Exploring the Impact of Health Insurance on Health Care Utilization and Outcome Using Electronic Medical Record Data
Notice bibliographique
Résumé
ABSTRACTObjectiveWith tremendous potential for research and policy use, the development of Electronic Medical Record (EMR) is unprecedentedly growing in China. The rich clinical and financial data in the Chinese EMR provides us a unique chance to examine the impacts of health insurance on health care utilization and outcomes, controlling for patient’s disease severity. ApproachesOur study population included patients with cirrhosis or primary liver cancer (PLC), from a large teaching hospital in Beijing. The comorbidity and disease severity variables were defined using EMR automated extraction methods that were validated in previous study. Health insurance was measured by actual reimbursement ratio (RR), which better captures patients’ actual financial burden than type of health insurance. Generalized linear regression model was used to analyze the impacts of health insurance coverage on total hospital expense, ratio of medication cost to total expense, and number of major procedures (i.e., transcatheter arterial chemoembolization, TACE) for cirrhosis. Logistic regression was used to assess the impact of health insurance on hospital mortality and the rate of TACE. We employed a wide range of risk factors in our models to adjust for disease severity and comorbidities, including Charlson comorbidities, MELD-Na score, and etiological factors of liver diseases.ResultsIn total, 5,465 cirrhosis patients and 3,357 PLC patients were included in the study. Among the PLC patients we identified 534 patients underwent TACE. After adjusted for comorbidities, disease severity and other confounders, RR was found to be associated with hospital mortality with odds ratio 3.2 in cirrhosis patients and 6.0 in PLC patients. Higher RR was correlated to lower total hospital cost (logarithm transferred coefficient -0.08 in cirrhosis patients and -0.15 in PLC patients) but related to higher ratio of medication cost (logarithm transferred coefficient 0.09 in both cirrhosis and PLC patients). Additionally, higher RR was associated with higher rate (odds ratio 1.6 in PLC patients) and also more times of TACE (logarithm transferred coefficient 0.31 in TACE patients). The results were consistent between cirrhosis patients and PLC patients.ConclusionThis study provided evidences that physicians’ behavior was influenced by health insurance. Patients with more generous health insurance coverage (higher RR) were found to have relatively lower total hospital cost but higher ratio of medication cost, higher rate and more times of TACE, and were more prone to die in hospital. These are evidences for physician’s gaming reacting to the economic incentives of the payment systems in China.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».