Estimating the completeness of physician billing claims: an application of three-source capture-recapture methods
Notice bibliographique
Résumé
Background: Physician billing claims data contain information about services provided to patients. Fee-for-service (FFS) and non-fee-for-service (NFFS) physicians both submit claims; however, physician billing claims data may not comprehensively capture patient contacts from NFFS physicians who do not submit parallel claims (i.e., shadow bill). Capture-recapture (CR) methods, which were first developed in ecology research to estimate animal population size, have been proposed to estimate the number of missed claims. Our objective was to use three-source CR methods to estimate the completeness of physician billing claims data in Manitoba. Methods: Log-linear regression (LLR) and multinomial logistic regression (MLR) models for three-source CR methods were investigated. Using computer simulation, the LLR and MLR models were compared using percent bias (PB) and 95% confidence interval (CI) coverage for correctly specified and misspecified models in the presence of heterogeneity of capture probability and data source dependence. The methods were applied to Manitoba’s administrative health data to estimate the number of cancer cases diagnosed by FFS and NFFS physicians. The Manitoba Cancer Registry was used to validate the estimates. Results: Both the LLR and MLR models had low PB and acceptable 95% CI coverage for the correctly specified model under all simulation scenarios. However, the MLR model had less bias and better coverage when there was dependence among sources and covariates. The numeric example, the study cohort was comprised of 3,331 individuals. A total of 1,747 (52.4%) individuals were seen by a FFS physician and 1,584 (47.6%) individuals seen by a NFFS physician. The best-fit model for the LLR model estimated FFS physicians missed 819 (31.9%) cases while the model estimated NFFS physicians missed 1,086 (40.4%) cases. The best-fit MLR model estimated FFS physician missed 798 (31.5%) cases and estimated NFFS physicians missed 976 (39.7%) cases. Conclusion: There remains uncertainty as to whether physician billing claims data is complete due to missed capture of claims from NFFS physicians, which can have consequences for disease surveillance. This research demonstrated the feasibility of using three-source CR methods and observed that NFFS physicians were estimated to miss more cancer cases than FFS physicians in administrative data.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| 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 ».