Understanding variations in hospital length of stay and cost
Bibliographic record
Abstract
Hospitals are the most expensive component of OECD health care systems, accounting for around one third of total health care expenditure. Given growing pressures on government budgets, this is an area of expenditure that has already been, and will continue to be, thoroughly scrutinised for potential increases in efficiency. One way to assess hospital efficiency is to measure the amount of resources each hospital uses to treat specific conditions. A care delivery process may be seen as more efficient – after accounting for broader health system and market factors that may constrain the hospital from operating at an efficient level – if it consumes fewer resources while delivering adequate care for the same condition, the dimension of efficiency under review here. In this light, measuring hospital length of stay and costs for a given condition helps the understanding of how efficient (better performing) hospitals are relative to each other. Through international comparative work, this paper helps policy makers understand the scope and nature of length of stay/costs variation across hospitals in OECD countries. It also explores whether characteristic of hospitals or of countries' regulatory and operating environments can explain differences in efficiency. Data on length of stay and costs to treat patients admitted to hospitals for nine tracing conditions/treatments were collected and analysed for Canada (Alberta province), France, Ireland and Israel for 2012-2014. Our analysis shows that hospitals with a number of beds ranging between 200 and 600, and not-for-profit hospitals report shorter length of stay and lower costs for several conditions/treatments. It also shows that variations in efficiency are more likely to exist at the hospital level for cardiac surgery (acute myocardial infarction with percutaneous transluminal coronary angioplasty and coronary artery bypass graft), and at country level for hysterectomy, caesarean section and normal delivery. These results shed some light on the importance of hospital payment system in fostering efficiency in care delivery for standard/high volume treatments such as normal delivery, whereas hospital management and organisation seem to drive efficiency for more complex/technology driven treatments such as bypass surgery.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".