Predicting Expenditures for Persons With Chronic Conditions
Notice bibliographique
Résumé
Research on the distribution of health care expenditures among the U.S. population has shown that a small proportion of individuals, many of whom have chronic health conditions, accounts for a disproportionate share of total expenses. Previous work by Berk and Monheit showed that in both 1987 and 1996 the top 1% of individuals in the expenditure distribution accounted for more than one-quarter of all expenses, and the top 5% accounted for more than half. They found this distribution had been constant for more than 20 years, despite significant changes in the U.S. health care system during that period. Previous research has also shown that more than three-quarters of all medical expenditures are associated with the care of persons with chronic conditions. Recent efforts to contain medical care costs in the U.S. have focused on what might be done to obviate some of this disproportionate spending, either through more effective use of preventive care, or better management of care for persons with chronic medical conditions. These efforts are complicated, however, by the fact that research has shown that being in the top of the expenditure distribution is not something that is highly persistent over time. Although expenditures in one year are correlated with expenditures in the next, there are a number of factors that determine individuals' levels of spending from one year to the next, and simply knowing base year expenditures does not mean insurers or providers can identify which individuals are most appropriate for additional attention. Nonetheless, to the extent there are specific, treatable conditions that are associated with persistently high expenditures there may be opportunities to develop methods of managing treatment that can enhance efficiency without sacrificing, or perhaps even improving, quality of care. This analysis builds on previous work on the prediction and concentration of expenditures to examine which health conditions and health status measures, in addition to demographic characteristics, are most associated with high medical care costs. The focus will be on chronic conditions, such as heart disease, cancer, diabetes, and depression, and combinations of those chronic conditions, to determine which set of conditions and other factors, including overall health status measures such as risk scores and self assessed health, are the best predictors of being in the upper tail of the expenditure distribution. For this study we pool data from the 1996 through 2004 Medical Expenditure Panel Survey (MEPS) and predict annual expenditures for individuals with single and multiple chronic conditions. The focus will be on identifying the subset of chronic conditions or combination of conditions that appear to have the greatest potential for efficiency improvement due to the level of expenditures associated with them.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».