{"id":"https://openalex.org/W7165619734","doi":"https://doi.org/10.48550/arxiv.2606.23301","title":"EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning","display_name":"EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165619734","doi":"https://doi.org/10.48550/arxiv.2606.23301"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23301","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139211303","display_name":"Yitong Qiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Yitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139142863","display_name":"Lei Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139136317","display_name":"Yue Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139151321","display_name":"Jian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139173734","display_name":"Jinjie Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Jinjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139174698","display_name":"Zhixuan Chu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Zhixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139182534","display_name":"Kui Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Kui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10350","display_name":"Electronic Health Records Systems","score":0.42879998683929443,"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"}},"topics":[{"id":"https://openalex.org/T10350","display_name":"Electronic Health Records Systems","score":0.42879998683929443,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.4104999899864197,"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/T11642","display_name":"Genomics and Rare Diseases","score":0.03319999948143959,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"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/sql","display_name":"SQL","score":0.7394999861717224},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4675000011920929},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.44440001249313354},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41130000352859497},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.40560001134872437},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.39160001277923584},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.37229999899864197},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.3610999882221222}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7878000140190125},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.7394999861717224},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4675000011920929},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.40560001134872437},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.4050000011920929},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C55596503","wikidata":"https://www.wikidata.org/wiki/Q1431648","display_name":"Data definition language","level":3,"score":0.33160001039505005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.29649999737739563},{"id":"https://openalex.org/C2779247653","wikidata":"https://www.wikidata.org/wiki/Q2066865","display_name":"Prot\u00e9g\u00e9","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C544833334","wikidata":"https://www.wikidata.org/wiki/Q2005","display_name":"JavaScript","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C2778514511","wikidata":"https://www.wikidata.org/wiki/Q1374194","display_name":"Programmer","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C146206909","wikidata":"https://www.wikidata.org/wiki/Q531152","display_name":"Declarative programming","level":4,"score":0.2720000147819519},{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C41009113","wikidata":"https://www.wikidata.org/wiki/Q54871","display_name":"SPARQL","level":4,"score":0.2597000002861023},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25440001487731934},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23301","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23301","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"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":{"Clinical":[0],"agents":[1,195],"promise":[2],"to":[3,6,15,86],"democratize":[4],"access":[5],"electronic":[7],"health":[8],"records":[9],"(EHRs),":[10],"yet":[11],"existing":[12],"benchmarks":[13],"fail":[14],"reflect":[16],"the":[17,55,102,129,138],"complexity":[18,106],"of":[19,132,165],"practical":[20],"EHR":[21,134,205],"analysis,":[22],"e.g.,":[23],"often":[24],"operating":[25],"on":[26,54,122,126],"idealized,":[27],"clean":[28],"EHRs":[29],"via":[30],"static":[31],"SQL":[32,94,104,117,179],"generation":[33],"rather":[34],"than":[35,167],"interactive":[36,49],"execution.":[37],"In":[38],"this":[39],"work,":[40],"we":[41],"introduce":[42],"EHR-Complex,":[43],"a":[44,89,190],"large-scale":[45,204],"benchmark":[46],"designed":[47],"for":[48,107,151,171,193,203],"clinical":[50,72,130,194],"database":[51],"reasoning.":[52],"Built":[53],"large":[56],"MIMIC-IV":[57],"substrate":[58],"(365K":[59],"patients,":[60],"31":[61],"tables,":[62],"500M+":[63],"records),":[64],"EHR-Complex":[65,100,127,188],"comprises":[66],"about":[67],"52K":[68],"tasks":[69],"spanning":[70],"six":[71],"intents,":[73],"supporting":[74],"both":[75],"patient-level":[76],"and":[77,111,185,196],"population-level":[78],"queries,":[79],"where":[80],"each":[81],"task":[82,105],"requires":[83],"an":[84],"agent":[85],"interact":[87],"with":[88,137],"sandboxed":[90],"environment":[91],"by":[92],"executing":[93],"queries":[95],"or":[96],"Python":[97],"code.":[98],"Notably,":[99],"considers":[101],"real-world":[103],"longitudinal":[108],"multi-table":[109],"aggregation":[110],"compositional":[112],"reasoning,":[113],"resulting":[114],"in":[115,200],"31.93":[116],"structural":[118],"components":[119],"per":[120],"query":[121],"average.":[123],"Evaluation":[124],"results":[125],"reveal":[128],"difficulty":[131],"these":[133],"reasoning":[135,202],"scenarios,":[136],"top-performing":[139],"model":[140],"achieving":[141],"only":[142],"62.3%":[143],"exact-match":[144],"accuracy.":[145],"Pass^k":[146],"consistency":[147],"drops":[148],"below":[149],"50%":[150],"nearly":[152],"all":[153],"evaluated":[154],"models":[155],"at":[156],"k=4,":[157],"exposing":[158],"broad":[159],"stochastic":[160],"fragility.":[161],"A":[162],"fine-grained":[163],"analysis":[164],"more":[166],"3,800":[168],"failed":[169],"trajectories":[170],"representative":[172],"LLMs":[173],"reveals":[174],"three":[175],"dominant":[176],"failure":[177],"modes:":[178],"logic":[180],"errors,":[181],"medical-code":[182],"lookup":[183],"failures,":[184],"semantic":[186],"misunderstandings.":[187],"provides":[189],"rigorous":[191],"testbed":[192],"highlights":[197],"remaining":[198],"gaps":[199],"robust":[201],"analysis.":[206]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}
