Task-adaptive multi-view learning with ordinal graph and continuous-score paths for public hospital performance stratification
- 1Southwest Medical University Affiliated Hospital, Luzhou
Abstract
Introduction: Public reporting provides repeated measures of hospital quality, spending, safety, and patient experience. Outcomes are ordered, and feature availability can change across releases. We evaluated Health–Insurance–Operational–Proxy synergy-aware Ordinal Graph Learning (HIOP–GL) for next-release performance stratification among publicly owned US hospitals.
Methods: Twelve Centers for Medicare & Medicaid Services (CMS) snapshots from 2023 to 2026 were aligned without concurrent targets. For the next-release overall hospital-rating task (PH-Star), development used 1,849 hospital-release observations from 341 hospitals, followed by 1,236 post-development observations from 338 hospitals. Because annual Hospital Value-Based Purchasing total performance scores (TPS) repeated within fiscal years, the corresponding PH-VBP task used FY2024 development ( n = 321) and FY2025 tuning ( n = 301) for selection. After architecture, loss, cutoff, and epoch choices were fixed using FY2024–FY2025 data, the model was refitted on both years (622 hospital-year records) and evaluated in FY2026 ( n = 308). PH-Star used a three-relation ordinal graph path specified before evaluation, whereas PH-VBP used a graph bypass, continuous TPS head, and tier cutoffs fixed from FY2024.
Results: Across five fits, PH-Star quadratic weighted kappa (QWK) was 0.816 ± 0.009 on the temporal test and 0.757 ± 0.013 after development. The two-year-refit PH-VBP model reached QWK 0.678 ± 0.021 in FY2026; ensemble QWK was 0.717 (95% CI 0.657–0.773). Hospital-disjoint and Census-region-disjoint cross-fitting yielded QWK 0.689 and 0.636. A PH-Star ensemble held constant after development achieved QWK 0.637 (95% CI 0.546–0.718; n = 229) in zero-overlap California non-public hospitals. Independent-authority checks found concordance with 2023 California multi-measure ratings ( n = 243, ρ = 0.247, 95% CI 0.125–0.366; Holm p = 0.0006) and 2024 New York all-payer mortality composites in 57 exact-linked hospitals (ρ = 0.451, 95% CI 0.195–0.663; Holm p = 0.0010). Concordance varied by criterion, with no supported California mortality association.
Discussion: HIOP-GL's clearest advantage was ordinal agreement in later-release evaluation. Macro-F1 and low-tier recall remained metric-dependent, and hospital-grouped PH-Star QWK was 0.661 vs. 0.730 for ordinal logistic. Ownership transport and multi-state independent-criterion concordance extend the assessment beyond development. The results support outcome-specific modeling for retrospective ordinal stratification; prospective same-outcome validation across independent systems remains necessary.
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BibTeX
@article{gan2026taskadaptive,
title = {{Task-adaptive multi-view learning with ordinal graph and continuous-score paths for public hospital performance stratification}},
author = {Yajie Gan and Dezhi Li},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1890759},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1890759/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Task-adaptive multi-view learning with ordinal graph and continuous-score paths for public hospital performance stratification
AU - Yajie Gan
AU - Dezhi Li
PY - 2026
DA - 2026/10/30
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1890759
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1890759/full
AB - Introduction: Public reporting provides repeated measures of hospital quality, spending, safety, and patient experience. Outcomes are ordered, and feature availability can change across releases. We evaluated Health–Insurance–Operational–Proxy synergy-aware Ordinal Graph Learning (HIOP–GL) for next-release performance stratification among publicly owned US hospitals. Methods: Twelve Centers for Medicare & Medicaid Services (CMS) snapshots from 2023 to 2026 were aligned without concurrent targets. For the next-release overall hospital-rating task (PH-Star), development used 1,849 hospital-release observations from 341 hospitals, followed by 1,236 post-development observations from 338 hospitals. Because annual Hospital Value-Based Purchasing total performance scores (TPS) repeated within fiscal years, the corresponding PH-VBP task used FY2024 development ( n = 321) and FY2025 tuning ( n = 301) for selection. After architecture, loss, cutoff, and epoch choices were fixed using FY2024–FY2025 data, the model was refitted on both years (622 hospital-year records) and evaluated in FY2026 ( n = 308). PH-Star used a three-relation ordinal graph path specified before evaluation, whereas PH-VBP used a graph bypass, continuous TPS head, and tier cutoffs fixed from FY2024. Results: Across five fits, PH-Star quadratic weighted kappa (QWK) was 0.816 ± 0.009 on the temporal test and 0.757 ± 0.013 after development. The two-year-refit PH-VBP model reached QWK 0.678 ± 0.021 in FY2026; ensemble QWK was 0.717 (95% CI 0.657–0.773). Hospital-disjoint and Census-region-disjoint cross-fitting yielded QWK 0.689 and 0.636. A PH-Star ensemble held constant after development achieved QWK 0.637 (95% CI 0.546–0.718; n = 229) in zero-overlap California non-public hospitals. Independent-authority checks found concordance with 2023 California multi-measure ratings ( n = 243, ρ = 0.247, 95% CI 0.125–0.366; Holm p = 0.0006) and 2024 New York all-payer mortality composites in 57 exact-linked hospitals (ρ = 0.451, 95% CI 0.195–0.663; Holm p = 0.0010). Concordance varied by criterion, with no supported California mortality association. Discussion: HIOP-GL's clearest advantage was ordinal agreement in later-release evaluation. Macro-F1 and low-tier recall remained metric-dependent, and hospital-grouped PH-Star QWK was 0.661 vs. 0.730 for ordinal logistic. Ownership transport and multi-state independent-criterion concordance extend the assessment beyond development. The results support outcome-specific modeling for retrospective ordinal stratification; prospective same-outcome validation across independent systems remains necessary.
SN - 2296-2565
ER -