Determinants of hospital utilization: the situation in Israel and selected countries.
Bibliographic record
Abstract
BACKGROUND: In 1995 hospital costs constituted about 42% of the health expenditures in Israel. Although this proportion remained stable over the last decade, hospital discharge rates per 1,000 population increased, while hospitalization days per 1,000 population and average length of stay declined. OBJECTIVE: To gain an insight into the forces behind these changes, we compared the trends in hospital utilization in Israel with those in 21 developed countries with available data. MATERIALS AND METHODS: Our data were derived from The "Hospitals and Day Care Units, 1995" report by the Health Information and Computer Services of the Israel Ministry of Health, and the Organization for Economic Cooperation and Development Health Data, 98. We examined the numbers of acute care hospital beds, of patients on dialysis and of doctors' consultations, health expenditures and age structure of the population in 1995 or closest year with available data, as well as changes in DRs, HDs and ALOS between 1976 and 1995. RESULTS: In Israel the DRs increased from 130 in 1976 to 177 in 1995 (36%), HDs declined from 992 to 818 (18%), and ALOS declined from 7.60 to 4.51 days (41%). Relative to other countries, in 1995 Israel had the lowest ALOS; low HDs similar to those in the UK, Portugal, Spain, the USA and Sweden; and intermediate DRs similar to those in Belgium, Germany, Sweden and Australia. The number of acute care beds per 1,000 population was directly related to HDs (r = 0.954, P = 0.000) and to DRs (r = 0.419, P = 0.052). Health expenditures (% of the gross national product) correlated with the number of patients on dialysis per 1,000,000 population (r = 0.743, P = 0.000). Between 1976 and 1995, HDs and ALOS declined in most countries, however the trends in DRs varied from an increase by 119% in the UK to a decline by 29% in Canada. CONCLUSIONS AND HYPOTHESES: The increase in DRs in Israel from 1976 to 1995 was shared by many but not all countries. This variability may be related to differences in trends in local practice norms and in available hospital beds. If the number of patients on dialysis is a valid index for use of expensive treatment modalities, the correlation of health expenditures with the number of patients on dialysis suggests that the use of expensive technology is a more important determinant of health care costs than the age of the population or hospital utilization. Since the use of expensive technology is highest during the first few days in hospital, decisions about health care policy should consider the possibility that the savings incurred by a further decline in HDs and ALOS may be offset by a possible increase in per diem hospital costs and in health care expenditures after discharge from hospital.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".