Population Health Trend Mining from Anonymized Records

Added Nov 2025 3 design docs

Public health teams and hospital networks need to see the big picture in their records: which conditions are rising, how utilization shifts by season, and where demographic groups diverge in outcomes. Extracting those trends from large volumes of anonymized records is a classic distributed data problem, and this project walks an intern through solving it properly. Anonymized health records are ingested as a stream through Kafka, mirroring how events flow continuously from clinical systems, and stored in HDFS for scalable retention. PySpark batch jobs then process the accumulated data to extract trends, computing condition frequencies over time, admission patterns, and cohort-level summaries. Python scripts orchestrate the pipeline, manage the processing runs, and export CSV summaries suitable for epidemiologists and administrators. The deliverables are the pipeline and its data artifacts; there is no user interface, which keeps the focus on correctness, scale, and reproducibility. The intern finishes with practical big-data skills applied to healthcare: streaming ingestion, distributed storage and computation, and the habit of treating sensitive-domain data with the care that anonymization and aggregation demand.

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