Drop fact_interventions (440K rows), mv_patient_treatment_summary (35K rows),
ref_drug_snomed_mapping (144K rows), and processed_files — all unused since
the app moved to pre-computed pathway_nodes.
Key changes:
- Rewrite load_data() to source from pathway_nodes + pathway_refresh_log
- Remove 7 dead methods and 8 dead state vars from pathways_app.py
- Delete patient_data.py, load_snomed_mapping.py, test_large_dataset_performance.py
- Remove SQLiteDataLoader (depended on fact_interventions)
- Remove file tracking schema (processed_files tracked fact_interventions loads)
- Remove legacy diagnosis functions from diagnosis_lookup.py
- Add source_row_count migration for pathway_refresh_log
- Clean all cross-references in __init__.py, data_source.py, migrate.py
Dry run test revealed GP lookup queries timing out at 30s (connection_timeout
in snowflake.toml). Increased to 600s. Also increased batch_size from 500 to
5000 — query time is ~40s regardless of batch size (CTE compilation overhead),
so larger batches reduce total time from ~50min to ~6min for 36K patients.
Dry run results: 91.8% GP match rate, 49.3% drug-indication match rate,
42,072 modified UPIDs, 1,846 pathway nodes across 6 date filters.
- Replace QUALIFY ROW_NUMBER()=1 with GROUP BY + COUNT(*) to return all matching
Search_Terms per patient instead of just the most recent
- Add earliest_hcd_date parameter to restrict GP codes to HCD data window
- Return code_frequency column (count of matching SNOMED codes per Search_Term)
for use as tiebreaker in drug-aware indication matching
- Update empty DataFrame returns to match new column format
Merge 'allergic asthma' and 'severe persistent allergic asthma' into
canonical 'asthma' in both CLUSTER_MAPPING_SQL (Snowflake CTE) and
load_drug_indication_mapping() (DimSearchTerm.csv loader).
- CLUSTER_MAPPING_SQL: 3 Cluster_IDs (AST_COD, eFI2_Asthma, SEVAST_COD) now
all map to Search_Term = 'asthma'
- Added SEARCH_TERM_MERGE_MAP constant for reusable normalization
- load_drug_indication_mapping() applies merge at CSV load time
- urticaria (XSAL_COD) stays separate — not merged with asthma
- Combined asthma drug list: BENRALIZUMAB, DUPILUMAB, INHALED, MEPOLIZUMAB,
OMALIZUMAB, RESLIZUMAB
Add load_drug_indication_mapping() and get_search_terms_for_drug() to
diagnosis_lookup.py. Loads DimSearchTerm.csv to build bidirectional
lookup between drug name fragments and Search_Terms. Uses substring
matching for drug fragments (handles both exact names like ADALIMUMAB
and partial fragments like PEGYLATED). Handles duplicate Search_Terms
(e.g., diabetes appearing under two directorates) by combining fragments.
Three issues identified and fixed during Task 3.1 testing:
1. Snowflake column name casing:
- Unquoted columns in Snowflake are returned as UPPERCASE
- Fixed by aliasing columns with quoted names: AS "Search_Term"
- Now correctly populates 139 unique Search_Terms (was 0)
2. Duplicate UPID index error:
- indication_df_for_chart could have duplicate UPIDs
- Added drop_duplicates(subset=['UPID']) before set_index()
- Keeps first occurrence (DIAGNOSIS over FALLBACK)
3. Missing UPIDs in indication lookup:
- Old code: built indication_df from unique PseudoNHSNoLinked only
- Problem: patients with multiple UPIDs (multi-provider) were missing
- Fixed: now builds indication_df from ALL unique UPIDs in df
- Also handles NaN values in Directory column safely
Validation results from test run:
- 36,628 patients queried
- 34,006 (92.8%) had GP diagnosis matches
- 139 unique Search_Terms found
- Top 5: drug misuse (8602), influenza (6239), diabetes (2476)
Still to verify: full pathway processing after these fixes.
- Add CLUSTER_MAPPING_SQL constant embedding full snomed_indication_mapping_query.sql
- Add get_patient_indication_groups() function that queries Snowflake directly
- Uses QUALIFY ROW_NUMBER() to get most recent diagnosis per patient
- Returns DataFrame with PatientPseudonym, Search_Term, EventDateTime
- Handles edge cases: empty list, Snowflake unavailable
- Batch processing with configurable batch_size (default 500)
- Comprehensive logging for match statistics
Two critical fixes for the indication-based pathway feature:
1. clean_snomed_code() now handles scientific notation (e.g., "1.06e+16")
- CSV export from pandas/Excel converts large SNOMED codes to scientific notation
- Without this fix, codes like "10629311000119108" were stored as "1.06e+16"
- Now properly converts to full integer strings
2. batch_lookup_indication_groups() now uses PseudoNHSNoLinked instead of PersonKey
- PersonKey is LocalPatientID (provider-specific like "J188448")
- PseudoNHSNoLinked is the pseudonymised NHS number that matches PatientPseudonym in GP records
- Without this fix, 0% of patients matched GP records
- Test shows ~20% match rate for ADALIMUMAB patients with correct identifier
- Added DirectorateAssignment dataclass for return type
- Added get_directorate_from_diagnosis() function to diagnosis_lookup.py
- Logic: Try diagnosis-based lookup first (direct SNOMED match)
- Returns FALLBACK source if no match found, letting caller handle fallback
- Extracts PatientPseudonym from UPID (last part after provider code)
- Updated __all__ exports with new dataclass and function
- Tested: function handles no-match cases correctly
Add two new functions to diagnosis_lookup.py for direct SNOMED code matching:
- get_drug_snomed_codes(drug_name): Query ref_drug_snomed_mapping for all
SNOMED codes mapped to a drug. Returns list of DrugSnomedMapping with
snomed_code, snomed_description, search_term, primary_directorate.
Tested: ADALIMUMAB returns 1320 mappings across 10 Search_Terms.
- patient_has_indication_direct(patient_pseudonym, mappings, connector):
Query PrimaryCareClinicalCoding for exact SNOMED code matches.
Returns most recent match by EventDateTime with DirectSnomedMatchResult.
Both functions follow existing patterns in the module and are exported
in __all__. The lookup is case-insensitive for drug names.
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Andrew Charlwood