Linking Pan-Canadian Administrative and Clinical Registry Data to gain insights across the continuum of care
Bibliographic record
Abstract
IntroductionData linkages expand the potential of discrete national health databases to follow patients across the health care continuum. Novel linkages across health care sectors, jurisdictions, and over time provide a deeper understanding of a patient’s journey and can facilitate health systems performance evaluation to ultimately improve patient-level health outcomes. Objectives and ApproachThe objective was to increase the value of pan-Canadian administrative and cost data using novel linkages to longitudinal patient-based clinical registries. One such example is a data linkage comprised of three national data holdings to examine inpatient care and associated costs during ten years of chronic dialysis treatments. Patient data on dialysis treatment changes from the longitudinal clinical registry were linked to episodic inpatient hospitalization and corresponding cost data. Effect of various cofactors on the risk of hospitalization, as well as the cost burden of such hospitalizations on the health system, were examined. ResultsEffective data linkage can facilitate analyses of a broader spectrum of outcomes adjusted for a broader set of patient characteristics and treatment modalities. Linking dialysis patient registry data to administrative inpatient care data showed that patient-specific covariates significantly affect the risk of dialysis patients being hospitalized. These findings were consistent for hospitalizations for dialysis-related infections, a highly preventable complication amongst dialysis patients. The rate of hospitalization of dialysis patients in Canada ranged from 1.1 to 1.4 hospitalizations per patient-year on dialysis. Linking to cost data showed that the average estimated cost for these hospitalizations ($13,634 per patient year on dialysis) was more than double that of the general population. These and other insights can be achieved more quickly and efficiently using data linkages. Conclusion/ImplicationsCanada’s many health-related data assets, when combined through effective linkage, can provide tremendous potential for performing longitudinal, patient-oriented analyses in a pan-Canadian setting. Using administrative and cost data to augment the clinical information in the registry data proves to be useful for informing improvements in health and health services.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".