Evidence from an Applied Health Research Question (AHRQ): Describing Bundled Care Use in Ontario among Individuals with Total Joint Arthroplasty from fiscal year 2018-2021.
Bibliographic record
Abstract
ObjectiveTo describe clinical pathways and use of the bundled care funding model, where a patient’s treatment and payment is assigned to a group of providers, among individuals who underwent a total joint arthroplasty (TJA) in Ontario. The request for this work was submitted by the Rehabilitative Care Alliance through the Applied Health Research Question (AHRQ) program. ApproachAdults with TJA from April 1st 2018 to March 31st 2022 were included. Demographics, surgical characteristics, inclusion in bundled care, and use of health services were defined using administrative health data. Results were stratified by location of surgery (bilateral or unilateral hip, knee, or shoulder TJA). ResultsAmong 152,135 individuals with TJA during the study period, 71,996 individuals (47.3%) received services through a bundled care approach. Rate of bundled care use was highest among individuals with unilateral knee TJA (52.5% in bundled care), and the lowest among individuals with shoulder TJA (13.4% in bundled care). In fiscal year 2018, 33% of TJA patients used bundled care, in fiscal year 2019, over 50% of TJA patients used bundled care and 55% of TJA patients used bundled care by 2021. Most individuals used bundled care following an inpatient surgery (88.9% of individuals following a unilateral knee TJA) as opposed to a same day surgery (11.1% with a unilateral knee TJA). ConclusionThe purpose of bundled care service delivery is to provide integrated healthcare delivery and therefore improved patient experience and outcomes. Understanding bundled care use may improve healthcare planning and assessment of 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 | 0.039 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".