{"id":"https://openalex.org/W7225654263","doi":"https://doi.org/10.5281/zenodo.24090449","title":"From discharge alerts to follow-up work: design and retrospective evaluation of a local, human-in-the-loop AI agent for primary care","display_name":"From discharge alerts to follow-up work: design and retrospective evaluation of a local, human-in-the-loop AI agent for primary care","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225654263","doi":"https://doi.org/10.5281/zenodo.24090449"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24090449","doi":"https://doi.org/10.5281/zenodo.24090449","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24090449","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24090449","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161329790","display_name":"Thiago Batista Nunes Bandeira","orcid":"https://orcid.org/0009-0006-0204-5298","observed_orcids":["https://orcid.org/0009-0006-0204-5298"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bandeira, Thiago Batista Nunes","raw_affiliation_strings":["Mendez Medical Center"],"raw_orcid":"https://orcid.org/0009-0006-0204-5298","affiliations":[{"raw_affiliation_string":"Mendez Medical Center","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5161329790"],"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":0.6929000020027161,"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":0.6929000020027161,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.2782999873161316,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.006200000178068876,"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/post-discharge-follow-up","display_name":"post-discharge follow-up","score":0.6790000200271606},{"id":"https://openalex.org/keywords/readmission-risk","display_name":"readmission risk","score":0.5720000267028809},{"id":"https://openalex.org/keywords/local-language-models","display_name":"local language models","score":0.6240000128746033},{"id":"https://openalex.org/keywords/human-in-the-loop-ai","display_name":"human-in-the-loop AI","score":0.6620000004768372},{"id":"https://openalex.org/keywords/primary-care","display_name":"primary care","score":0.6389999985694885},{"id":"https://openalex.org/keywords/readmission-reduction","display_name":"readmission prevention","score":0.44999998807907104},{"id":"https://openalex.org/keywords/qlora-fine-tuning","display_name":"QLoRA fine-tuning","score":0.7429999709129333},{"id":"https://openalex.org/keywords/medication-extraction","display_name":"medication extraction","score":0.3370000123977661},{"id":"https://openalex.org/keywords/mimic-iv","display_name":"MIMIC-IV","score":0.7350000143051147},{"id":"https://openalex.org/keywords/clinical-decision-support","display_name":"clinical decision support","score":0.34299999475479126},{"id":"https://openalex.org/keywords/retrospective-evaluation","display_name":"retrospective evaluation","score":0.3580000102519989},{"id":"https://openalex.org/keywords/zero-shot-prompting","display_name":"zero-shot prompting","score":0.3889999985694885}],"concepts":[{"id":"https://openalex.org/C2984752397","wikidata":"https://www.wikidata.org/wiki/Q3259956","display_name":"Primary care","level":2,"score":0.6316999793052673},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6226999759674072},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.5920000076293945},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.5411999821662903},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.48969998955726624},{"id":"https://openalex.org/C3017624810","wikidata":"https://www.wikidata.org/wiki/Q3259564","display_name":"Hospital discharge","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.43320000171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40700000524520874},{"id":"https://openalex.org/C167135981","wikidata":"https://www.wikidata.org/wiki/Q2146302","display_name":"Retrospective cohort study","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3684999942779541},{"id":"https://openalex.org/C2992870097","wikidata":"https://www.wikidata.org/wiki/Q3259564","display_name":"Patient discharge","level":3,"score":0.3391999900341034},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C3018949938","wikidata":"https://www.wikidata.org/wiki/Q17166101","display_name":"Text messaging","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.2761000096797943},{"id":"https://openalex.org/C3019871722","wikidata":"https://www.wikidata.org/wiki/Q718944","display_name":"Hospital admission","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2777896191","wikidata":"https://www.wikidata.org/wiki/Q22907655","display_name":"Patient experience","level":3,"score":0.25}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5281/zenodo.24090449","doi":"https://doi.org/10.5281/zenodo.24090449","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24090449","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.24090450","doi":"https://doi.org/10.5281/zenodo.24090450","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24090450","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.24090662","doi":"https://doi.org/10.5281/zenodo.24090662","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24090662","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24090449","doi":"https://doi.org/10.5281/zenodo.24090449","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24090449","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":[{"score":0.9537000060081482,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7213437997","https://openalex.org/W7216290757","https://openalex.org/W7165567284","https://openalex.org/W7126197599","https://openalex.org/W4402671463","https://openalex.org/W7215934082","https://openalex.org/W7139977857","https://openalex.org/W7211952426","https://openalex.org/W4409375103","https://openalex.org/W7213469316"],"abstract_inverted_index":{"Objective:":[0],"Primary":[1],"care":[2],"practices":[3],"learn":[4],"about":[5,183],"their":[6],"patients'":[7],"hospital":[8,53],"discharges":[9,180],"from":[10,79,159],"payer":[11],"alerts,":[12],"but":[13],"turning":[14],"each":[15,59],"alert":[16],"into":[17],"timely":[18],"follow-up":[19,70,336],"is":[20,338],"manual":[21],"work,":[22],"and":[23,46,56,67,86,92,102,135,147,169,231,242,257,291,321,324,383,390],"the":[24,32,69,80,113,160,188,195,205,208,223,232,237,243,248,279,287,296,304,318,352,372,377,381,391],"hospital's":[25],"discharge":[26,81,129,270],"summary":[27],"often":[28,186],"does":[29],"not":[30,394],"reach":[31],"practice.":[33],"We":[34],"designed":[35],"an":[36,163],"AI":[37],"agent":[38,337,392],"that":[39,107,285],"runs":[40],"on":[41,51,142,271,314,341],"a":[42,88,93,98,170,282,347,368],"practice's":[43],"own":[44,289],"hardware":[45],"evaluated":[47,112],"its":[48],"components":[49],"retrospectively":[50],"de-identified":[52],"data.":[54],"Materials":[55],"Methods:":[57],"For":[58],"discharge,":[60],"deterministic":[61],"code":[62],"scores":[63],"readmission":[64,119],"risk":[65,114],"(LACE)":[66],"sets":[68],"dates.":[71],"A":[72,333],"local":[73,309],"language":[74],"model":[75,350],"extracts":[76],"structured":[77],"facts":[78],"summary,":[82],"when":[83],"one":[84,272],"exists,":[85],"drafts":[87,280,382],"coordinator":[89],"call":[90],"script":[91],"provider":[94],"brief;":[95],"by":[96],"design":[97],"person":[99],"approves":[100],"them,":[101],"this":[103],"evaluation":[104,325],"stopped":[105],"before":[106],"step.":[108],"On":[109,344],"MIMIC-IV,":[110],"we":[111],"tiers":[115],"against":[116,191,216,260,326,367],"30-day":[117],"unplanned":[118],"(an":[120],"admission-type":[121],"proxy)":[122],"in":[123,127,149,278,295,397],"64,036":[124],"stays;":[125],"extraction":[126,156],"200":[128],"summaries,":[130],"comparing":[131],"prompted":[132,219,226,263,348],"Qwen3":[133],"8B":[134],"14B":[136],"models":[137,210,220,227,310],"with":[138,152],"QLoRA":[139],"fine-tunes":[140,233,244],"trained":[141],"rule-generated":[143,161,238,315],"or":[144],"teacher":[145],"labels;":[146],"drafting":[148],"195":[150],"discharges,":[151],"automated":[153],"checks.":[154],"Both":[155],"references":[157],"started":[158],"labels:":[162],"author-corrected":[164],"diagnosis":[165,206],"reference":[166,172,369],"(51":[167],"notes)":[168],"language-model-corrected":[171],"for":[173,201,218,262],"all":[174],"fields":[175,361],"(200":[176],"notes).":[177],"Results:":[178],"High-risk":[179],"were":[181],"readmitted":[182],"twice":[184],"as":[185,187],"rest":[189],"(28.3%":[190],"14.3%;":[192],"area":[193],"under":[194],"receiver":[196],"operating":[197],"characteristic":[198],"curve":[199],"[AUROC]":[200],"LACE,":[202],"0.638).":[203],"Against":[204,222],"reference,":[207,225],"fine-tuned":[209],"scored":[211,229],"highest":[212],"(micro":[213],"F1":[214],"0.93-0.94,":[215],"0.69-0.72":[217],"zero-shot).":[221],"all-field":[224],"zero-shot":[228],"0.70-0.72":[230],"0.58-0.60,":[234],"similar":[235],"to":[236,360,387],"labels":[239,316,329,363],"themselves":[240],"(0.58),":[241],"matched":[245,281],"243-299":[246],"of":[247,380],"rules'":[249],"301":[250],"potentially":[251],"false":[252],"\"started\"":[253],"medications":[254],"(by":[255],"note":[256,297],"drug-name":[258],"key),":[259],"22-46":[261],"models.":[264],"Drafting":[265],"took":[266],"16.8":[267],"s":[268],"per":[269],"consumer":[273],"GPU;":[274],"every":[275],"medication":[276],"name":[277],"record":[283],"stand-in":[284],"included":[286],"model's":[288],"extraction,":[290],"98.4%":[292],"also":[293],"appeared":[294],"text.":[298,312],"Discussion:":[299],"Deterministic":[300],"rules":[301],"can":[302],"carry":[303],"time-critical":[305],"scheduling":[306],"while":[307],"small":[308],"handle":[311],"Fine-tuning":[313],"reproduced":[317],"labels'":[319],"gaps":[320],"potential":[322],"errors,":[323],"those":[327],"same":[328],"concealed":[330],"it.":[331],"Conclusion:":[332],"local,":[334],"human-in-the-loop":[335],"computationally":[339],"feasible":[340],"commodity":[342],"hardware.":[343],"these":[345],"benchmarks":[346],"base":[349],"was":[351],"better":[353],"default":[354],"extractor;":[355],"fine-tuning":[356],"should":[357],"be":[358,388],"limited":[359],"whose":[362],"have":[364],"been":[365,395],"checked":[366],"other":[370],"than":[371],"training":[373],"labels.":[374],"Human":[375],"review,":[376],"clinical":[378],"accuracy":[379],"operational":[384],"benefit":[385],"remain":[386],"evaluated,":[389],"has":[393],"used":[396],"patient":[398],"care.":[399]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
