Monitoring health service use at the end of life in the Calgary Zone of Alberta: a Population-level analysis linking multiple administrative datasets
Bibliographic record
Abstract
IntroductionAs part of the Alberta Health Services (AHS) Calgary Zone Healthcare planning, a Palliative and End of Life Care Program (PEOLC) dashboard was developed and face validity of the indicators was examined by key stakeholders such as clinicians and decision makers. Objectives and ApproachAn internal dashboard was developed to explore End of Life (EOL) indicators that could provide evidence to support local PEOLC planning. Multiple administrative datasets available to AHS were used to estimate population needs of palliative care, current state of resource use, and EOL quality indicators. Underlying cause of death in Vital Statistics data was used to calculate minimal and maximal population estimates of palliative care needs between 2000 and 2014. Trends in acute care use during the last year of life were analyzed. Overall rates and geographic variations of selected indicators in Calgary Zone were reported. ResultsOver the period 2000 to 2014, number of adult deaths increased in Calgary Zone, from 5,094 in 2000 to 6,823 in 2014. In 2015/16, about half of all 10,848 hospital discharges in the last year of life were incurred in the last 60 days of life, and about 40 percent were incurred in the last 30 days. Overall, 11% of decedents visited ED more than once, 7% were discharged from hospital more than once, 19% spent more than 14 days in hospital. According to the ED and inpatient data, 40.7% of decedents, roughly 3,000 people, died in hospital. We observed an urban rural continuum gradient in most of these indicators, with rates varying more than two-fold for ED and hospital discharge related indicators. Conclusion/ImplicationsThe project demonstrates the feasibility of using existing data to generate information to support the PEOLC program planning in Calgary Zone. With early stakeholder engagement in dashboard design, analysis, interpretation, and dissemination, the dashboard was well received and will be updated as more recent data becomes available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".