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?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».