Measuring the Value of Total Hip and Knee Arthroplasty: Considering Costs Over the Continuum of Care
Bibliographic record
Abstract
BACKGROUND: Controlling escalating costs of hip (THA) and knee arthroplasty (TKA) without compromising quality of care has created the need for innovative system reorganization to inform sustainable solutions. QUESTIONS/PURPOSES: The purpose of this study was to inform estimates of the value of THA and TKA by determining: (1) the data sources data required to obtain costs across the care continuum; (2) the data required for different analytical perspectives; and (3) the relative costs across the continuum of care. METHODS: Within the context of a pragmatic randomized controlled trial comparing alternative care pathways, we captured healthcare resource use: (1) 12 months before surgery; (2) inpatient; (3) acute recovery; and (4) long-term recovery 3 and 12 months postsurgery. We established a standardized costing model to reflect both the healthcare payer and patient perspectives. RESULTS: Multiple data sources from regional health authorities, administrative databases, and patient questionnaire were required to estimate costs across the care continuum. Inpatient and acute care costs were approximately 60% of the total with the remaining 40% incurred 12 months presurgery and 12 months postsurgery. Regional health authorities bear close to 60%, and patient costs are approximately 30% of the mean total costs, most of which were incurred after the acute inpatient stay. CONCLUSIONS: To fully understand the value of an orthopaedic intervention such as THA and TKA, a broader perspective than one limited to the payer should be considered using a standardized measurement framework over a relevant time horizon and from multiple viewpoints to reflect the substantial patient burden and support sustainable improvement over the care continuum. LEVEL OF EVIDENCE: Level III, economic and decision analyses study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.026 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".