ ๊ฐ ๋ฐ์ดํฐ ํฌ์ธํธ์ ์ถ์ฒ๋ฅผ ๋ณด์ฌ์ฃผ๋ ๋น๊ต ์ ๋ณด์ ํจ๊ป ๋ถ์ผ์น๋ฅผ ์์ ์ง์์๊ฒ ์ ์ํฉ๋๋ค. ์ด๋ฌํ ์ฌ์ ์๋ฐฉ์ ์กฐ์ ์ ํฌ์ฝ ์ค๋ฅ๋ฅผ ๋ฐฉ์งํ๊ณ ์ง๋ฃ ์ ํ ์ ๋ฐ์ ๊ฑธ์ณ ์ข ๋จ์ ํ์ ๊ธฐ๋ก์ ์ ํ์ฑ์ ๋ณด์ฅํ๋ ๋ฐ ๋์์ด ๋ฉ๋๋ค.
MicrocosmWorks๋ HL7 FHIR, HL7 v2 ๋ฉ์์ง ๋ฐ CDA ๋ฌธ์ ๊ตํ ํ์ค์ ์ฌ์ฉํ์ฌ Epic (FHIR R4 ๋ฐ ์ฌ์ฉ์ ์ง์ API๋ฅผ ํตํด), Cerner/Oracle Health, Allscripts, athenahealth ๋ฐ eClinicalWorks์ ํตํฉ์ ๊ตฌ์ถํฉ๋๋ค. ์ด ์ด์์คํดํธ๋ EHR์์ ๋ฐ์ดํฐ๋ฅผ ์ฝ๊ณ ๋ค์ ์ธ ์ ์์ด ์๋ํ๋ ์ฐจํธ ์ ๋ฐ์ดํธ, ์ฝ๋ฉ ์ ์ ๋ฐ ์ฌ์ ์น์ธ ๋ฐ์ดํฐ ์ฑ์ฐ๊ธฐ๋ฅผ ๊ฐ๋ฅํ๊ฒ ํฉ๋๋ค. ์ฃผ์ EHR ์์คํ ์ ๋ํ ํตํฉ ๊ฐ๋ฐ์ EHR ๊ณต๊ธ์ ์ฒด์ API ์ฑ์๋์ ๋ฐ๋ผ ์๊ฐ๋น $25-$50์ ์์จ๋ก ์ผ๋ฐ์ ์ผ๋ก 4-8์ฃผ๊ฐ ์์๋ฉ๋๋ค.
MicrocosmWorks๋ CPT, ICD-10-CM/PCS ๋ฐ HCPCS ์ฝ๋ฉ ์ง์นจ์ ๋ฐ๋ผ ์๋ฃ ๊ธฐ๋ก ์ด์์คํดํธ๋ฅผ ํ๋ จํ์ฌ ์์ ๋ฌธ์๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ ์ ํ ์ฝ๋๋ฅผ ์ ์ํ๊ณ , ์์ต ์์ค์ ์ด๋ํ๋ ๋ถ์ถฉ๋ถํ๊ฒ ์ฝ๋ฉ๋ ์ง๋ฃ ๊ธฐ๋ก๊ณผ ๊ฐ์ฌ ๋ ธ์ถ ์ํ์ด ์๋ ๊ณผ๋ํ๊ฒ ์ฝ๋ฉ๋ ์ง๋ฃ ๊ธฐ๋ก์ ํ์ํฉ๋๋ค. ์ด ์์คํ ์ ๋ฌธ์์ ์ฝ๋ฉ ๊ท์น์ ๊ต์ฐจ ์ฐธ์กฐํ์ฌ ๋๋ฝ๋ ํน์ด์ฑ(์: ์ธก๋ฉด์ฑ ๋๋ ์ค์ฆ๋)์ ์๋ณํ๊ณ ์ฒญ๊ตฌ ์ ์ถ ์ ์ ์์์์๊ฒ ์ถ๊ฐ ์ค๋ช ์ ์์ฒญํฉ๋๋ค. ๊ณ ๊ฐ์ ์ผ๋ฐ์ ์ผ๋ก ์ฒซ ๋ถ๊ธฐ ๋ด์ ์ฒญ๊ตฌ ๊ฑฐ๋ถ์จ์ด 10-20% ๊ฐ์ํ๊ณ ์ฝ๋ฉ ์ ํ๋๊ฐ ์ธก์ ๊ฐ๋ฅํ๊ฒ ์ฆ๊ฐํ๋ ๊ฒ์ ํ์ธํฉ๋๋ค.
์ ๋ฌธ๊ฐ ํ์ด ๊ทํ์ ๋น์ฆ๋์ค๋ฅผ ์ํด ์ด ์๋ฃจ์ ์ ๊ตฌ์ถํ๋ ๋ฐฉ๋ฒ์ ๋ํด ๋ฌธ์ํ์ธ์.
์ฐ๋ฝํ๊ธฐMicrocosmWorks can deliver a HIPAA-compliant AI medical records assistant purpose-built for the regulatory and clinical demands of healthcare. The system ingests records from any source and format, applies medical-grade OCR and document layout analysis, and uses clinically-aware LLMs to extract structured data โ diagnoses, procedures, medications, allergies, lab values, and provider information โ with the contextual understanding needed to distinguish a "history of" from an "active" condition. The assistant generates concise clinical summaries, suggests ICD-10 and CPT codes for coder review, flags discrepancies between records, and surfaces critical findings that require immediate attention. Every interaction is governed by a comprehensive security and compliance framework: data is encrypted at rest and in transit, all access is role-based with MFA, every AI inference is logged in an immutable audit trail, and PHI never leaves your approved cloud environment. A human-in-the-loop review interface ensures clinicians retain final authority over all outputs.
The platform deploys within a dedicated, HIPAA-compliant cloud environment with strict network isolation, encryption boundaries, and access controls. Records flow through an ingestion layer into a secure processing pipeline: OCR and normalization, clinical NLP and entity extraction, coding suggestion, summary generation, and quality assurance โ each stage producing structured outputs stored in an encrypted clinical data repository. The inference layer runs within the compliance boundary with no external API calls, using self-hosted models or BAA-covered AI services exclusively. An audit service records every data access, model inference, and user action for regulatory reporting.
| Phase | Duration | Deliverables |
|---|---|---|
| Compliance & Architecture | Weeks 1-3 | HIPAA security assessment, BAA alignment, infrastructure design, data flow mapping |
| Secure Infrastructure | Weeks 3-5 | HIPAA-compliant cloud environment, encryption layers, access controls, audit logging |
| Clinical NLP Pipeline | Weeks 5-8 | OCR integration, medical NLP models, entity extraction, coding suggestion engine |
| Review UI & EHR Integration | Weeks 8-11 | Clinical workbench, HL7/FHIR connectors, EHR bi-directional sync, role-based access |
| Validation & Go-Live | Weeks 11-14 | Clinical accuracy validation, penetration testing, compliance audit, phased deployment |
| Layer | Technologies |
|---|---|
| Backend | Python, FastAPI, Celery, HL7 FHIR R4 |
| AI / ML | Self-hosted Llama 3 (clinical fine-tuned), Azure AI (BAA-covered), MedSpaCy, SciSpaCy, Tesseract OCR |
| Frontend | React, TypeScript, TailwindCSS (clinical review workbench) |
| Database | PostgreSQL (encrypted), Elasticsearch, Azure Blob Storage (encrypted at rest) |
| Infrastructure | Azure (HIPAA/HITRUST), AKS, Key Vault, Azure Monitor, Private Link |
| Metric | Improvement | Detail |
|---|---|---|
| Record Processing Time | -80% | Automated extraction replaces hours of manual chart review per patient encounter |
| Coding Accuracy | 93-96% | Clinically-aware AI suggests codes with supporting evidence, reducing denial rates by 40% |
| Clinician Documentation Time | -50% | AI-generated summaries and pre-populated fields cut documentation burden significantly |
| Compliance Audit Readiness | 100% coverage | Every data access and AI inference logged with immutable audit trail for HIPAA reporting |
| Claims Denial Rate | -40% | Accurate, evidence-backed coding reduces payer rejections and accelerates reimbursement |
AI consistently misinterprets
์์ฒ ๋ช ์ ์ง์์๋ฅผ ๋ช ๋ถ ๋ง์ ๊ณต์ ํ๊ณ ์ผ๊ด๋๋ฉฐ ์ค๋ช ๊ฐ๋ฅํ ๋ฐฉ์์ผ๋ก ํ๊ฐํ๊ณ , ๊ทํ์ ATS์ ์ง์ ํตํฉ๋ฉ๋๋ค.