Palliative care coding practices in Canada since the introduction of guidelines and the HSMR indicator
Bibliographic record
Abstract
OBJECTIVES: This study examines palliative care (PC) coding practices since the introduction of a national coding standard and assesses a potential association with hospital standardised mortality ratio (HSMR) results. SETTING: Acute-care hospitals in Canada. PARTICIPANTS: ∼16 million hospital discharges recorded in Canadian Institute for Health Information (CIHI)'s Discharge Abstract Database from April 2006 to March 2013. PRIMARY AND SECONDARY OUTCOME MEASURES: In-hospital mortality, patient characteristics and service utilisation among all hospitalisations, HSMR cases and palliative patients. METHODS: We assessed all separations in the Discharge Abstract Database between fiscal years 2006-2007 and 2012-2013 for PC cases at national, provincial and facility levels. In-hospital mortality was measured among all hospitalisations (including HSMR cases) and palliative patients. We calculated a variant HSMR-PC that included PC cases. RESULTS: There was an increase in the frequency of PC coding over the study period (from 0.78% to 1.12% of all separations), and year-over-year improvement in adherence to PC coding guidelines. Characteristics and resource utilisation of PC patients remained stable within provinces. Crude mortality among HSMR cases declined from 8.7% to 7.3%. National HSMR declined by 22% during the study period, compared with a 17% decline in HSMR-PC. Provincial results for HSMR-PC are not significantly different from regular HSMR calculation. CONCLUSIONS: The introduction of a national coding standard resulted in increased identification of palliative patients and services. Aside from PC coding practices, we note numerous independent drivers of improving HSMR results, notably, a significant reduction of in-hospital mortality, and increase in admissions accompanied by a greater number of coded comorbidities. While PC impacts the HSMR indicator, its influence remains modest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".