{"id":"https://openalex.org/W7147301572","doi":"https://doi.org/10.48550/arxiv.2603.29709","title":"Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Coding","display_name":"Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Coding","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147301572","doi":"https://doi.org/10.48550/arxiv.2603.29709"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.29709","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061228375","display_name":"Joakim Edin","orcid":"https://orcid.org/0000-0003-1005-8276"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Edin, Joakim","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040570766","display_name":"Andreas Geert Motzfeldt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Motzfeldt, Andreas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028331418","display_name":"Simon Flachs","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Flachs, Simon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132597834","display_name":"Lars Maal\u00f8e","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maal\u00f8e, Lars","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061228375"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.5934000015258789,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.5934000015258789,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14400","display_name":"Medical Coding and Health Information","score":0.18979999423027039,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.04560000076889992,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5806999802589417},{"id":"https://openalex.org/keywords/documentation","display_name":"Documentation","score":0.5185999870300293},{"id":"https://openalex.org/keywords/symphony","display_name":"Symphony","score":0.4936999976634979},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4320000112056732},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.33489999175071716},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.33239999413490295},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.3255000114440918}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6723999977111816},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5806999802589417},{"id":"https://openalex.org/C56666940","wikidata":"https://www.wikidata.org/wiki/Q788790","display_name":"Documentation","level":2,"score":0.5185999870300293},{"id":"https://openalex.org/C16277566","wikidata":"https://www.wikidata.org/wiki/Q9734","display_name":"Symphony","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35510000586509705},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33799999952316284},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.2745000123977661},{"id":"https://openalex.org/C42525527","wikidata":"https://www.wikidata.org/wiki/Q1209955","display_name":"Formative assessment","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0,87],"coding":[1,63,112,122],"translates":[2],"free-text":[3],"clinical":[4,29,105,180],"documentation":[5],"into":[6],"standardized":[7],"codes":[8,51,60],"drawn":[9],"from":[10,52],"classification":[11],"systems":[12,64],"that":[13,91,136],"contain":[14],"tens":[15],"of":[16,18,50],"thousands":[17],"entries":[19],"and":[20,31,39,124,145,153,160],"are":[21],"updated":[22],"annually.":[23],"It":[24],"is":[25],"central":[26],"to":[27,45,58,110,118,125,133],"billing,":[28],"research,":[30],"quality":[32],"reporting,":[33],"yet":[34],"remains":[35],"largely":[36],"manual,":[37],"slow,":[38],"error-prone.":[40],"Existing":[41],"automated":[42,179],"approaches":[43,92],"learn":[44],"predict":[46],"a":[47,89,174],"fixed":[48],"set":[49],"labeled":[53],"data,":[54],"thereby":[55],"preventing":[56],"adaptation":[57],"new":[59],"or":[61],"different":[62,68],"without":[65],"retraining":[66],"on":[67,141],"data.":[69],"They":[70],"also":[71],"provide":[72,126],"no":[73],"explanation":[74],"for":[75,86,178],"their":[76],"predictions,":[77],"limiting":[78],"trust":[79],"in":[80],"safety-critical":[81],"settings.":[82],"We":[83,139],"introduce":[84],"Symphony":[85,117,164],"Coding,":[88],"system":[90,123],"the":[93,95,104,111,134,157,161],"task":[94],"way":[96],"expert":[97],"human":[98],"coders":[99],"do:":[100],"by":[101],"reasoning":[102],"over":[103],"narrative":[106],"with":[107],"direct":[108],"access":[109],"guidelines.":[113],"This":[114],"design":[115],"allows":[116],"operate":[119],"across":[120,156,168],"any":[121],"span-level":[127],"evidence":[128],"linking":[129],"each":[130],"predicted":[131],"code":[132],"text":[135],"supports":[137],"it.":[138],"evaluate":[140],"two":[142],"public":[143],"benchmarks":[144],"three":[146],"real-world":[147],"datasets":[148],"spanning":[149],"inpatient,":[150],"outpatient,":[151],"emergency,":[152],"subspecialty":[154],"settings":[155],"United":[158,162],"States":[159],"Kingdom.":[163],"achieves":[165],"state-of-the-art":[166],"results":[167],"all":[169],"settings,":[170],"establishing":[171],"itself":[172],"as":[173],"flexible,":[175],"deployment-ready":[176],"foundation":[177],"coding.":[181]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}
