Medicare Beneficiaries’ Perspectives on the Quality of Hospital Care and Their Implications for Value-Based Payment
Notice bibliographique
Résumé
Importance: Medicare's Hospital Value-Based Purchasing (HVBP) program adjusts hospital payments according to performance on 4 equally weighted quality domains: clinical outcomes, safety, patient experience, and efficiency. The assumption that performance on each domain is equally important may not reflect the preferences of Medicare beneficiaries. Objective: To estimate the relative importance (ie, weight) of the 4 quality domains in the HVBP program from the perspective of Medicare beneficiaries and the impact of using beneficiary value weights on incentive payments for hospitals enrolled in fiscal year 2019. Design, Setting, and Participants: An online survey was conducted in March 2022. A nationally representative sample of Medicare beneficiaries was recruited through Ipsos KnowledgePanel. Value weights were estimated using a discrete choice experiment that asked respondents to choose between 2 hospitals and indicate which they preferred. Hospitals were described using 6 attributes, including (1) clinical outcomes, (2) patient experience, (3) safety, (4) Medicare spending per patient, (5) distance, and (6) out-of-pocket cost. Data analysis was performed from April to November 2022. Main Outcomes and Measures: An effects-coded mixed logit regression model was used to estimate the relative importance of quality domains. HVBP program performance was linked to Medicare payment data in the Medicare Inpatient Hospitals by Provider and Service data set and hospital characteristics from the American Hospital Association Annual Survey data set, and the estimated impact of using beneficiary value weights on hospital payments was estimated. Results: A total of 1025 Medicare beneficiaries (518 women [51%]; 879 individuals [86%] aged ≥65 years; 717 White individuals [70%]) responded to the survey. A hospital's performance on clinical outcomes was most highly valued by beneficiaries (49%), followed by safety (22%), patient experience (21%), and efficiency (8%). Nearly twice as many hospitals would see a payment reduction when using beneficiary value weights than would see an increase (1830 vs 922 hospitals); however, the average net decrease was smaller (mean [SD], -$46 978 [$71 211]; median [IQR], -$24 628 [-$53 507 to -$9562]) than the comparable increase (mean [SD], $93 243 [$190 654]; median [IQR], $35 358 [$9906 to $97 348]). Hospitals seeing a net reduction with beneficiary value weights were more likely to be smaller, lower volume, nonteaching, and non-safety-net hospitals located in more deprived areas that served less complex patients. Conclusions and Relevance: This survey study of Medicare beneficiaries found that current HVBP program value weights do not reflect beneficiary preferences, suggesting that the use of beneficiary value weights may exacerbate disparities by rewarding larger, high-volume hospitals.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,004 | 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 ».