Healthcare Data & Analytics

For dashboards, I build custom solutions using React with charting libraries like Recharts, D3.js, and Nivo for healthcare-specific visualizations. For data pipelines, I use Node.js and Python for ETL processes, PostgreSQL or Snowflake for the data warehouse, and dbt for transformation logic. For organizations that prefer BI platforms, I also work with Metabase and Apache Superset. The choice depends on your team capabilities and specific requirements.

Clinical data comes coded in various terminologies, including ICD-10, SNOMED CT, LOINC, CPT, and RxNorm, and different source systems may use local codes or older versions. I build terminology mapping layers that normalize codes to a common standard, using the UMLS Metathesaurus and custom mapping tables. This ensures that analytics queries produce accurate results regardless of which source system contributed the data.

Yes. I build population health platforms that aggregate patient data across care settings to identify high-risk patients, track care gaps, measure quality metrics at the population level, and support risk stratification models. These platforms combine EHR data, claims data, and social determinants of health information to provide a comprehensive view of patient populations and enable targeted intervention programs.

I implement data quality frameworks that validate data at every stage of the pipeline. This includes completeness checks (are expected data elements present), conformance checks (do values match expected formats and ranges), consistency checks (do related data elements agree), and timeliness checks (is data arriving within expected latency). Data quality metrics are tracked on dashboards so your team can monitor and address quality issues proactively.

Analytics platforms handling PHI require the same HIPAA safeguards as any other healthcare system. I implement access controls that limit data visibility based on user roles and organizational affiliation, audit logging for all data access, encryption at rest and in transit, and de-identification capabilities for research and external reporting use cases. For analytics that do not require patient-level detail, I implement aggregate-only views that avoid exposing individual patient records.