{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nDiscussion: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Southwest Medical University Affiliated Hospital, Luzhou"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yajie Gan"
    },
    {
      "affs": [
        0
      ],
      "name": "Dezhi Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ts8zr960e576.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1890759",
  "fulltext_url": null,
  "id": "1ts8zr960e576",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1890759/full",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/1ts8zr960e576.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:21:09Z",
      "native_id": "10.3389/fpubh.2026.1890759",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1890759/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2296-2565",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Task-adaptive multi-view learning with ordinal graph and continuous-score paths for public hospital performance stratification",
  "title_text": "Task-adaptive multi-view learning with ordinal graph and continuous-score paths for public hospital performance stratification",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:21:06Z",
  "url": "https://research.draconitic.com/paper/1ts8zr960e576",
  "venue": "Frontiers in Public Health",
  "versions": []
}