{"id":"https://openalex.org/W7138409150","doi":"https://doi.org/10.48550/arxiv.2603.14631","title":"Anterior's Approach to Fairness Evaluation of Automated Prior Authorization System","display_name":"Anterior's Approach to Fairness Evaluation of Automated Prior Authorization System","publication_year":2026,"publication_date":"2026-03-15","ids":{"openalex":"https://openalex.org/W7138409150","doi":"https://doi.org/10.48550/arxiv.2603.14631"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14631","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14631","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.2603.14631","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129751612","display_name":"Sai P. Selvaraj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Selvaraj, Sai P.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129738099","display_name":"Khadija Mahmoud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahmoud, Khadija","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129661026","display_name":"Anuj Iravane","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iravane, Anuj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.57669997215271,"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":0.57669997215271,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.19460000097751617,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10350","display_name":"Electronic Health Records Systems","score":0.03739999979734421,"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/staffing","display_name":"Staffing","score":0.5403000116348267},{"id":"https://openalex.org/keywords/authorization","display_name":"Authorization","score":0.5162000060081482},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5008999705314636},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.3700000047683716},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.3540000021457672},{"id":"https://openalex.org/keywords/confidentiality","display_name":"Confidentiality","score":0.3407999873161316},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.3215999901294708},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.3215000033378601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.593999981880188},{"id":"https://openalex.org/C2777512617","wikidata":"https://www.wikidata.org/wiki/Q1783605","display_name":"Staffing","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C108759981","wikidata":"https://www.wikidata.org/wiki/Q788590","display_name":"Authorization","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.3257000148296356},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2988170871","wikidata":"https://www.wikidata.org/wiki/Q11000047","display_name":"Healthcare system","level":3,"score":0.3012000024318695},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30070000886917114},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C2777151079","wikidata":"https://www.wikidata.org/wiki/Q141160","display_name":"Parity (physics)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.25110000371932983},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14631","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14631","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.2603.14631","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14631","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":[{"score":0.7122472524642944,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Increasing":[0],"staffing":[1],"constraints":[2],"and":[3,34,87,106,118,149,165],"turnaround-time":[4],"pressures":[5],"in":[6,24,45,83,145,171],"Prior":[7],"authorization":[8,60],"(PA)":[9],"have":[10],"led":[11],"to":[12,18,168],"increasing":[13],"automation":[14],"of":[15],"decision":[16],"systems":[17,26],"support":[19],"PA":[20],"review.":[21],"Evaluating":[22],"fairness":[23,50,55,169],"such":[25],"poses":[27],"unique":[28],"challenges":[29],"because":[30],"legitimate":[31],"clinical":[32],"guidelines":[33],"medical":[35,77],"necessity":[36,78],"criteria":[37],"often":[38],"differ":[39],"across":[40],"demographic":[41],"groups,":[42],"making":[43],"parity":[44],"approval":[46,69],"rates":[47,66,115],"an":[48],"inappropriate":[49],"metric.":[51],"We":[52],"propose":[53],"a":[54,98,163],"evaluation":[56,91,170],"framework":[57],"for":[58],"prior":[59],"models":[61],"based":[62],"on":[63],"model":[64,113],"error":[65,114],"rather":[67],"than":[68],"outcomes.":[70],"Using":[71],"7,166":[72],"human-reviewed":[73],"cases":[74],"spanning":[75],"27":[76],"guidelines,":[79],"we":[80,158],"assessed":[81],"consistency":[82],"sex,":[84],"age,":[85],"race/ethnicity,":[86,133],"socioeconomic":[88],"status.":[89],"Our":[90],"combined":[92],"error-rate":[93],"comparisons,":[94],"tolerance-band":[95],"analysis":[96],"with":[97,152],"predefined":[99,124],"$\\pm$5":[100],"percentage-point":[101],"margin,":[102],"statistical":[103],"power":[104],"evaluation,":[105],"protocol-controlled":[107],"logistic":[108],"regression.":[109],"Across":[110],"most":[111],"demographics,":[112],"were":[116,142],"consistent,":[117],"confidence":[119,147],"intervals":[120,148],"fell":[121],"within":[122,155],"the":[123,156],"tolerance":[125],"band,":[126],"indicating":[127],"no":[128],"meaningful":[129],"performance":[130],"differences.":[131],"For":[132],"point":[134],"estimates":[135],"remain":[136],"small,":[137],"but":[138],"subgroup":[139],"sample":[140],"sizes":[141],"limited,":[143],"resulting":[144],"wide":[146],"underpowered":[150],"tests,":[151],"inconclusive":[153],"evidence":[154],"dataset":[157],"explored.":[159],"These":[160],"findings":[161],"illustrate":[162],"rigorous":[164],"regulator-aligned":[166],"approach":[167],"administrative":[172],"healthcare":[173],"AI":[174],"systems.":[175]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}
