{"id":"https://openalex.org/W7164577551","doi":"https://doi.org/10.48550/arxiv.2606.12702","title":"Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System","display_name":"Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164577551","doi":"https://doi.org/10.48550/arxiv.2606.12702"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.12702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12702","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.12702","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138541049","display_name":"Alyssa Unell","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Unell, Alyssa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138497011","display_name":"Miguel Fuentes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fuentes, Miguel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040350204","display_name":"Brenna Li","orcid":"https://orcid.org/0000-0003-3692-243X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Brenna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138545145","display_name":"Bridget Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Bridget","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005798722","display_name":"Meena Jagadeesan","orcid":"https://orcid.org/0000-0002-3521-440X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jagadeesan, Meena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138486719","display_name":"Sanmi Koyejo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koyejo, Sanmi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136221977","display_name":"Nigam Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Nigam","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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.5728999972343445,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.5728999972343445,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.14489999413490295,"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/T10350","display_name":"Electronic Health Records Systems","score":0.08560000360012054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6712999939918518},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5652999877929688},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5461999773979187},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5321999788284302},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4092000126838684},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.3953000009059906},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.3898000121116638},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.3409000039100647},{"id":"https://openalex.org/keywords/query-language","display_name":"Query language","score":0.3206999897956848}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7508000135421753},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6712999939918518},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5652999877929688},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5321999788284302},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4934000074863434},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41359999775886536},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40849998593330383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C2778058735","wikidata":"https://www.wikidata.org/wiki/Q4692253","display_name":"Aggregate data","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29260000586509705},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C2777622855","wikidata":"https://www.wikidata.org/wiki/Q7901844","display_name":"User information","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.12702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12702","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.12702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.12702","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1,175],"models":[2],"(LLMs)":[3],"are":[4],"increasingly":[5],"integrated":[6],"into":[7],"clinical":[8,49],"systems,":[9],"making":[10,164],"it":[11],"essential":[12],"to":[13,25,43,182,189,217],"evaluate":[14],"the":[15,81,92,101,104,144,170,187,192,196,205,215],"real-world":[16],"utility":[17],"of":[18,59,122,128,139,146,166,207],"these":[19],"systems.":[20,50],"However,":[21],"static":[22],"benchmarks":[23],"tend":[24],"measure":[26],"correctness":[27],"rather":[28],"than":[29],"user":[30,74,102,129,193,209],"acceptance,":[31],"aggregate":[32],"performance":[33],"across":[34],"queries,":[35],"and":[36,111,156],"require":[37],"densely":[38],"annotated":[39],"datasets":[40],"--":[41],"leading":[42],"major":[44],"blind":[45],"spots":[46],"for":[47,178],"evaluating":[48],"In":[51],"this":[52],"work,":[53],"we":[54,85,142],"perform":[55],"a":[56,87,95,119],"deployment-centered":[57],"evaluation":[58],"an":[60,69,137],"LLM":[61,105],"system":[62,197],"embedded":[63],"within":[64],"electronic":[65],"health":[66],"records":[67],"at":[68],"academic":[70],"medical":[71],"center,":[72],"where":[73],"feedback":[75],"is":[76,162],"sparse":[77],"but":[78],"closely":[79],"reflects":[80],"deployment":[82],"conditions.":[83],"Specifically,":[84],"train":[86],"pre-response":[88],"classifier":[89],"that":[90,94,132,163],"estimates":[91],"risk":[93],"future":[96],"interaction":[97],"will":[98,194],"result":[99],"in":[100,149],"rejecting":[103],"response,":[106],"based":[107],"on":[108],"query":[109,184],"content":[110],"deployment-specific":[112,167,212],"context":[113,168],"available":[114],"before":[115],"generation.":[116],"We":[117],"conduct":[118],"prospective":[120],"analysis":[121],"our":[123,133,200],"model":[124,135,176],"over":[125],"4.5":[126],"months":[127],"feedback,":[130],"finding":[131],"prediction":[134],"achieves":[136],"AUROC":[138],"0.719.":[140],"Further,":[141],"estimate":[143],"benefit":[145],"such":[147],"predictions":[148],"two":[150],"downstream":[151],"use":[152,165],"cases":[153],"(guardrail":[154],"triggering":[155],"abstention).":[157],"Our":[158],"key":[159],"conceptual":[160],"insight":[161],"(i.e.,":[169],"provider":[171],"type,":[172],"department":[173],"name,":[174],"used":[177],"response),":[179],"as":[180],"opposed":[181],"only":[183],"content,":[185],"improves":[186],"ability":[188],"predict":[190],"whether":[191],"reject":[195],"output.":[198],"Altogether,":[199],"empirical":[201],"case":[202],"study":[203],"demonstrates":[204],"feasibility":[206],"predicting":[208],"rejection":[210],"using":[211],"context,":[213],"opening":[214],"door":[216],"targeted":[218],"guardrails.":[219]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-13T00:00:00"}
