HealthPY Applied — Standalone

## The scenario You have been promoted onto the **CubedNet Health analytics team** and asked to investigate why one of the three trusts (the South East acute) consistently runs ~14% above tariff on day-case ophthalmology spells over a rolling 12-month window. Finance wants to know whether this is a coding drift problem, a casemix problem, or a process problem before they raise it at the next contract review. You receive twelve monthly SUS+ extracts, the rolling tariff history (some HRGs were re-priced mid-year), a provider reference table, and an audit-flag log from the Core report's outputs. ## Deliverables A pandas-based analysis (`healthpy_applied.py` orchestrating notebooks or scripts) that produces: 1. `monthly_variance.csv` — per-provider, per-HRG-chapter monthly spend vs expected-on-current-tariff, with rolling 3-month and 12-month deltas. 2. `casemix_drift.csv` — month-on-month HRG distribution shift per provider (Wasserstein distance or KL-divergence is fine; justify your choice). 3. `coding_anomaly_flags.csv` — spells where the HRG looks inconsistent with the diagnosis/procedure pair (build a simple rule library or a frequency-based outlier rule). 4. `findings.md` — a 400-600 word note your manager could forward to Finance: where the variance is coming from, what's noise vs signal, and one recommended next step. ## Acceptance criteria (summary) pandas/numpy allowed · charts saved to `./output/figures/` · findings.md present and substantive · rolling windows computed correctly · tariff history respected (use the rate in force on the discharge date, not a single snapshot) · reproducible: `python healthpy_applied.py` regenerates everything · ≥10 conventional commits. Full brief, dataset orientation, and starter notebook appear inside the lesson once enrolled.