LLMs for Healthcare Process Orchestration: Promises and Challenges...24th International Conference of Informatics, Management and Technology in Healthcare (ICIMTH), July 3-5, 2026, Athens, Greece.
Healthcare processes combine stable workflow segments with open, casedependent work. This makes the use of large language models (LLMs) and more autonomous AI systems less a question of technical capability alone than of orchestration: different degrees of AI autonomy fit different degrees of proces...
| Publicado en: | Studies in Health Technology & Informatics Vol. 338; pp. 589 - 594 |
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| Autor principal: | |
| Formato: | proceedings research Journal Article |
| Publicado: |
Sage Publications Inc.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195115687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195115687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2026 vid: 338 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 195115687 195115687 195115687 10.3233/SHTI260912 195115687 ppf: 589 ppct: 5 formats: tig: atl: LLMs for Healthcare Process Orchestration: Promises and Challenges...24th International Conference of Informatics, Management and Technology in Healthcare (ICIMTH), July 3-5, 2026, Athens, Greece. aug: au: SARIYAR, Murat affil: Bern University of Applied Sciences, Switzerland sug: subj: Workflow Autonomy Case Management Task Performance and Analysis Process Assessment (Health Care) Quality Management, Organizational Natural Language Processing Congresses and Conferences Greece Greece Human Documentation Communication Judgment Artificial Intelligence Conceptual Framework Hand Off (Patient Safety) Models, Theoretical ab: Healthcare processes combine stable workflow segments with open, casedependent work. This makes the use of large language models (LLMs) and more autonomous AI systems less a question of technical capability alone than of orchestration: different degrees of AI autonomy fit different degrees of process structure. Based on a conceptual review, this paper develops a BPMN-CMMN lens for distinguishing between structured tasks that support tightly bounded LLMguided execution and case-dependent segments in which more autonomous coordination may become relevant. The literature suggests that LLMs fit best in documentation, summarization, communication, referral support, and other bounded workflow steps, whereas more autonomous systems become more plausible where progression depends on changing context, exceptions, and discretionary judgment. The main challenge lies in evaluation. Classical process indicators remain necessary, but they become harder to interpret once work is distributed across humans and AI. In addition to operational KPIs, healthcare organizations need measures that capture orchestration quality, supervision burden, context carryover, and the stability of outcomes across variable cases. pubtype: Academic Journal doctype: proceedings research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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