Beyond Traditional Inefficiency Measures: Quantifying Health System Waste Through a Hierarchical Model of Inherited and Self-Generated Persistent Inefficiency
Bibliographic record
Abstract
This study develops a hierarchical inefficiency model to quantify how persistent technical inefficiency is generated and transmitted through multi-tiered healthcare systems. Departing from conventional hospital-centric assessments, the model decomposes inefficiency into inherited (propagated from higher governance levels) and self-generated (locally produced) components across three administrative tiers: province, region, and hospital. By leveraging the nested structure of Canadian healthcare governance, the framework captures system-level inefficiencies embedded in institutional design rather than isolated provider-level performance. Applied to panel data from hospitals in Alberta, Nova Scotia, and Ontario, the analysis shows that persistent inefficiency consistently originates at the top of the hierarchy, within provincial governance, where it is highest: 7.25% in Alberta, 7.02% in Nova Scotia, and 6.97% in Ontario. It then compounds as it flows downward, yielding total system inefficiencies of 16.45%, 14.63%, and 14.88%, respectively. These results demonstrate that hospital-level inefficiencies often reflect upstream structural constraints rather than solely local mismanagement. While the decomposition provides a tractable diagnostic of how inefficiency propagates, it also raises a policy dilemma: Should reforms equip hospitals and regions to absorb inherited burdens, or should they target the persistent sources of inefficiency embedded in provincial governance? These findings challenge standard health economics approaches that localise inefficiency at the point of care. By reframing inefficiency as a cascading, structural phenomenon, this study offers a system-aligned perspective for identifying where meaningful reform must begin.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".