Services & partnerships
I’m Suvro Ghosh, an independent maker based in Calcutta. I build my own projects and help businesses with software and data work.
I’m looking to work with Gulf-based firms that have clients but need help with the technical work. You handle the client relationship. I help plan and do the work from India.
What I can help with
- Connect software: help systems share data and check that it arrives correctly.
- Organise data: clean, move and check data, and build useful reports.
- Reduce manual work: build tools for repeated tasks and make errors easier to spot.
- Use AI where it helps: start with a small test and check the results before relying on it.
- Work with healthcare data: help with older systems, research data and sharing information between systems.
- Improve basic security: review who can access what and fix agreed settings. This is not a security certification or a specialist security test.
I check that I can do the work before agreeing to it. If another specialist is needed, we agree their work and cost first.
How we start
Start with one small paid project. Agree the work, price, dates and how we will check the result. Part payment upfront.
You manage the client and the sale. I help plan and do the agreed technical work. You speak directly with me, not an account manager.
We can agree a fixed fee for my work or a share of the project margin. Put the costs and payment dates in writing. Any later support or changes are agreed separately.
Tell me what needs doing
What does your client need? What software do they use? What is not working, and when is it needed? A short outline is enough to start.
Do not send patient records, passwords or private client data through the form. We will agree how to handle any needed access or data later.
Background and writing
My earlier work includes U.S. healthcare research, software and data systems. The resume records that history. ClinZen ended in 2016.
The articles below explain how I think about technical problems. They are not client case studies. My interactive projects are labelled to show what they do and do not demonstrate.
Areas of experience
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 and Clinical Trial Systems
Database and application design for health information exchange, protocol-driven data collection, validation and clinical research workflows.
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.
Public analysis
Selected healthcare systems analysis
These are public technical analyses, not client case studies. They show how I reason about interoperability, legacy clinical systems, analytics, and AI-ready healthcare data.
- Interoperability & standards
FHIR and the Clerk With the Clipboard
How FHIR uses web APIs, resources, profiles, and terminology to exchange health data—and why a standard alone cannot fix fragmented care.
Read analysis - Legacy clinical data
How VA Healthcare Data Systems Work: From MUMPS to SQL
How VA clinical data moves from VistA, MUMPS, and FileMan into SQL warehouses—and why the journey is semantic translation, not simple conversion.
Read analysis - Clinical analytics
Confounding Factors
How confounding enters healthcare analytics through workflows, selection, time, missingness, and site differences—and how to design more honest analyses.
Read analysis - AI data readiness
Latent Space in Healthcare Data, From the Beginning
How clinical embeddings represent hidden patterns in healthcare data, where they fail, and why every patient vector needs provenance and an audit path.
Read analysis