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

Abstract

Introduction 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. Methods 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. Discussion 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. Trial registration German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

Introducing a machine learning algorithm for delirium prediction—the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead)

“…In turn, we focused on features that are either already routinely assessed in a clinical setting or associated with only minor costs, both in time and resources. The present work is embedded in the SURGE-Ahead project [ 11 ]. As opposed to the PAWEL-R model that focused on binary input variables to enable manual calculation, the present model can be expressed as a multivariate regression with a calibrated probabilistic output (see Table 2 ).…”

“…Furthermore, the model has been screened for potential sex and native language bias and does not disadvantage patients based on their sex or native language. In regards to age (>70), the demographic of the training data is representative of the target population of the SURGE-Ahead project [ 11 ].…”

“…Second, it is common for performance metrics of clinical prediction tools to drop significantly when tested on external data [ 10 , 42 ]. In regard to this, an external validation is planned with data from an observational study that recently started within the SURGE-Ahead project and is expected to end in early 2024 [ 11 ]. Third, our model was calibrated to the entire sample, however, prevalence of delirium differs in various settings.…”

“…A comprehensive way to assess these subscores was proposed by Wong and colleagues [ 43 ] in a modified version of the MoCA, which requires ~5 min. With the exception of the falls anamnesis, clinical frailty scale and the MoCA 5-min assessment [ 43 ], all features are routine data that could be extracted from the patient’s electronic medical record using an automated pipeline, the development of which is part of the SURGE-Ahead project [ 11 ]. In addition, the proposed POD prediction algorithm is capable of handling missing data by simple using the median/mean of the training data (see Table 2 ), albeit at the cost of accuracy.…”

“…The application of delirium prediction tools is desirable in digital healthcare applications targeting older adults. In the SURGE-Ahead project (Supporting SURgery with GEriatric Co-Management and AI [ 11 ]), we aim to develop a digital healthcare application with a dashboard-style user-interface, assisting surgical teams in the care of geriatric patients. One cornerstone of this application will be the prediction of POD.…”