I bring 10+ years across healthcare spanning payers, hospitals, health tech, and clinical operations. I translate clinical and operational needs into practical technology solutions through clinical systems analysis, requirements, UAT, digital health implementation, healthcare data, and AI.
I work at the intersection of healthcare, clinical systems, technology, and data—translating real-world clinical and operational needs into solutions people can actually use.
With 10+ years across payers, hospitals, health tech, clinical operations, and analytics, I understand both the healthcare problem and the technology behind the solution.
My experience spans clinical workflow analysis, EHR systems, requirements gathering, UAT, digital health implementation, healthcare analytics, AI/LLM evaluation, and technology-enabled process improvement.
I have worked with Epic, Meditech, Salesforce, SQL, Python, Snowflake, and healthcare AI/ML technologies, collaborating with clinicians, operations, Data Science, IT, and cross-functional teams.
My focus is Health IT, Clinical Systems, Digital Health, technology implementation, and product/project delivery.
Applied projects demonstrating how I use healthcare domain knowledge, clinical data, analytics, AI, and technology to solve real-world healthcare problems.
Built a production-style data engineering pipeline ingesting adverse drug event reports from the openFDA API into an ICH E2B(R3)-aligned MySQL schema. Designed a normalized relational data model capturing drug, reaction, patient, and report entities. Surfaced drug safety signals through a Flask/Plotly interactive dashboard enabling exploration by drug class, reaction type, and report volume over time.
Developed a machine learning pipeline to classify clinically significant drug-drug interactions using the TwoSIDES pharmacovigilance dataset. Engineered features from adverse event co-occurrence patterns and trained logistic regression and random forest classifiers. Evaluated with AUC-ROC, precision-recall curves, and cross-validation with a focus on minimizing false negatives given clinical risk implications.
Built a cardiovascular disease risk prediction model using logistic regression across three public clinical datasets. Performed data harmonization, missing value imputation, and feature selection to produce a unified modeling dataset. Evaluated model calibration and discrimination across demographic subgroups to assess equity implications of risk score deployment in clinical settings.
Built a two-layer PostgreSQL data warehouse on real CMS Medicare claims data, a raw layer where data lands as-is and a clean layer where it's transformed and structured. Wrote a Python pipeline that downloads, cleans, and loads data automatically. Produced ten progressively complex SQL queries covering window functions, CTEs, and PMPM calculations. Final deliverable is a live Looker Studio dashboard with four panels: executive scorecard, PMPM trend, claims by service line, and top high-cost members with date and service line filters.
A fully automated three-stage Prefect pipeline that runs on a schedule — Extract pulls fresh CMS data, Transform applies data quality checks and logs bad records to an error table, Load pushes clean data incrementally into PostgreSQL so only new records are added each run. Includes retry logic for stage failures. A Plotly Dash monitoring dashboard surfaces pipeline run history, records processed per run, error rate over time, and a data quality summary panel.
Cloud-native data integration platform on Snowflake with three schemas — raw, staging, and analytics. A Python ingestion script loads CMS data into the raw schema via the Snowflake connector. SQL transformation scripts promote data through staging to analytics, cleaning, joining, and computing metrics at each layer. Snowflake Tasks automate transformations on a schedule. Three live Tableau Public dashboards serve as the front end: an executive scorecard, a clinical operations dashboard, and a payer analytics dashboard.
Structured analysis of clinical, operational, technology, interoperability, ethics, and digital-health challenges.
Examines whether telemedicine can address access, cost, and continuity-of-care challenges in Jamaica particularly for rural and underserved communities while remaining aligned with health informatics principles.
Investigates how healthcare organizations can improve their cybersecurity posture to reduce the risk and impact of cyberattacks while protecting sensitive patient data and maintaining clinical operations.
Examines whether CDSS tools consistently prioritize patient care over financial interests as they become increasingly commercialized with analysis of IBM Watson for Oncology and Epic's sepsis model as real-world failures.
Healthcare domain expertise supported by hands-on clinical systems, implementation, analytics, data, and AI capabilities.
10+ years across healthcare operations, payers, clinical environments, and health technology—evolving into Health IT, clinical systems, digital health, and technology solutions.
If you're building healthcare technology that needs to work in the real world—from clinical systems and digital health to data and AI—let's connect.