Academic Journal
A large language model-enhanced knowledge graph framework for text-implied public health policy gap screening: digital health executability, behavioral accessibility, and service-support coverage.
| Τίτλος: | A large language model-enhanced knowledge graph framework for text-implied public health policy gap screening: digital health executability, behavioral accessibility, and service-support coverage. |
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| Συγγραφείς: | Wang X; Carey Business School, Johns Hopkins University, Baltimore, MD, United States., Lu J; School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, Shaanxi, China. |
| Πηγή: | Frontiers in public health [Front Public Health] 2026 Sep 07; Vol. 14, pp. 1937335. Date of Electronic Publication: 2026 Sep 07 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Lausanne : Frontiers Editorial Office |
| Ιατρικοί όροι (MeSH): | Large Language Models* , Health Policy* , Public Health*, Digital Health ; Humans ; Policy Making |
| Περίληψη: | Screening for text-implied structural gaps in policy documents is an important component of public health policy review, particularly when implementation relies on online portals, digital identity verification, remote-care platforms, or self-service processes. However, existing text classifiers, large language model prompting methods, and retrieval-based approaches often provide document-level predictions or general explanations without explicitly representing key elements, relations, and potentially missing links in policy implementation. This study proposes Policy Review Process-Knowledge Graph (PRP-KG), a large language model-enhanced knowledge graph framework for identifying structural-gap review signals across policy clauses, implementation processes, and target populations. PRP-KG segments policy documents into clauses, extracts implementation-related elements using a predefined policy-execution schema, and grounds the extracted entities and relations in source evidence spans. The validated elements are then organized into a three-layer knowledge graph. A graph-consistency feedback mechanism revises missing or schema-inconsistent triples, after which structural-gap patterns identify text-implied gaps in execution specification, accessibility safeguards, and service-support coverage. Experiments on policy-text annotation benchmarks constructed from public sources show that, under the evaluated settings, PRP-KG achieves lower prediction error and more accurate identification of high-priority text-implied review signals than most comparison methods. Controlled analyses indicate comparatively stable performance under the evaluated perturbations and provide evidence-linked outputs that can be inspected by policy reviewers. (Copyright © 2026 Wang and Lu.) |
| Competing Interests: | The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. |
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| Contributed Indexing: | Keywords: behavioral accessibility; digital health policy; evidence-linked explanation; knowledge graph; large language models; policy executability; policy-text screening; service-support coverage |
| Entry Date(s): | Date Created: 20260922 Date Completed: 20260922 Latest Revision: 20260923 |
| Update Code: | 20260923 |
| PubMed Central ID: | PMC13590772 |
| DOI: | 10.3389/fpubh.2026.1937335 |
| PMID: | 42769437 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2296-2565 |
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| DOI: | 10.3389/fpubh.2026.1937335 |