{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:00:46Z","timestamp":1771513246926,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose of Review<\/jats:title>\n                <jats:p>Embedding machine learning workflows into real-world hospital environments is essential to ensure model alignment with clinical workflows and real-world data. Many non-healthcare industries undergoing digital transformation have already developed data labelling and data quality management services as a vertically integrated business process.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Recent Findings<\/jats:title>\n                <jats:p>In this paper, we describe our experiences developing and implementing a first-of-its-kind clinical NLP (natural language processing) service in the National Health Service, United Kingdom using parallel harmonised platforms. We report on our work developing clinical NLP resources and implementation framework to distil expert clinical knowledge into our NLP models.\u00a0To date, we have amassed over 26,086 annotations spanning 556 SNOMED CT concepts working with secondary care specialties.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Summary<\/jats:title>\n                <jats:p>Our integrated language modelling service has delivered numerous clinical and operational use-cases using named entity recognition (NER). Such services improve efficiency of healthcare delivery and drive downstream data-driven technologies. We believe it will only be a matter of time before NLP services become an integral part of healthcare providers.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-024-02713-x","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T09:14:03Z","timestamp":1732612443000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Natural language processing data services for healthcare providers"],"prefix":"10.1186","volume":"24","author":[{"given":"Joshua","family":"Au Yeung","sequence":"first","affiliation":[]},{"given":"Anthony","family":"Shek","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Searle","sequence":"additional","affiliation":[]},{"given":"Zeljko","family":"Kraljevic","sequence":"additional","affiliation":[]},{"given":"Vlad","family":"Dinu","sequence":"additional","affiliation":[]},{"given":"Mart","family":"Ratas","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Al-Agil","sequence":"additional","affiliation":[]},{"given":"Aleksandra","family":"Foy","sequence":"additional","affiliation":[]},{"given":"Barbara","family":"Rafferty","sequence":"additional","affiliation":[]},{"given":"Vitaliy","family":"Oliynyk","sequence":"additional","affiliation":[]},{"given":"James T.","family":"Teo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"2713_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4258\/hir.2019.25.1.1","volume":"25","author":"H-J Kong","year":"2019","unstructured":"Kong H-J. Managing unstructured Big Data in Healthcare System. Healthc Inf Res. 2019;25:1\u20132.","journal-title":"Healthc Inf Res"},{"key":"2713_CR2","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1002\/ejhf.1924","volume":"22","author":"DM Bean","year":"2020","unstructured":"Bean DM, et al. Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID-19 infection in a multi-site UK acute hospital trust. Eur J Heart Fail. 2020;22:967\u201374.","journal-title":"Eur J Heart Fail"},{"key":"2713_CR3","doi-asserted-by":"publisher","first-page":"4090","DOI":"10.1111\/ene.15071","volume":"28","author":"A Shek","year":"2021","unstructured":"Shek A, et al. Machine learning-enabled multitrust audit of stroke comorbidities using natural language processing. Eur J Neurol. 2021;28:4090\u20137.","journal-title":"Eur J Neurol"},{"key":"2713_CR4","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/s41746-021-00406-7","volume":"4","author":"JTH Teo","year":"2021","unstructured":"Teo JTH, et al. Real-time clinician text feeds from electronic health records. NPJ Digit Med. 2021;4:35.","journal-title":"NPJ Digit Med"},{"key":"2713_CR5","doi-asserted-by":"publisher","unstructured":"Kraljevic Z et al. 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI) (IEEE, 2023). https:\/\/doi.org\/10.1109\/ichi57859.2023.00098","DOI":"10.1109\/ichi57859.2023.00098"},{"key":"2713_CR6","doi-asserted-by":"publisher","first-page":"e281","DOI":"10.1016\/S2589-7500(24)00025-6","volume":"6","author":"Z Kraljevic","year":"2024","unstructured":"Kraljevic Z, et al. Foresight\u2014a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study. Lancet Digit Health. 2024;6:e281\u201390.","journal-title":"Lancet Digit Health"},{"key":"2713_CR7","doi-asserted-by":"publisher","first-page":"e0000218","DOI":"10.1371\/journal.pdig.0000218","volume":"2","author":"DM Bean","year":"2023","unstructured":"Bean DM, Kraljevic Z, Shek A, Teo J, Dobson RJ. B. Hospital-wide natural language processing summarising the health data of 1 million patients. PLOS Digit Health. 2023;2:e0000218.","journal-title":"PLOS Digit Health"},{"key":"2713_CR8","doi-asserted-by":"crossref","unstructured":"Johnson AEW et al. MIMIC-III, a freely accessible critical care database. Sci Data 3, (2016).","DOI":"10.1038\/sdata.2016.35"},{"key":"2713_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-017-0580-8","volume":"18","author":"R Jackson","year":"2018","unstructured":"Jackson R, et al. CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust Hospital. BMC Med Inf Decis Mak. 2018;18:1\u201313.","journal-title":"BMC Med Inf Decis Mak"},{"key":"2713_CR10","doi-asserted-by":"publisher","unstructured":"Kraljevic Z et al. MedCAT -- Medical Concept Annotation Tool. (2019) https:\/\/doi.org\/10.48550\/ARXIV.1912.10166","DOI":"10.48550\/ARXIV.1912.10166"},{"key":"2713_CR11","doi-asserted-by":"publisher","unstructured":"Searle T, Kraljevic Z, Bendayan R, Bean D, Dobson R, MedCATTrainer. A biomedical free text annotation interface with active learning and research use case specific customisation. (2019) https:\/\/doi.org\/10.48550\/ARXIV.1907.07322","DOI":"10.48550\/ARXIV.1907.07322"},{"key":"2713_CR12","doi-asserted-by":"publisher","unstructured":"Kraljevic Z et al. Validating transformers for redaction of text from electronic health records in real-world healthcare. (2023) https:\/\/doi.org\/10.48550\/ARXIV.2310.04468","DOI":"10.48550\/ARXIV.2310.04468"},{"key":"2713_CR13","doi-asserted-by":"crossref","unstructured":"Dong H et al. Automated clinical coding: what, why, and where we are? NPJ Digit Med 5, (2022).","DOI":"10.1038\/s41746-022-00705-7"},{"key":"2713_CR14","doi-asserted-by":"publisher","unstructured":"Brown TB et al. Language Models are Few-Shot Learners. (2020) https:\/\/doi.org\/10.48550\/ARXIV.2005.14165","DOI":"10.48550\/ARXIV.2005.14165"},{"key":"2713_CR15","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal K, et al. Large language models encode clinical knowledge. Nature. 2023;620:172\u201380.","journal-title":"Nature"},{"key":"2713_CR16","doi-asserted-by":"publisher","first-page":"1161098","DOI":"10.3389\/fdgth.2023.1161098","volume":"5","author":"J Au Yeung","year":"2023","unstructured":"Au Yeung J, et al. AI chatbots not yet ready for clinical use. Front Digit Health. 2023;5:1161098.","journal-title":"Front Digit Health"},{"key":"2713_CR17","doi-asserted-by":"publisher","unstructured":"Maynez J, Narayan S, Bohnet B, McDonald R. On Faithfulness and Factuality in Abstractive Summarization. (2020) https:\/\/doi.org\/10.48550\/ARXIV.2005.00661","DOI":"10.48550\/ARXIV.2005.00661"},{"key":"2713_CR18","doi-asserted-by":"publisher","unstructured":"Bai Y et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. (2022) https:\/\/doi.org\/10.48550\/ARXIV.2204.05862","DOI":"10.48550\/ARXIV.2204.05862"},{"key":"2713_CR19","doi-asserted-by":"publisher","unstructured":"Touvron H et al. LLaMA: Open and efficient foundation language models. (2023) https:\/\/doi.org\/10.48550\/ARXIV.2302.13971","DOI":"10.48550\/ARXIV.2302.13971"},{"key":"2713_CR20","unstructured":"Zeljko, A Large Language Model for Healthcare. AI for Healthcare https:\/\/aiforhealthcare.substack.com\/p\/a-large-language-model-for-healthcare (2023)."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02713-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02713-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02713-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T10:03:54Z","timestamp":1732615434000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02713-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2713"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02713-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,26]]},"assertion":[{"value":"29 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"NER\u2009+\u2009L experiments use freely available open-access datasets accessible by data owners. SNOMED-CT and UMLS licences were obtained by all users at all hospital sites. Site specific ethics is listed below. KCH: This project operated under London South East Research Ethics Committee approval (reference 24\/LO\/0057) granted to the King\u2019s Electronic Records Research Interface (KERRI); specific work on research on natural language processing for clinical coding was reviewed with expert patient input on the KERRI committee with Caldicott Guardian oversight. Governance is provided for all projects and dissemination through a patient-led oversight committee. Individual consent from participants was not required as the data is de-identified and used in a data-secure format, with all personal health information redacted. All patients who do not wish for their data to be used have the choice of national data opt-out (NDOO) which excludes their data from being used for subsequent research or audit projects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable: No informed consent was sought from individual persons, as no individual persons\u2019 data is presented in this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"JTT has previously received research grant support from Innovate UK, NHSX, Office of Life Sciences, NIHR, Health Data Research UK, Bristol-Meyers-Squibb and Pfizer; has received honorarium from Bayer, Bristol-Meyers-Squibb and Goldman Sachs; holds stock in Amazon, Alphabet, Nvidia; and receives royalties from Wiley-Blackwell Publishing. Other authors have no conflcts of interest to declare. Other authors have no conflicts of interest to declare.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"356"}}