What Drives Variation in Episode-of-care Payments for Primary TKA? An Analysis of Medicare Administrative Data
Notice bibliographique
Résumé
BACKGROUND: Episode-of-care payments are defined as a single lump-sum payment for all services associated with a single medical event or surgery and are designed to incentivize efficiency and integration among providers and healthcare systems. A TKA is considered an exemplar for an episode-of-care payment model by many policymakers, but data describing variation payments between hospitals for TKA are extremely limited. QUESTIONS/PURPOSES: We asked: (1) How much variation is there between hospitals in episode-of-care payments for primary TKA? (2) Is variation in payment explained by differences in hospital structural characteristics such as teaching status or geographic location, patient factors (age, sex, ethnicity, comorbidities), and discharge disposition during the postoperative period (home versus skilled nursing facility)? (3) After accounting for those factors, what proportion of the observed variation remains unexplained? METHODS: We used Medicare administrative data to identify fee-for-service beneficiaries who underwent a primary elective TKA in 2009. After excluding low-volume hospitals, we created longitudinal records for all patients undergoing TKAs in eligible hospitals encompassing virtually all payments by Medicare for a 120-day window around the TKA (30 days before to 90 days after). We examined payments for the preoperative, perioperative, and postdischarge periods based on the hospital where the TKA was performed. Confounding variables were controlled for using multivariate analyses to determine whether differences in hospital payments could be explained by differences in patient demographics, comorbidity, or hospital structural factors. RESULTS: There was considerable variation in payments across hospitals. Median (interquartile range) hospital preoperative, perioperative, postdischarge, and 120-day payments for patients who did not experience a complication were USD 623 (USD 516-768), USD 13,119 (USD 12,165-14,668), USD 8020 (USD 6403-9933), and USD 21,870 (USD 19,736-25,041), respectively. Variation cannot be explained by differences in hospital structure. Median (interquartile range) episode payments were greater for hospitals in the Northeast (USD 26,291 [22,377-30,323]) compared with the Midwest, South, and West (USD 20,614, [USD 18,592-22.968]; USD 21,584, [USD 19,663-23,941]; USD 22,421, [USD 20,317-25,860]; p < 0.001) and for teaching compared with nonteaching hospitals (USD 23,152 [USD 20,426-27,127] versus USD 21,336 [USD 19,352-23,846]; p < 0.001). Patient characteristics explained approximately 15% of the variance in hospital payments, hospital characteristics (teaching status, geographic region) explained 30% of variance, and approximately 55% of variance was not explained by either factor. CONCLUSIONS: There is much unexplained variation in episode-of-care payments at the hospital-level, suggesting opportunities for enhanced efficiency. Further research is needed to ensure an appropriate balance between such efficiencies and access to care. LEVEL OF EVIDENCE: Level II, economic analysis.
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 | 0,019 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».