Einsatz von KI-basierten Anwendungen zur Symptombeurteilung als entscheidungsunterstützende Systeme in der Notaufnahme
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Abstract
Healthcare systems worldwide are facing significant challenges such as
an aging population, increased prevalence of chronic diseases and
multimorbidity, and a shortage of skilled healthcare workers.
Consequently, emergency departments are becoming overcrowded,
leading to longer waiting times, delayed treatment, and suboptimal
outcomes. To tackle these issues, AI-based Symptom Assessment
Applications (SAAs) are being integrated into healthcare networks.
These SAAs aim to guide patients towards appropriate care by
providing personalised medical information and recommendations on if,
when and where to seek care to support their decision-making process.
Some SAAs are furthermore allowing them to share their symptom
report with their healthcare professionals (HCPs).
The objective of this work was to prospectively evaluate the use of an
SAA in emergency care through two clinical trials. In Study 1, 378
patients at Marburg University Hospital's emergency department were
enrolled, comparing the urgency advice provided by the SAA with the
Manchester Triage Scale (MTS) for patients in the three lowest MTScategories. The results showed that 91% of cases were triaged the
same or more conservatively, while 5% of cases were considered
potentially avoidable hazardous situations (AHS). Furthermore, 43% of
patients seeking emergency care were not deemed to require
emergency care. In Study 2, 81 patients at Katharinenkrankenhaus in
Stuttgart's emergency department were enrolled to prospectively
assess patients' and physicians' acceptance and perception of the
usefulness of the information transfer report generated by an SAA. The
findings indicated that in 74% of cases, doctors perceived the
consultation as facilitated, but only 35% considered the integration as
potentially time-saving. On the other hand, patients perceived the use of
the SAA positively in 90% of cases, with 86% found the content
understandable and 75% felt better understood by their treating
physician through the report delivery.
The results of this work suggest that SAAs are mostly safe and easy to
use for lower acuity patients in emergency departments and that the
symptom report can facilitate the clinical conversation between patients
and doctors. Further research is required to assess the impact of
patients using the SAA at home, specifically to evaluate if they would
follow the suggested advice and to determine if this would lead to a
more balanced distribution of patients across the healthcare ecosystem.
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Issued: 2026-01-14
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Is based on: (DOI) 10.2196/32340Is based on: (DOI) 10.2196/28199
Faculty
FB20:Medizin
Language
de
Keywords
patient navigationdigital healthsymptom assessment applicationemergency department
DFG-subjects
2.22-02 - Public Health, Gesundheitsbezogene Versorgungsforschung, Sozial- und Arbeitsmedizin
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Cotte, Fabienne: Einsatz von KI-basierten Anwendungen zur Symptombeurteilung als entscheidungsunterstützende Systeme in der Notaufnahme. : 2026-01-14.
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