{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:07:37Z","timestamp":1783577257085,"version":"3.55.0"},"reference-count":27,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Large language models (LLM) still face challenges in accurately extracting and summarizing medical information from EHR and EMR. The variability in EHR and EMR formats across institutions further complicates information integration. Moreover, doctors need to spend a lot of time reviewing patient information, which affects the efficiency and effectiveness of clinical decision-making.\n                    <jats:bold>Objective:<\/jats:bold>\n                    This study aims to develop a medical record summarization system that uses the innovative X-RAG technique with GPT-4o to extract medical information from EHR and EMR and convert them into structured FHIR format. The system ultimately generates a doctor-friendly report to improve the efficiency and effectiveness of clinical decision-making.\n                    <jats:bold>Methods:<\/jats:bold>\n                    We propose an innovative X-RAG, which adds page-based chunking, chunk filtering, and guided extraction prompting to the basic framework of RAG and combines it with GPT-4o to extract medical measurement data, diagnostic reports, and medication history records from EHR and EMR with high accuracy.\n                    <jats:bold>Results:<\/jats:bold>\n                    The system achieved 96.5% accuracy in medical data extraction and reduced approximately 40% of the time doctors spend reviewing patient information in clinical applications.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    The proposed system improves the efficiency and effectiveness of clinical decision-making and provides a valuable tool to optimize medical information management and clinical workflows.\n                  <\/jats:p>","DOI":"10.1177\/14604582251381233","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T20:31:46Z","timestamp":1758141106000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["An innovative X-RAG technique combined with GPT-4o for summarizing medical information from EHR and EMR to assist doctors in clinical decision-making effectively and efficiently"],"prefix":"10.1177","volume":"31","author":[{"given":"Jhing-Fa","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9519-0022","authenticated-orcid":false,"given":"Che-Chuan","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Te-Ming","family":"Chiang","sequence":"additional","affiliation":[{"name":"Doctors\u2019 Doctor Clinic, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzu-Chun","family":"Yeh","sequence":"additional","affiliation":[{"name":"DeepWave Co. Ltd, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Cheng","sequence":"additional","affiliation":[{"name":"C-Media Electronics Inc, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan-Teh","family":"Lee","sequence":"additional","affiliation":[{"name":"Doctors\u2019 Doctor Clinic, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-I","family":"Chen","sequence":"additional","affiliation":[{"name":"Doctors\u2019 Doctor Clinic, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"e_1_3_5_2_2","unstructured":"Li L Zhou J Gao Z et al. A scoping review of using large language models (LLMs) to investigate electronic health records (EHRs). 2024. arXiv [Preprint]. https:\/\/arxiv.org\/abs\/2405.03066 (accessed 1 July 2025)."},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03074-8"},{"key":"e_1_3_5_4_2","doi-asserted-by":"crossref","unstructured":"Garcia-Carmona AM Prieto ML Puertas E et al. Enhanced medical data extraction: leveraging LLMs for accurate retrieval of patient information from medical reports. 2024. Preprints.org [Preprint]. https:\/\/www.preprints.org\/manuscript\/202407.0986\/v1 (accessed 1 July 2025).","DOI":"10.20944\/preprints202407.0986.v1"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.25953"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv189"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.2196\/21929"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.2196\/35724"},{"key":"e_1_3_5_9_2","unstructured":"Alsaqer BF Alsaqer AF Asif A. Towards system modelling to support diseases data extraction from the electronic health records for physicians\u2019 research activities. 2024. arXiv [Preprint]. https:\/\/arxiv.org\/abs\/2404.01218 (accessed 1 July 2025)."},{"issue":"2","key":"e_1_3_5_10_2","first-page":"552","article-title":"Artificial intelligence\u2019s transformative role in management of electronic medical records","volume":"5","author":"Chughtai S","year":"2024","unstructured":"Chughtai S. Artificial intelligence\u2019s transformative role in management of electronic medical records. J Pak Soc Intern Med 2024; 5(2): 552\u2013557.","journal-title":"J Pak Soc Intern Med"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac040"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-022-02397-7"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126585"},{"key":"e_1_3_5_14_2","doi-asserted-by":"crossref","unstructured":"Agrawal M Hegselmann S Lang H et al. Large language models are few-shot clinical information extractors. In: Goldberg Y Kozareva Z Zhang Y (eds). Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics pp. 1998\u20132022.","DOI":"10.18653\/v1\/2022.emnlp-main.130"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104458"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaf008"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000877"},{"key":"e_1_3_5_18_2","unstructured":"Lewis P Perez E Piktus A et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Larochelle H Ranzato M Hadsell R et al. (eds). 34th Conference on Neural Information Processing Systems. Curran Associates Inc pp. 9459\u20139474."},{"key":"e_1_3_5_19_2","unstructured":"Eibich M Nagpal S Fred-Ojala A. ARAGOG: advanced RAG output grading. 2024. arXiv [Preprint]. https:\/\/arxiv.org\/abs\/2404.01037 (accessed 1 July 2025)."},{"key":"e_1_3_5_20_2","volume-title":"Advanced RAG 01: small to big retrieval","author":"Yang S","year":"2023","unstructured":"Yang S. Advanced RAG 01: small to big retrieval. Medium, 2023. https:\/\/medium.com\/towards-data-science\/advanced-rag-01-small-to-big-retrieval-172181b396d4 (accessed 1 July 2025)."},{"key":"e_1_3_5_21_2","unstructured":"Liu J. A new document summary index for LLM-powered QA systems. 2023. https:\/\/www.llamaindex.ai\/blog\/a-new-document-summary-index-for-llm-powered-qa-systems-9a32ece2f9ec (accessed 1 July 2025)."},{"key":"e_1_3_5_22_2","unstructured":"Gao L Ma X Lin J et al. Precise zero-shot dense retrieval without relevance labels. In: Rogers A Boyd-Graber J Okazaki N (eds). Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics pp. 1762\u20131777."},{"key":"e_1_3_5_23_2","unstructured":"LangChain. Query transformations. 2023. https:\/\/blog.langchain.dev\/query-transformations\/ (accessed 1 July 2025)."},{"key":"e_1_3_5_24_2","doi-asserted-by":"crossref","unstructured":"Carbonell J Goldstein J. The use of MMR diversity-based reranking for reordering documents and producing summaries. In: Croft WB Moffat A van Rijsbergen CJ et al. (eds). Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press pp. 335\u2013336.","DOI":"10.1145\/290941.291025"},{"key":"e_1_3_5_25_2","unstructured":"Pinecone. Rerankers and two-stage retrieval. 2023. https:\/\/www.pinecone.io\/learn\/series\/rag\/rerankers\/ (accessed 1 July 2025)."},{"key":"e_1_3_5_26_2","unstructured":"Liu J. Using LLM\u2019s for retrieval and reranking. 2023. https:\/\/www.llamaindex.ai\/blog\/using-llms-for-retrieval-and-reranking-23cf2d3a14b6 (accessed 1 July 2025)."},{"key":"e_1_3_5_27_2","volume-title":"intfloat\/multilingual-e5-large","author":"Wang L","year":"2024","unstructured":"Wang L. intfloat\/multilingual-e5-large. Hugging Face, 2024. https:\/\/huggingface.co\/intfloat\/multilingual-e5-large (accessed 1 July 2025)."},{"key":"e_1_3_5_28_2","volume-title":"ihower\/zh-tw-embedding-model-benchmark","author":"Chang WT","year":"2024","unstructured":"Chang WT. ihower\/zh-tw-embedding-model-benchmark. GitHub, 2024. https:\/\/github.com\/ihower\/zh-tw-embedding-model-benchmark (accessed 1 July 2025)."}],"container-title":["Health Informatics Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582251381233","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14604582251381233","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582251381233","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:28:58Z","timestamp":1777501738000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14604582251381233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1177\/14604582251381233"],"URL":"https:\/\/doi.org\/10.1177\/14604582251381233","relation":{},"ISSN":["1460-4582","1741-2811"],"issn-type":[{"value":"1460-4582","type":"print"},{"value":"1741-2811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]},"article-number":"14604582251381233"}}