Years of managing labor and delivery triage cultivated an instinct for separating critical signals from clinical noise. That diagnostic focus now governs data pipeline hygiene and relational query architecture.
Pairing patient care experience with modern Python and statistical modeling turns raw electronic health record extracts into reliable clinical decision metrics.
Medical Precision Meets Data Analytics
Disciplined methodologies connecting bedside clinical understanding to scalable quantitative infrastructure.
Clinical Informatics
Data Pipeline Modeling
Outcome Analytics
Translating nuanced clinical documentation, terminology standards, and bedside EHR workflows into clean, normalized data schemas.
Designing auditable SQL queries and Python transformation workflows with rigorous data validation protocols mirroring diagnostic patient safety standards.
Constructing longitudinal patient cohort models and hospital operational indicators to track quality metrics and identify clinical process bottlenecks.
Cultivated Projects and Case Studies
Maternal Triage Risk Pipeline
A reproducible statistical model identifying high-risk labor admission indicators, decreasing triage documentation latency and standardizing clinical assessment protocols.
Inpatient Length of Stay Model
Longitudinal analysis of postoperative recovery durations across hospital units, identifying operational discharge bottlenecks through survival models and structured query workflows.
Available for data analyst and healthcare informatics roles with collaborative teams.