Multidisciplinary Insights into Health Care Financial Risk and Hospital Surge Capacity, Part 4: What Size Does a Health Insurer or Health Authority Need to be to Minimise Risk?
Bibliographic record
Abstract
Death acts as a dual proxy for nearness to death (NTD) and related wider morbidity. Hospital bed use in the last year of life accounts for somewhere around 25 bed days of hospital resource consumption. The knock-on morbidity due to the agents promoting death also accounts for up to another 25 bed days of hospital resource consumption. This is approximately 50% of total bed consumption as a ratio of occupied beds per death (all-cause mortality). This explains why the trend in deaths alone can explain so much of medical bed utilisation. The volatility in the year-to-year difference in deaths can be used to determine the number of deaths in a population (health insurance members, health authority, health maintenance organisation, commissioning group, etc) at which this volatility reaches an asymptote. Data from 97 countries with more than 1,000 deaths per annum shows that at 1,000 deaths the standard deviation is high at between ± 5% to ± 10% depending on country. Somewhere around 20,000 deaths appear to be the size which minimises volatility (standard deviation) to an acceptable level of ± 2% to ± 3.5% depending on country. Volatility shows a small further reduction up to 100,000 deaths above which there is no further reduction. This is country specific and Australia, Canada, and the USA seem to have a lower volatility than UK local government areas. The maximum year-to-year increase is around 2- to 3.5-times higher than the standard deviation. The ability to forecast year-end deaths, and hence death associated costs, gives an acceptable tolerance at 20,000 deaths. This is illustrated using a month 6 forecast for 350 English and Welsh local government areas and regions. Countries with fewer than 20,000 deaths are free to subdivide the country into smaller health authorities, etc but must ensure that risk sharing between these units is done at national level. For example, New Zealand only has around 30,000 deaths per annum but has 21 Area Health Boards ranging from 300 to 3,000 deaths per annum (median 1,500). None of these are large enough to sustain the implied financial and capacity risk and this risk should be held at national level. Risk sharing based on deviation from funded number of deaths seems a sensible compromise with adjustment for costs associated with cause of death.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".