fix: increase network timeout and batch size for GP lookup queries (Task 3.2)
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.
This commit is contained in:
Andrew Charlwood
+123
-196
@@ -5,115 +5,130 @@ If you discover a new failure pattern during your work, add it to this file.
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---
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## Drug-Indication Matching Guardrails
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### Match drugs to indications, not just patients to indications
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- **When**: Building the indication mapping for pathway charts
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- **Rule**: Each drug must be validated against BOTH the patient's GP diagnoses AND the drug-to-indication mapping from DimSearchTerm.csv. A patient being diagnosed with rheumatoid arthritis does NOT mean all their drugs are for rheumatoid arthritis.
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- **Why**: The previous approach assigned ONE indication per patient (most recent GP dx), ignoring which drugs actually treat which conditions. This produced misleading pathways.
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### Use DimSearchTerm.csv for drug-to-Search_Term mapping
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- **When**: Determining which Search_Term a drug belongs to
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- **Rule**: Load `data/DimSearchTerm.csv`. The `CleanedDrugName` column has pipe-separated drug name fragments. Match HCD drug names against these fragments using substring matching (case-insensitive).
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- **Why**: This CSV is the authoritative mapping of which drugs are used for which clinical indications.
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### Use substring matching for drug fragments
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- **When**: Matching HCD drug names against DimSearchTerm CleanedDrugName fragments
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- **Rule**: Check if any fragment from DimSearchTerm is a SUBSTRING of the HCD drug name (case-insensitive). E.g., "PEGYLATED" should match "PEGYLATED LIPOSOMAL DOXORUBICIN".
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- **Why**: DimSearchTerm contains both full drug names (ADALIMUMAB) and partial fragments (PEGYLATED, INHALED). Exact match would miss the partial ones.
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### Modified UPID uses pipe delimiter
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- **When**: Creating indication-aware UPIDs
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- **Rule**: Format is `{original_UPID}|{search_term}`. Use pipe `|` as delimiter. Do NOT use ` - ` (hyphen with spaces) as that's used for pathway hierarchy levels in the `ids` column.
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- **Why**: The `ids` column uses " - " to separate hierarchy levels (e.g., "N&WICS - NNUH - rheumatoid arthritis - ADALIMUMAB"). Using the same delimiter in UPIDs would break hierarchy parsing.
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### Return ALL GP matches per patient, not just most recent
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- **When**: Querying Snowflake for patient GP diagnoses
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- **Rule**: Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`. Return ALL matching Search_Terms per patient with `GROUP BY + COUNT(*)` for code_frequency.
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- **Why**: A patient may have GP diagnoses for both rheumatoid arthritis AND asthma. We need ALL matches to cross-reference with their drugs.
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### Restrict GP code lookup to HCD data window
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- **When**: Building the WHERE clause for the GP record query
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- **Rule**: Add `AND pc."EventDateTime" >= :earliest_hcd_date` where `earliest_hcd_date` is `MIN(Intervention Date)` from the HCD DataFrame. Pass this as a parameter to `get_patient_indication_groups()`.
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- **Why**: Old GP codes from years before treatment started add noise. A diagnosis coded 10 years ago may no longer be relevant. Restricting to the HCD window ensures code_frequency reflects recent clinical activity for the conditions being actively treated.
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### Tiebreaker: highest GP code frequency when a drug matches multiple indications
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- **When**: A single drug maps to multiple Search_Terms AND the patient has GP dx for multiple
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- **Rule**: Use `code_frequency` (COUNT of matching SNOMED codes per Search_Term per patient) from the GP query. The Search_Term with the most matching codes in the patient's GP record wins. If tied, use alphabetical Search_Term for determinism.
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- **Why**: E.g., ADALIMUMAB is listed under rheumatoid arthritis, crohn's disease, psoriatic arthritis, etc. A patient with 47 RA codes and 2 crohn's codes is almost certainly on ADALIMUMAB for RA. Frequency of GP coding is a much stronger signal of clinical intent than recency — a recent one-off asthma check doesn't mean ADALIMUMAB is for asthma.
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### Same patient, different indications = separate modified UPIDs
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- **When**: A patient's drugs map to different Search_Terms
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- **Rule**: Create separate modified UPIDs for each indication. E.g., `RMV12345|rheumatoid arthritis` and `RMV12345|asthma`. These are treated as separate "patients" by the pathway analyzer.
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- **Why**: This is the core design — drugs for different indications should create separate treatment pathways, even for the same physical patient.
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### Fallback to directory for unmatched drugs
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- **When**: A drug doesn't match any Search_Term OR the patient has no GP dx for any of the drug's Search_Terms
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- **Rule**: Use fallback format: `{UPID}|{Directory} (no GP dx)`. The indication_df maps this to `"{Directory} (no GP dx)"`.
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- **Why**: Maintains consistent behavior with the previous approach for patients/drugs without GP diagnosis matches.
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### Merge asthma Search_Terms but keep urticaria separate
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- **When**: Working with asthma-related Search_Terms from CLUSTER_MAPPING_SQL or DimSearchTerm.csv
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- **Rule**: Merge "allergic asthma", "asthma", and "severe persistent allergic asthma" into a single "asthma" Search_Term. Keep "urticaria" as a separate Search_Term — do NOT merge it with asthma.
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- **Why**: These are clinically the same condition at different severity levels. Splitting them fragments the data. Urticaria is a distinct dermatological condition that happens to share OMALIZUMAB.
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### Don't modify directory chart processing
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- **When**: Making changes to the indication matching logic
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- **Rule**: Only modify the indication chart path (`elif current_chart_type == "indication":`). Directory charts use unmodified UPIDs and directory-based grouping.
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- **Why**: Directory charts work correctly and should not be affected by indication matching changes.
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---
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## Snowflake Query Guardrails
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### Use PseudoNHSNoLinked for GP record matching
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- **When**: Querying GP records (PrimaryCareClinicalCoding) for patient diagnoses
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- **Rule**: Use `PseudoNHSNoLinked` column from HCD data, NOT `PersonKey` (LocalPatientID)
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- **Why**: PersonKey is provider-specific local ID. Only PseudoNHSNoLinked matches PatientPseudonym in GP records.
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### Embed cluster query as CTE in Snowflake
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- **When**: Looking up patient indications during data refresh
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- **Rule**: Use the `CLUSTER_MAPPING_SQL` content as a WITH clause in the patient lookup query
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- **Why**: This ensures we always use the complete cluster mapping and don't need local storage
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### Quote mixed-case column aliases in Snowflake SQL
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- **When**: Writing SELECT queries that return results to Python code
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- **Rule**: Use `AS "ColumnName"` (quoted) for any column alias you'll access by name in Python
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- **Why**: Snowflake uppercases unquoted identifiers. `SELECT foo AS Search_Term` returns `SEARCH_TERM`, so `row.get('Search_Term')` returns None. Fix: `SELECT foo AS "Search_Term"`
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### Build indication_df from all unique UPIDs, not PseudoNHSNoLinked
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- **When**: Creating the indication mapping DataFrame for pathway processing
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- **Rule**: Use `df.drop_duplicates(subset=['UPID'])` not `drop_duplicates(subset=['PseudoNHSNoLinked'])`
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- **Why**: A patient visiting multiple providers has multiple UPIDs. Using unique PseudoNHSNoLinked only maps one UPID per patient, leaving others as NaN.
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---
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## Data Processing Guardrails
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### Copy DataFrames in functions that modify columns
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- **When**: Writing functions like `prepare_data()` that modify DataFrame columns
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- **Rule**: Always `df = df.copy()` at the start of any function that modifies column values on the input DataFrame
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- **Why**: `prepare_data()` mapped Provider Code → Name in-place. When called multiple times on the same DataFrame, only the first call worked. The fix: `df.copy()` prevents destructive mutation.
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### Include chart_type in UNIQUE constraints for pathway_nodes
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- **When**: Creating or modifying the pathway_nodes table schema
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- **Rule**: The UNIQUE constraint MUST include `chart_type`: `UNIQUE(date_filter_id, chart_type, ids)`
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- **Why**: Without `chart_type`, `INSERT OR REPLACE` silently overwrites directory chart nodes when indication chart nodes are inserted.
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### Handle NaN in Directory when building fallback labels
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- **When**: Creating fallback indication labels for patients without GP diagnosis match
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- **Rule**: Check `pd.notna(directory)` before concatenating to string. Use `"UNKNOWN (no GP dx)"` for NaN cases.
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- **Why**: NaN handling prevents TypeError and ensures meaningful fallback labels.
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### Use parameterized queries for SQLite
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- **When**: Building WHERE clauses with user-selected filters
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- **Rule**: Use `?` placeholders and pass params tuple — never string interpolation
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- **Why**: Prevents SQL injection and handles special characters in drug/directory names
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### Use existing pathway_analyzer functions
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- **When**: Processing pathway data for the icicle chart
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- **Rule**: Reuse functions from `analysis/pathway_analyzer.py` — don't reinvent
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- **Why**: The existing code handles edge cases (empty groups, statistics calculation, color mapping)
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---
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## Reflex Guardrails
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### Use .to() methods for Var operations in rx.foreach
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- **When**: Working with items inside `rx.foreach` render functions
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- **Rule**: Use `item.to(int)` for numeric comparisons, `item.to_string()` for text operations
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- **Why**: Items from rx.foreach are `ObjectItemOperation` Vars, not plain Python values. Using `>=` or f-strings directly causes TypeError.
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**Bad:**
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```python
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def render_row(item):
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color = rx.cond(item["value"] >= 50, "green", "red") # TypeError!
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return rx.text(f"{item['name']}: {item['value']}") # Won't interpolate!
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```
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**Good:**
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```python
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def render_row(item):
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color = rx.cond(item["value"].to(int) >= 50, "green", "red")
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return rx.text(item["name"].to_string() + ": " + item["value"].to_string())
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```
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- **Why**: Items from rx.foreach are Var objects, not plain Python values.
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### Use rx.cond for conditional rendering, not Python if
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- **When**: Conditionally showing/hiding components or changing styles based on state
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- **Rule**: Use `rx.cond(condition, true_component, false_component)` — not Python `if`
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- **Why**: Python `if` evaluates at definition time; `rx.cond` evaluates reactively at render time
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### State variables must have default values
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- **When**: Defining state variables in the State class
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- **Rule**: Always provide a default: `my_var: str = ""` not just `my_var: str`
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- **Why**: Reflex requires defaults for state initialization
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### Computed vars use @rx.var decorator
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- **When**: Creating derived/computed values from state
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- **Rule**: Use `@rx.var` decorator, return a value, and include return type annotation
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- **Why**: Without the decorator, the method won't be reactive
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```python
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@rx.var
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def filtered_count(self) -> int:
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return len(self.filtered_data)
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```
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### Event handlers don't return values to components
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- **When**: Creating methods that handle user interactions
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- **Rule**: Event handlers modify state; they don't return values directly to UI
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- **Why**: Use state variables and computed vars to communicate between handlers and UI
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---
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## Design System Guardrails
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### Never hardcode colors
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- **When**: Any styling that involves color
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- **Rule**: Import from `pathways_app.styles` and use `Colors.PRIMARY`, `Colors.SLATE_700`, etc.
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- **Why**: Hardcoded colors break consistency and make theming impossible
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### Never hardcode spacing
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- **When**: Any padding, margin, gap values
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- **Rule**: Use `Spacing.SM`, `Spacing.LG`, etc. from the styles module
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- **Why**: Consistent spacing is fundamental to visual cohesion
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### Use design system typography
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- **When**: Any text styling
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- **Rule**: Use the typography classes/helpers from styles.py
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- **Why**: Typography hierarchy creates visual structure
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---
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## Data Processing Guardrails
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### Use existing pathway_analyzer functions
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- **When**: Processing pathway data for the icicle chart
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- **Rule**: Reuse functions from `analysis/pathway_analyzer.py` — don't reinvent
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- **Why**: The existing code handles edge cases (empty groups, statistics calculation, color mapping)
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### Extract denormalized fields from ids string
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- **When**: Creating denormalized columns (trust_name, directory, drug_sequence)
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- **Rule**: Parse the `ids` column which contains the full hierarchical path
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- **Why**: The ids format is "Trust|Directory|Drug1|Drug2|..." — split on "|" to extract components
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### Handle None/NULL values in pathway data
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- **When**: Reading pathway_nodes from SQLite
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- **Rule**: Always use `or ""` / `or 0` / `or "N/A"` when accessing optional columns
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- **Why**: Many columns (costpp, average_spacing, etc.) can be NULL for certain hierarchy levels
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### Use parameterized queries for SQLite
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- **When**: Building WHERE clauses with user-selected filters
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- **Rule**: Use `?` placeholders and pass params tuple — never string interpolation
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- **Why**: Prevents SQL injection and handles special characters in drug/directory names
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---
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## Code Quality Guardrails
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### Verify compilation before committing
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- **When**: After ANY code changes
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- **Rule**: Run `python -m py_compile <file>` AND `reflex run` (briefly) to check
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- **Why**: Committing broken code wastes the next iteration fixing preventable errors
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### One component per function
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- **When**: Creating UI components
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- **Rule**: Each logical component should be its own function returning `rx.Component`
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- **Why**: Smaller functions are easier to debug and reuse
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### Keep state minimal
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- **When**: Designing state structure
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- **Rule**: Only store what's necessary; derive everything else with computed vars
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- **Why**: Duplicate state leads to sync bugs
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---
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## Process Guardrails
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@@ -134,107 +149,19 @@ def filtered_count(self) -> int:
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- **Why**: The next iteration has zero memory. If you don't write it down, it's lost.
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### Check existing code for patterns
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- **When**: Unsure how to implement something in Reflex or pathway processing
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- **Rule**: Look at `pathways_app/pathways_app.py`, `analysis/pathway_analyzer.py`, `visualization/plotly_generator.py`
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- **When**: Unsure how to implement something
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- **Rule**: Look at `pathways_app/pathways_app.py`, `analysis/pathway_analyzer.py`, `cli/refresh_pathways.py`
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- **Why**: The existing codebase has solved many quirks already
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---
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### Snowflake connection_timeout must be high enough for GP lookup queries
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- **When**: GP record queries against PrimaryCareClinicalCoding time out
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- **Rule**: Ensure `connection_timeout` in config/snowflake.toml is at least 600 (currently set to 600). This controls the Python client's `network_timeout`, which is how long the client waits for ANY Snowflake response. Do NOT lower this value.
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- **Why**: GP lookup queries take ~40s per batch due to CTE compilation overhead. With connection_timeout=30, every batch timed out silently (error 000604/57014).
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## UI Redesign Guardrails
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### Clear Reflex cache before running
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- **When**: Before running `reflex run` or `reflex compile`, especially after style/layout changes
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- **Rule**: Delete `.states` and `.web` folders first: `Remove-Item -Recurse -Force .states, .web -ErrorAction SilentlyContinue`
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- **Why**: Stale cache causes old styles/components to persist, making it appear changes didn't work
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### Test visual changes with reflex run
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- **When**: After any layout or styling changes
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- **Rule**: Run `reflex run` and visually verify in browser. Screenshots are not enough.
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- **Why**: CSS calculations and flex layouts often behave differently than expected
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### Don't break existing functionality
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- **When**: Refactoring layout components
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- **Rule**: Ensure all filter handlers, KPI updates, and chart rendering still work after changes
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- **Why**: It's easy to accidentally disconnect event handlers when restructuring components
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### Use calc() for responsive heights
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- **When**: Making elements fill remaining viewport space
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- **Rule**: Use `height="calc(100vh - Xpx)"` where X is the sum of fixed-height elements above
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- **Why**: Fixed heights don't adapt to content changes; calc() keeps things responsive
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### Test at multiple viewport widths
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- **When**: Making full-width changes
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- **Rule**: Test at 1366px, 1920px, and 2560px widths minimum
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- **Why**: Full-width layouts can break or look sparse at extreme sizes
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### Keep filter dropdown z-index high
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- **When**: Restructuring filter section
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- **Rule**: Dropdown panels need `z_index="50"` or higher to appear above chart
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- **Why**: Plotly charts have their own stacking context and can overlap dropdowns
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---
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## Snowflake Query Guardrails
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### Use PseudoNHSNoLinked for GP record matching
|
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- **When**: Querying GP records (PrimaryCareClinicalCoding) for patient diagnoses
|
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- **Rule**: Use `PseudoNHSNoLinked` column from HCD data, NOT `PersonKey` (LocalPatientID)
|
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- **Why**: PersonKey is provider-specific local ID. Only PseudoNHSNoLinked matches PatientPseudonym in GP records.
|
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### Use Search_Term for grouping, not Indication
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- **When**: Creating indication-based pathway hierarchy
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- **Rule**: Group patients by `Search_Term` from the cluster query
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- **Why**: Search_Term provides meaningful clinical groupings (~148 values)
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### Handle unmatched patients in indication chart
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- **When**: Patient has no GP diagnosis matching cluster SNOMED codes
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- **Rule**: Use their assigned directorate (from fallback logic) as the grouping label, not "Unknown"
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- **Why**: User wants mixed labels - Search_Terms for matched patients, directorate names for unmatched
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### Use most recent SNOMED code for multiple matches
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- **When**: Patient has GP records matching multiple SNOMED codes
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- **Rule**: Use the match with the most recent `EventDateTime` from PrimaryCareClinicalCoding
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- **Why**: Most recent diagnosis reflects current clinical state
|
||||
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### Embed cluster query as CTE in Snowflake
|
||||
- **When**: Looking up patient indications during data refresh
|
||||
- **Rule**: Use the `snomed_indication_mapping_query.sql` content as a WITH clause in the patient lookup query
|
||||
- **Why**: This ensures we always use the complete cluster mapping and don't need local storage
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|
||||
### Chart type column in pathway_nodes
|
||||
- **When**: Inserting pathway records to SQLite
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||||
- **Rule**: Include `chart_type` column with value "directory" or "indication"
|
||||
- **Why**: Needed to filter pathways when user toggles chart type in UI
|
||||
|
||||
### Quote mixed-case column aliases in Snowflake SQL
|
||||
- **When**: Writing SELECT queries that return results to Python code
|
||||
- **Rule**: Use `AS "ColumnName"` (quoted) for any column alias you'll access by name in Python
|
||||
- **Why**: Snowflake uppercases unquoted identifiers. `SELECT foo AS Search_Term` returns `SEARCH_TERM`, so `row.get('Search_Term')` returns None. Fix: `SELECT foo AS "Search_Term"`
|
||||
|
||||
### Build indication_df from all unique UPIDs, not PseudoNHSNoLinked
|
||||
- **When**: Creating the indication mapping DataFrame for pathway processing
|
||||
- **Rule**: Use `df.drop_duplicates(subset=['UPID'])` not `drop_duplicates(subset=['PseudoNHSNoLinked'])`
|
||||
- **Why**: A patient visiting multiple providers has multiple UPIDs (UPID = ProviderCode[:3] + PersonKey). Using unique PseudoNHSNoLinked only maps one UPID per patient, leaving others as NaN and causing TypeError in build_hierarchy.
|
||||
|
||||
### Handle NaN in Directory when building fallback labels
|
||||
- **When**: Creating fallback indication labels for patients without GP diagnosis match
|
||||
- **Rule**: Check `pd.notna(directory)` before concatenating to string. Use `"UNKNOWN (no GP dx)"` for NaN cases.
|
||||
- **Why**: `str(nan) + " (no GP dx)"` doesn't cause error, but `nan + " (no GP dx)"` causes TypeError. Always be explicit about NaN handling.
|
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|
||||
### Copy DataFrames in functions that modify columns
|
||||
- **When**: Writing functions like `prepare_data()` that modify DataFrame columns (e.g., mapping Provider Code to trust names)
|
||||
- **Rule**: Always `df = df.copy()` at the start of any function that modifies column values on the input DataFrame
|
||||
- **Why**: `prepare_data()` mapped Provider Code → Name in-place. When called for directory charts first, then indication charts second, the second call tried to map already-mapped names → NaN, silently dropping all data. The fix: `df = df.copy()` prevents destructive mutation of the caller's DataFrame.
|
||||
|
||||
### Include chart_type in UNIQUE constraints for pathway_nodes
|
||||
- **When**: Creating or modifying the pathway_nodes table schema
|
||||
- **Rule**: The UNIQUE constraint MUST include `chart_type`: `UNIQUE(date_filter_id, chart_type, ids)`
|
||||
- **Why**: Without `chart_type`, `INSERT OR REPLACE` silently overwrites directory chart root/trust nodes when indication chart nodes with the same `ids` are inserted. This caused directory charts to lose all level 0 (root) and level 1 (trust) nodes, making KPIs show 0 patients. If the database exists with an old schema, you must DROP and recreate the table.
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|
||||
### Verify database schema matches code after migrations
|
||||
- **When**: After running data refresh and seeing unexpected results (e.g., missing nodes, wrong counts)
|
||||
- **Rule**: Compare actual table schema (`SELECT sql FROM sqlite_master WHERE name='tablename'`) with the schema defined in `data_processing/schema.py`
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||||
- **Why**: SQLite doesn't alter UNIQUE constraints in place. If the schema was created before a constraint was updated in code, the old constraint persists silently.
|
||||
### Use large batch sizes (5000+) for GP record lookups
|
||||
- **When**: Calling `get_patient_indication_groups()` with patient batches
|
||||
- **Rule**: Use batch_size=5000 or larger. The query time is ~40s regardless of batch size (5 patients ≈ 500 patients ≈ 5000 patients). Smaller batches just multiply the fixed overhead.
|
||||
- **Why**: With batch_size=500, 36K patients needed 74 batches × 40s = ~50 min. With batch_size=5000, only 8 batches × 45s = ~6 min. The bottleneck is CTE compilation, not data volume.
|
||||
|
||||
<!--
|
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ADD NEW GUARDRAILS BELOW as failures are observed during the loop.
|
||||
|
||||
Reference in New Issue
Block a user