PLoS ONE (Jan 2023)

Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician.

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

DOI
https://doi.org/10.1371/journal.pone.0287230
Journal volume & issue
Vol. 18, no. 6
p. e0287230

Abstract

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IntroductionGeriatric 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.MethodsA 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.DiscussionThe 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.Trial registrationGerman clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.