Insights from linking routinely collected data across Australian health jurisdictions: a case study of end-of-life health service use
Bibliographic record
Abstract
OBJECTIVES: The jurisdictional nature of routinely collected health data in Australia has created challenges for linking data across state/territory and federal government boundaries. This has impeded understanding of the interplay between service use across hospital and community care. Our objective was to demonstrate the value-add of cross-jurisdictional data using a case study of health service use and the factors associated with healthcare use towards the end of life. STUDY TYPE: Retrospective cohort study using routinely collected health data. METHODS: We used two decedent cohorts of people aged ≥65 years who died in New South Wales (NSW), Australia, in 2006 or 2007. The population cohort comprised the general NSW population linked to NSW data collections; the other cohort comprised Australian Government Department of Veterans' Affairs (DVA) clients (with full healthcare entitlements) linked to NSW and Commonwealth data. We compared information available on health services received during the last 6 months of life and ran multivariable analyses for both cohorts to demonstrate the added value of the Commonwealth data. RESULTS: We included 37 567 decedents in the population cohort and 11 259 in the DVA cohort. Cancer was the cause of death for 27% of the NSW cohort and 22% of the DVA cohort; approximately 40% of decedents in each cohort had a cancer history. We summarise information on hospital services for both cohorts and examine community care (general practitioner consultations, specialist presentations, prescriptions dispensed) for the DVA cohort only. Multivariable analyses in the DVA cohort demonstrated that high rates of emergency department (ED) presentations and hospitalisation were associated with higher rates of use of all health services, including community care. Use of primary care did not reduce ED or hospital use. We were not able to examine the interplay between community and hospital care in the NSW population cohort. CONCLUSIONS: In our case study, we demonstrated the value-add of Commonwealth data for understanding the drivers of hospital services use, which has implications for service delivery and resource allocation. There is an abundance of routinely collected health data in Australia that can be used to describe whole-of-healthcare use for a broad range of issues.
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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 | 0.061 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".