AI for Healthcare

Healthcare AI systems must satisfy stringent requirements for data privacy, clinical safety, and regulatory compliance. MicrocosmWorks can build healthcare AI on HIPAA-compliant infrastructure with defense-in-depth security, designing every system with the FDA's SaMD framework in mind -- even when initial deployment does not require regulatory clearance. Our architectures support federated learning for multi-site model development without centralizing protected health information.

• HIPAA & PHI Protection: Every system is built on HIPAA-compliant infrastructure with BAAs in place for all service providers. PHI is encrypted at rest (AES-256) and in transit (TLS 1.3), access is controlled through role-based policies with minimum necessary access principles, and comprehensive audit logs track every data access event. De-identification pipelines using both Safe Harbor and Expert Determination methods are available for research and analytics use cases.
• FDA Software as a Medical Device (SaMD): For AI systems that meet the FDA's definition of SaMD, MicrocosmWorks follows the predetermined change control plan framework, maintains quality management systems aligned with 21 CFR Part 820, and supports clients through the 510(k) or De Novo submission process. We design systems with locked vs. adaptive algorithm architectures appropriate to the regulatory pathway.
• Clinical Safety & Bias: All clinical AI models undergo rigorous validation for performance across demographic subgroups (age, sex, race, ethnicity) to detect and mitigate algorithmic bias. Human-in-the-loop design ensures that AI augments rather than replaces clinical judgment, and fail-safe mechanisms ensure graceful degradation when model confidence is low.

Consider a typical engagement scenario: A multi-hospital health system partners with MicrocosmWorks to address clinician documentation burden and improve coding accuracy across their enterprise. Physicians spend an average of 2.3 hours per day on documentation, and their first-pass ICD-10 coding accuracy is 74%, requiring extensive CDI (clinical documentation improvement) specialist review. MW deploys a clinical NLP platform that extracts structured data from physician notes, generates automated coding suggestions, and provides ambient documentation assistance.

Projected outcomes:

• Projected 62% reduction in clinician documentation time (from 2.3 hours to 52 minutes daily)
• First-pass ICD-10 coding accuracy improved to 94.8%
• CDI specialist review volume reduced by 55%, enabling redeployment to complex cases
• $4.8M in projected annualized revenue improvement from more accurate and complete coding
• Clinician satisfaction scores for EHR usability improved by 40 points

The platform can then be expanded to support radiology report generation and discharge summary automation.

• Healthcare-specialized AI engineering: Our team includes engineers with deep domain expertise in clinical informatics, medical imaging, and health data standards (HL7 FHIR, OMOP, DICOM). We speak the language of healthcare and understand the clinical workflows our systems must support.
• Regulatory navigation expertise: Our team brings expertise in navigating the FDA SaMD regulatory landscape and building quality management systems that satisfy both FDA and HIPAA requirements. We understand the difference between building a demo and building a deployable medical AI product.
• Privacy-preserving AI at scale: Our federated learning and de-identification capabilities enable clients to develop powerful AI models without compromising patient privacy -- unlocking multi-site collaboration and research that was previously impractical.
• Interoperability-first architecture: Every system we build is designed for seamless EHR integration using HL7 FHIR and standard healthcare APIs, ensuring adoption within existing clinical workflows rather than creating parallel systems that clinicians will not use.