{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:56:19Z","timestamp":1784120179042,"version":"3.55.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"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>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Well-organised electronic health records (EHR) are essential for high quality patient care, but EHR user interfaces can be cumbersome for entry of structured information, resulting in the majority of information being in free text rather than a structured form. This makes it difficult to retrieve information for clinical purposes and limits the research potential of the data. Natural language processing (NLP) at the point of care has been suggested as a way of improving data quality and completeness, but there is little evidence as to its effectiveness. We sought to generate such evidence by developing an open source, modular, configurable NLP system called MiADE, which is designed to integrate with an EHR. This paper describes the design of MiADE and the deployment at University College London Hospitals (UCLH), and is intended to benefit those who may wish to develop or implement a similar system elsewhere.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The MiADE system includes components to extract diagnoses, medications and allergies from a clinical note, and communicate with an EHR system in real time using Health Level 7 Clinical Document Architecture (HL7 CDA) messaging. This enables NLP results to be displayed to a clinician for verification before saving them to the patient\u2019s record. MiADE utilises the MedCAT library (part of the Cogstack family of NLP tools) for named entity recognition (NER) and linking to SNOMED CT, as well as context detection. MedCAT models underwent unsupervised and supervised training on patient notes from UCLH, achieving precision of 83.2% (95% CI 77.0, 88.1), and recall of 85.2% (95% CI 79.1, 89.8) for detection of diagnosis concepts. In simulation testing we found that MiADE reduced the time taken for clinicians to enter structured problem lists by 89%. We have commenced a trial implementation of MiADE at UCLH in live clinical use, integrated with the Epic EHR at UCLH.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We have developed an open source point of care NLP system and successfully integrated it with the EHR in live clinical use at a major hospital. Simulation testing has shown that our system significantly reduces the time taken for clinicians to enter structured diagnosis codes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-025-03195-1","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T13:13:07Z","timestamp":1759842787000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor)"],"prefix":"10.1186","volume":"25","author":[{"given":"Jennifer","family":"Jiang-Kells","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Brandreth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leilei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jack","family":"Ross","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogini","family":"Jani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrico","family":"Costanza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maisarah","family":"Amran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeljko","family":"Kraljevic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.M.N.S.","family":"Dilan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayathri","family":"Wijayarathne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Wickramaratne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Folkert W.","family":"Asselbergs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard J.B.","family":"Dobson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wai Keong","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anoop D.","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"issue":"11","key":"3195_CR1","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1056\/NEJM196803142781105","volume":"278","author":"LL Weed","year":"1968","unstructured":"Weed LL. Medical records that guide and teach. N Engl J Med. 1968;278(11):593\u2013600. https:\/\/doi.org\/10.1056\/NEJM196803142781105.","journal-title":"N Engl J Med"},{"issue":"1","key":"3195_CR2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41746-023-00994-6","volume":"7","author":"M Dugas","year":"2024","unstructured":"Dugas M, Blumenstock M, Dittrich T, Eisenmann U, Feder SC, Fritz-Kebede F, Kessler LJ, Klass M, Knaup P, Lehmann CU, Merzweiler A, Niklas C, Pausch TM, Zental N, Ganzinger M. Next-generation study databases require FAIR, EHR-integrated, and scalable electronic data capture for medical documentation and decision support. NPJ Digit Med. 2024;7(1):10. https:\/\/doi.org\/10.1038\/s41746-023-00994-6.","journal-title":"NPJ Digit Med"},{"key":"3195_CR3","doi-asserted-by":"publisher","unstructured":"Klappe ES, Heijmans J, Groen K, Ter Schure J, Cornet R, Keizer NF. Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: a single-blinded crossover randomized controlled trial. Int J Med Inf. 2023;180(105264), 105264 (https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105264.","DOI":"10.1016\/j.ijmedinf.2023.105264"},{"key":"3195_CR4","doi-asserted-by":"publisher","unstructured":"Poulos J, Zhu L, Shah AD. Data gaps in electronic health record (ehr) systems: an audit of problem list completeness during the covid-19 pandemic. Int J Multiling Med Inf. 2021;150, 104452 (https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104452.","DOI":"10.1016\/j.ijmedinf.2021.104452"},{"issue":"1","key":"3195_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.4104\/pcrj.2010.00078","volume":"20","author":"D Kalra","year":"2011","unstructured":"Kalra D, Fernando B. Approaches to enhancing the validity of coded data in electronic medical records. Prim Care Respir J. 2011;20(1):4\u20135. https:\/\/doi.org\/10.4104\/pcrj.2010.00078.","journal-title":"Prim Care Respir J"},{"key":"3195_CR6","doi-asserted-by":"publisher","unstructured":"Shah AD, Quinn NJ, Chaudhry A, Sullivan R, Costello J, O\u2019Riordan D, Hoogewerf J, Orton M, Foley L, Feger H, Williams JG. Recording problems and diagnoses in clinical care: developing guidance for healthcare professionals and system designers. BMJ Health Care inform. 2019;26(1). https:\/\/doi.org\/10.1136\/bmjhci-2019-100106.","DOI":"10.1136\/bmjhci-2019-100106"},{"key":"3195_CR7","first-page":"683","volume":"225","author":"J Millar","year":"2016","unstructured":"Millar J. The need for a global language - SNOMED CT introduction. Stud Health Technol inform. 2016;225:683\u201385.","journal-title":"Stud Health Technol inform"},{"issue":"1","key":"3195_CR8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ijmedinf.2008.07.002","volume":"78","author":"C Pearce","year":"2009","unstructured":"Pearce C, Dwan K, Arnold M, Phillips C, Trumble S. Doctor, patient and computer\u2013a framework for the new consultation. Int J Med Inf. 2009;78(1):32\u201338. https:\/\/doi.org\/10.1016\/j.ijmedinf.2008.07.002.","journal-title":"Int J Med Inf"},{"issue":"3","key":"3195_CR9","doi-asserted-by":"publisher","first-page":"149","DOI":"10.14236\/jhi.v11i3.563","volume":"11","author":"A Theadom","year":"2003","unstructured":"Theadom A, Lusignan S, Wilson E, Chan T. Using three-channel video to evaluate the impact of the use of the computer on the patient-centredness of the general practice consultation. Inf Prim Care. 2003;11(3):149\u201356. https:\/\/doi.org\/10.14236\/jhi.v11i3.563.","journal-title":"Inf Prim Care"},{"key":"3195_CR10","doi-asserted-by":"publisher","unstructured":"Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Gr\u00fcbner O, Rinaldi F, Wyl V. University of Zurich digital society initiative (UZH-DSI) health community: challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review. PLoS Digit Health. 2023;2(10), 0000347 (https:\/\/doi.org\/10.1371\/journal.pdig.0000347.","DOI":"10.1371\/journal.pdig.0000347"},{"issue":"1","key":"3195_CR11","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/s41746-022-00730-6","volume":"5","author":"H Wu","year":"2022","unstructured":"Wu H, Wang M, Wu J, Francis F, Chang Y-H, Shavick A, Dong H, Poon MTC, Fitzpatrick N, Levine AP, Slater LT, Handy A, Karwath A, Gkoutos GV, Chelala C, Shah AD, Stewart R, Collier N, Alex B, Whiteley W, Sudlow C, Roberts A, Dobson RJB. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ Digit Med. 2022;5(1):186. https:\/\/doi.org\/10.1038\/s41746-022-00730-6.","journal-title":"NPJ Digit Med"},{"key":"3195_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.10.005","author":"S Velupillai","year":"2018","unstructured":"Velupillai S, Suominen H, Liakata M, Roberts A, Shah AD, Morley K, Osborn D, Hayes J, Stewart R, Downs J, Chapman W, Dutta R. Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances. J Biomed Inf. 2018. https:\/\/doi.org\/10.1016\/j.jbi.2018.10.005.","journal-title":"J Biomed Inf"},{"issue":"Suppl 2","key":"3195_CR13","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/s12911-022-01927-1","volume":"22","author":"JM Havrilla","year":"2022","unstructured":"Havrilla JM, Singaravelu A, Driscoll DM, Minkovsky L, Helbig I, Medne L, Wang K, Krantz I, Desai BR. PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care. BMC Med. Inf. Decis. Mak. 2022;22(Suppl 2):198. https:\/\/doi.org\/10.1186\/s12911-022-01927-1.","journal-title":"BMC Med. Inf. Decis. Mak"},{"key":"3195_CR14","unstructured":"Zhou L. AHRQ grant final progress report: encoding and processing patient allergy information in EHRs. Technical report. 2018. https:\/\/digital.ahrq.gov\/sites\/default\/files\/docs\/citation\/r01hs022728-zhou-final-report-2018.pdf, Brigham and Women\u2019s Hospital, Harvard Medical School."},{"key":"3195_CR15","unstructured":"3M m*modal fluency direct. https:\/\/www.solventum.com\/en-us\/home\/h\/f\/b5005151015\/."},{"key":"3195_CR16","unstructured":"Clintegrity Physician coding. https:\/\/www.nuance.com\/healthcare\/provider-solutions\/coding-compliance\/physician-coding.html."},{"key":"3195_CR17","doi-asserted-by":"crossref","unstructured":"Kraljevic Z, Searle T, Shek A, Roguski L, Noor K, Bean D, Mascio A, Zhu L, Folarin AA, Roberts A, Bendayan R, Richardson MP, Stewart R, Shah AD, Wong WK, Ibrahim ZM, Teo JT, Dobson RJB. Multi-domain clinical natural language processing with medcat: the medical concept annotation toolkit. Corr Abs\/2010.01165. 2020. https:\/\/arxiv.org\/abs\/2010.01165.","DOI":"10.1016\/j.artmed.2021.102083"},{"key":"3195_CR18","doi-asserted-by":"publisher","unstructured":"Noor K, Roguski L, Bai X, Handy A, Klapaukh R, Folarin A, Romao L, Matteson J, Lea N, Zhu L, Asselbergs FW, Wong WK, Shah A, Dobson RJ. Deployment of a free-text analytics platform at a UK National health Service research hospital: cogStack at University college London hospitals. JMIR Med inform. 2022;10(8), 38122 (https:\/\/doi.org\/10.2196\/38122.","DOI":"10.2196\/38122"},{"issue":"5","key":"3195_CR19","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1093\/jamia\/ocx160","volume":"25","author":"H Wu","year":"2018","unstructured":"Wu H, Toti G, Morley KI, Ibrahim ZM, Folarin A, Jackson R, Kartoglu I, Agrawal A, Stringer C, Gale D, Gorrell G, Roberts A, Broadbent M, Stewart R, Dobson RJB. SemEHR: a general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research. J. Am. Med. Inf. assoc. 2018;25(5):530\u201337. https:\/\/doi.org\/10.1093\/jamia\/ocx160.","journal-title":"J. Am. Med. Inf. assoc"},{"key":"3195_CR20","unstructured":"Bio-YODIE. https:\/\/github.com\/GateNLP\/Bio-YODIE."},{"issue":"5","key":"3195_CR21","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1136\/jamia.2009.001560","volume":"17","author":"GK Savova","year":"2010","unstructured":"Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inf. assoc. 2010;17(5):507\u201313. https:\/\/doi.org\/10.1136\/jamia.2009.001560.","journal-title":"J. Am. Med. Inf. assoc"},{"key":"3195_CR22","unstructured":"MetaMap. http:\/\/metamap.nlm.nih.gov\/."},{"key":"3195_CR23","unstructured":"Shah AD: Rdiagnosislist: Manipulate SNOMED CT Diagnosis Lists. R package version 1.5.0. 2025. https:\/\/cran.r-project.org\/web\/packages\/Rdiagnosislist\/index.html."},{"key":"3195_CR24","unstructured":"Business Services Authority N. Dictionary of medicines and devices. https:\/\/dmd-browser.nhsbsa.nhs.uk\/."},{"key":"3195_CR25","doi-asserted-by":"crossref","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. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. (EMNLP-IJCNLP):, Stroudsburg, PA, USA: System Demonstrations. Association for Computational Linguistics; 2019, pp. 139\u201344). https:\/\/www.aclweb.org\/anthology\/D19-3024.","DOI":"10.18653\/v1\/D19-3024"},{"key":"3195_CR26","unstructured":"GitHub repository miade-datasets. https:\/\/github.com\/uclh-criu\/miade-datasets."},{"key":"3195_CR27","doi-asserted-by":"crossref","unstructured":"Kormilitzin A, Vaci N, Liu Q, Nevado-Holgado A. Med7: a transferable clinical natural language processing model for electronic health records. arXiv preprint arXiv:2003.01271 (2020.","DOI":"10.1016\/j.artmed.2021.102086"},{"issue":"3","key":"3195_CR28","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1002\/pds.1151","volume":"15","author":"AD Shah","year":"2006","unstructured":"Shah AD, Martinez C. An algorithm to derive a numerical daily dose from unstructured text dosage instructions. Pharmacoepidemiol Drug Saf. 2006;15(3):161\u201366.","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"3195_CR29","unstructured":"White S. Clinical risk management: Its application in the manufacture of health it systems - specification. Technical report. 2018. https:\/\/digital.nhs.uk\/data-and-information\/information-standards\/information-standards-and-data-collections-including-extractions\/publications-and-notifications\/standards-and-collections\/dcb0129-clinical-risk-management-its-application-in-the-manufacture-of-health-it-systems, NHS Digital."},{"key":"3195_CR30","unstructured":"White S. Clinical risk management: Its application in the deployment and use of health it systems - specification. Technical report. 2018. https:\/\/digital.nhs.uk\/data-and-information\/information-standards\/information-standards-and-data-collections-including-extractions\/publications-and-notifications\/standards-and-collections\/dcb0160-clinical-risk-management-its-application-in-the-deployment-and-use-of-health-it-systems, NHS Digital."},{"key":"3195_CR31","doi-asserted-by":"publisher","unstructured":"Fraile Navarro D, Ijaz K, Rezazadegan D, Rahimi-Ardabili H, Dras M, Coiera E, Berkovsky S. Clinical named entity recognition and relation extraction using natural language processing of medical free text: a systematic review. Int J Med Inf. 2023;177(105122), 105122 (https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105122.","DOI":"10.1016\/j.ijmedinf.2023.105122"},{"key":"3195_CR32","doi-asserted-by":"publisher","unstructured":"Soroush A, Glicksberg BS, Zimlichman E, Barash Y, Freeman R, Charney AW, Nadkarni GN, Klang E. Large language models are poor medical coders \u2014 benchmarking of medical code querying. In: NEJM AI. Vol. 1(5), 2300040 (Publisher: Massachusetts Medical Society; 2024. https:\/\/doi.org\/10.1056\/AIdbp2300040. Accessed 2024-08-09.","DOI":"10.1056\/AIdbp2300040"},{"key":"3195_CR33","unstructured":"Zhao WX, Zhou K, Li J, Tang T, Wang X, Hou Y, Min Y, Zhang B, Zhang J, Dong Z, Du Y, Yang C, Chen Y, Chen Z, Jiang J, Ren R, Li Y, Tang X, Liu Z, Liu P, Nie J-Y, Wen J-R. A survey of large language models. 2023. https:\/\/arxiv.org\/abs\/2303.18223."},{"key":"3195_CR34","unstructured":"Kojima T, Gu SS, Reid M, Matsuo Y, Iwasawa Y. Large language models are Zero-Shot Reasoners. 2023. https:\/\/arxiv.org\/abs\/2205.11916."},{"key":"3195_CR35","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q, Zhou D. Chain-of-thought prompting elicits reasoning in large language models. 2023. https:\/\/arxiv.org\/abs\/2201.11903."},{"key":"3195_CR36","doi-asserted-by":"crossref","unstructured":"Xiong G, Jin Q, Lu Z, Zhang A. Benchmarking retrieval-augmented generation for medicine. 2024. https:\/\/arxiv.org\/abs\/2402.13178.","DOI":"10.18653\/v1\/2024.findings-acl.372"},{"key":"3195_CR37","unstructured":"Sudarshan M, Shih S, Yee E, Yang A, Zou J, Chen C, Zhou Q, Chen L, Singhal C, Shih G. Agentic LLM workflows for generating patient-friendly medical reports. 2024. https:\/\/arxiv.org\/abs\/2408.01112."},{"key":"3195_CR38","doi-asserted-by":"crossref","unstructured":"Harris S, Bonnici T, Keen T, Lilaonitkul W, White MJ, Swanepoel N. Clinical deployment environments: five pillars of translational machine learning for health. 2022;4.","DOI":"10.3389\/fdgth.2022.939292"},{"key":"3195_CR39","unstructured":"Garde S. Ocean health Systems: OpenEHR Problem\/Diagnosis archetype. https:\/\/ckm.openehr.org\/ckm\/archetypes\/1013.1.169. Accessed: 2021-5-10. https:\/\/ckm.openehr.org\/ckm\/archetypes\/1013.1.169."},{"key":"3195_CR40","unstructured":"Condition - FHIR v4.0.1. https:\/\/www.hl7.org\/fhir\/condition.html. Accessed: 2021-2-23. https:\/\/www.hl7.org\/fhir\/condition.html."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03195-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03195-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03195-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T01:02:06Z","timestamp":1759885326000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03195-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3195"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03195-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4925228\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,7]]},"assertion":[{"value":"16 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The development of MiADE was undertaken as part of Cogstack service development within University College London Hospitals NHS Trust and has approval of relevant Trust committees. The MiADE evaluation study was approved by the Hampshire ethics committee (23\/SC\/0221: Amendment 1, March 2024) and is registered on ISRCTN (\n                      \n                      ). All clinicians participating in the study gave written informed consent. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"365"}}