Details

Autor(en) / Beteiligte

• Leinert, Christoph
• Fotteler, Marina
• Kocar, Thomas Derya
• Dallmeier, Dhayana
• Kestler, Hans A
• Wolf, Dennis
• Gebhard, Florian
• Uihlein, Adriane
• Steger, Florian
• Kilian, Reinhold
• Mueller-Stierlin, Annabel S
• Michalski, Christoph W
• Mihaljevic, André
• Bolenz, Christian
• Zengerling, Friedemann
• Leinert, Elena
• Schütze, Sabine
• Hoffmann, Thomas K
• Onder, Graziano
• Andersen-Ranberg, Karen
• O'Neill, Desmond
• Wehling, Martin
• Schobel, Johannes
• Swoboda, Walter
• Denkinger, Michael

Titel

Supporting SURgery with GEriatric Co-Management and AI

Ist Teil von

• PloS one, 2023-06, Vol.18 (6), p.e0287230

Ort / Verlag

Public Library of Science

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults.