Healthcare IT Consulting
Clinical data systems, interoperability, HIE, EHR data, HL7/FHIR, SQL/ETL, and AI-ready healthcare architecture.
I am useful where healthcare software claims, clinical workflows, messy data, vendors, and implementation reality need to be examined carefully. I help turn healthcare IT plans into inspectable systems: data models, interfaces, workflows, reports, governance, and implementation reality.
What I Help With
Healthcare Data Architecture
Data modeling, relational and dimensional design, data dictionaries, quality frameworks, and semantic mapping for clinical and operational datasets.
HL7/FHIR and Interoperability
HL7 v2, FHIR R4/R5, integration architecture, clinical data exchange, identity, consent, provenance, and terminology mapping across systems.
HIE / EHR / EMR Implementation Support
Health information exchange design, EHR/EMR data integration, migration planning, and workflow-aligned implementation guidance.
Clinical Data Migration and Normalization
Legacy data migration, cross-system reconciliation, cleaning, validation, and normalization of inconsistent real-world clinical data.
SQL Server / ETL / Reporting
SQL Server data warehousing, SSIS ETL pipelines, reporting databases, and multi-source data integration for healthcare analytics.
Terminology Mapping
ICD-9/10/11, CPT, LOINC, SNOMED CT, RxNorm, ATC, and CDISC mapping across clinical, research, and operational contexts.
Healthcare AI Data-Readiness
Data substrate evaluation, workflow mapping, structured knowledge representation, and risk-aware implementation planning before AI deployment.
Vendor Oversight and Project Rescue
Reviewing vendor claims, examining implementation reality, identifying what is broken, and helping teams course-correct on stalled healthcare IT projects.
Best-Fit Projects
- • Healthcare interoperability and HL7/FHIR architecture
- • HIE implementation and EHR/EMR data integration
- • Clinical data engineering and migration
- • Healthcare data warehouse and reporting design
- • Clinical trial data systems and validation logic
- • Healthcare AI data-readiness assessment
- • ABDM and FHIR readiness for Indian healthtech
- • Vendor oversight and project rescue
Why My Background Fits Messy Healthcare Data Projects
Healthcare AI, analytics, and digital health projects usually fail before the model, dashboard, or application begins. They fail in the data substrate: identity, terminology, workflows, missing context, legacy systems, broken mappings, inconsistent definitions, and silent semantic loss.
My work sits at that layer. I help healthcare teams understand what their data actually represents, how it moves across systems, what breaks during transformation, and what must be fixed before analytics or AI can be trusted.
I bring a rare combination of computer science, U.S. healthcare data experience, clinical research systems, database architecture, ETL, interoperability thinking, and the practical patience required to work with real healthcare systems rather than imaginary clean-room diagrams.