{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FDFBYV2CIHYF4TIYAUKAWACSQY","short_pith_number":"pith:FDFBYV2C","schema_version":"1.0","canonical_sha256":"28ca1c574241f05e4d1805140b0052861e4b5500e424b525a3e4f836345c774f","source":{"kind":"arxiv","id":"2604.14215","version":2},"attestation_state":"computed","paper":{"title":"PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PriHA uses query optimization and dual retrieval to give more accurate answers to Hong Kong primary healthcare questions than standard LLMs.","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Hao Chen, Liangjun Jiang, Richard Wai Cheung Chan, Shanru Lin, Wenqi Fan, Ya-nan Ma","submitted_at":"2026-04-10T06:47:43Z","abstract_excerpt":"To address the unsustainable rise in public health expenditures, the Hong Kong SAR Government is shifting its strategic focus to primary healthcare and encouraging citizens to use community resources to self-manage their health. However, official clinical guidelines are fragmented across disparate departments and formats, creating significant access barriers. While general-purpose Large Language Models (LLMs) such as ChatGPT and DeepSeek offer potential solutions for information accessibility, they are prone to generating factually inaccurate content due to a lack of localized and domain-speci"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2604.14215","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-10T06:47:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"10cec891981e910dc87501172178eb8f38604fa804406a9e49de02441369e4d7","abstract_canon_sha256":"3653125cb558be92a8c3a2bf1465318ab500c5f44b24e898ee458429d63af215"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:11.485514Z","signature_b64":"xEbJs3djMLpJXCvv5+Hlo6rgF8Ht+qxy8y2BOlq79hFxGpUMXpZlrULFOogvCFSQ7OoZEjisCouA2HSrfvYzDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28ca1c574241f05e4d1805140b0052861e4b5500e424b525a3e4f836345c774f","last_reissued_at":"2026-05-20T00:03:11.484441Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:11.484441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PriHA uses query optimization and dual retrieval to give more accurate answers to Hong Kong primary healthcare questions than standard LLMs.","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Hao Chen, Liangjun Jiang, Richard Wai Cheung Chan, Shanru Lin, Wenqi Fan, Ya-nan Ma","submitted_at":"2026-04-10T06:47:43Z","abstract_excerpt":"To address the unsustainable rise in public health expenditures, the Hong Kong SAR Government is shifting its strategic focus to primary healthcare and encouraging citizens to use community resources to self-manage their health. However, official clinical guidelines are fragmented across disparate departments and formats, creating significant access barriers. While general-purpose Large Language Models (LLMs) such as ChatGPT and DeepSeek offer potential solutions for information accessibility, they are prone to generating factually inaccurate content due to a lack of localized and domain-speci"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Comprehensive experiments and a detailed case study demonstrate that our proposed method can outperform both ablations and baseline in terms of accuracy and clarity.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the mixed-source retrieval in DRAG will consistently surface accurate, up-to-date, and complete guideline fragments without introducing new factual errors or omissions for medical queries.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PriHA combines query optimization with a Dual Retrieval Augmented Generation pipeline to improve accuracy and clarity of LLM responses on fragmented Hong Kong primary care guidelines.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PriHA uses query optimization and dual retrieval to give more accurate answers to Hong Kong primary healthcare questions than standard LLMs.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f94aceea7084c8cec53f223ddba4f95be38b729855c780c8ba1bcb6961577992"},"source":{"id":"2604.14215","kind":"arxiv","version":2},"verdict":{"id":"4e6a6443-9c8c-49f6-9914-ead55a343abd","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T17:06:38.192160Z","strongest_claim":"Comprehensive experiments and a detailed case study demonstrate that our proposed method can outperform both ablations and baseline in terms of accuracy and clarity.","one_line_summary":"PriHA combines query optimization with a Dual Retrieval Augmented Generation pipeline to improve accuracy and clarity of LLM responses on fragmented Hong Kong primary care guidelines.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the mixed-source retrieval in DRAG will consistently surface accurate, up-to-date, and complete guideline fragments without introducing new factual errors or omissions for medical queries.","pith_extraction_headline":"PriHA uses query optimization and dual retrieval to give more accurate answers to Hong Kong primary healthcare questions than standard LLMs."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.14215/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2604.14215","created_at":"2026-05-20T00:03:11.484585+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.14215v2","created_at":"2026-05-20T00:03:11.484585+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.14215","created_at":"2026-05-20T00:03:11.484585+00:00"},{"alias_kind":"pith_short_12","alias_value":"FDFBYV2CIHYF","created_at":"2026-05-20T00:03:11.484585+00:00"},{"alias_kind":"pith_short_16","alias_value":"FDFBYV2CIHYF4TIY","created_at":"2026-05-20T00:03:11.484585+00:00"},{"alias_kind":"pith_short_8","alias_value":"FDFBYV2C","created_at":"2026-05-20T00:03:11.484585+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY","json":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY.json","graph_json":"https://pith.science/api/pith-number/FDFBYV2CIHYF4TIYAUKAWACSQY/graph.json","events_json":"https://pith.science/api/pith-number/FDFBYV2CIHYF4TIYAUKAWACSQY/events.json","paper":"https://pith.science/paper/FDFBYV2C"},"agent_actions":{"view_html":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY","download_json":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY.json","view_paper":"https://pith.science/paper/FDFBYV2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.14215&json=true","fetch_graph":"https://pith.science/api/pith-number/FDFBYV2CIHYF4TIYAUKAWACSQY/graph.json","fetch_events":"https://pith.science/api/pith-number/FDFBYV2CIHYF4TIYAUKAWACSQY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY/action/storage_attestation","attest_author":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY/action/author_attestation","sign_citation":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY/action/citation_signature","submit_replication":"https://pith.science/pith/FDFBYV2CIHYF4TIYAUKAWACSQY/action/replication_record"}},"created_at":"2026-05-20T00:03:11.484585+00:00","updated_at":"2026-05-20T00:03:11.484585+00:00"}