To provide an ethical analysis of the implications of the usage of artificial intelligence-supported clinical decision support systems (AI-CDSS) in geriatrics. Ethical analysis based on the normative arguments regarding the use of AI-CDSS in geriatrics using a principle-based ethical framework. Normative arguments identified in 29 articles on AI-CDSS in geriatrics. Our analysis is based on a literature search that was done to determine ethical arguments that are currently discussed regarding AI-CDSS. The relevant articles were subjected to a detailed qualitative analysis regarding the ethical considerations Supplementary Datamentioned therein. We then discussed the identified arguments within the frame of the 4 principles of medical ethics according to Beauchamp and Childress and with respect to the needs of frail older adults. We found a total of 5089 articles; 29 articles met the inclusion criteria and were subsequently subjected to a detailed qualitative analysis. We could not identify any systematic analysis of the ethical implications of AI-CDSS in geriatrics. The ethical considerations are very unsystematic and scattered, and the existing literature has a predominantly technical focus emphasizing the technology's utility. In an extensive ethical analysis, we systematically discuss the ethical implications of the usage of AI-CDSS in geriatrics. AI-CDSS in geriatrics can be a great asset, especially when dealing with patients with cognitive disorders; however, from an ethical perspective, we see the need for further research. By using AI-CDSS, older patients' values and beliefs might be overlooked, and the quality of the doctor-patient relationship might be altered, endangering compliance to the 4 ethical principles of Beauchamp and Childress.

Introduction: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14–56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. Methods: The model was trained on the PAWEL study’s dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). Results: The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores ‘memory’, ‘orientation’ and ‘verbal fluency’, pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78–0.85] in the training set, 0.81 [95% CI 0.71–0.88] in the test set and 0.76 [95% CI 0.71–0.79] in a cross-centre validation. Conclusion: We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project.

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 21^st November 2022.

Background: Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics including a decision support for the best continuity of care option, supported by artificial intelligence (AI) algorithms. Objective: This qualitative study aims to explore the current challenges and demands in surgical geriatric patient care. Based on these challenges, the study explores the attitude of interviewees toward the introduction of an AI-supported CDSS (AI-CDSS) in geriatric patient care in surgery, focusing on technical and general wishes about an AI-CDSS, as well as ethical considerations. Methods: In this study, 15 personal interviews with physicians, nurses, physiotherapists, and social workers, employed in surgical departments at a university hospital in Southern Germany, were conducted in April 2022. Interviews were conducted in person, transcribed, and coded by 2 researchers (AU, LB) using content and thematic analysis. During the analysis, quotes were sorted into the main categories of geriatric patient care, use of an AI-CDSS, and ethical considerations by 2 authors (AU, LB). The main themes of the interviews were subsequently described in a narrative synthesis, citing key quotes. Results: In total, 399 quotes were extracted and categorized from the interviews. Most quotes could be assigned to the primary code challenges in geriatric patient care (111 quotes), with the most frequent subcode being medical challenges (45 quotes). More quotes were assigned to the primary code chances of an AI-CDSS (37 quotes), with its most frequent subcode being holistic patient overview (16 quotes), then to the primary code limits of an AI-CDSS (26 quotes). Regarding the primary code technical wishes (37 quotes), most quotes could be assigned to the subcode intuitive usability (15 quotes), followed by mobile availability and easy access (11 quotes). Regarding the main category ethical aspects of an AI-CDSS, most quotes could be assigned to the subcode critical position toward trust in an AI-CDSS (9 quotes), followed by the subcodes respecting the patient’s will and individual situation (8 quotes) and responsibility remaining in the hands of humans (7 quotes). Conclusions: Support regarding medical geriatric challenges and responsible handling of AI-based recommendations, as well as necessity for a holistic approach focused on usability, were the most important topics of health care professionals in surgery regarding development of an AI-CDSS for geriatric care. These findings, together with the wish to preserve the patient-caregiver relationship, will help set the focus for the ongoing development of AI-supported CDSS.

Clinical practice guidelines (CPGs) translate evidence into actionable recommendations to enhance care quality, improve clinical outcomes, reduce treatment variations, and make health-care delivery more cost-effective. CPGs primarily aid health-care practitioners but are also used by patients, policymakers, and organizations. The study aim was to assess CPG quality using the Appraisal of Guidelines, Research and Evaluation (AGREE) II instrument and to identify AGREE II items and domains that influence the overall assessment. Medline and eight CPG databases were searched for guidelines applicable to older patients (≥60 years, frail, or with dementia/delirium) in acute orthopedic/traumatological settings, published in English or German since January 1, 2016, and using an evidence appraisal. Titles, abstracts, and full texts were independently screened by two reviewers using Covidence. AGREE II assessments were conducted across all 23 items in six domains by three reviewers with different professional backgrounds. Data were analyzed using descriptive statistics, Mann-Whitney U test, Pearson's r correlation matrix, variance inflation factor, univariable and multivariable regression (nonnegative least squares), and intraclass correlation coefficient. Significance was set at P < .05. A total of 120 CPGs have been appraised, reaching a mean overall rating of 4.35 (±1.13). Most guidelines received an overall rating of five (n = 40, 33.33%), one guideline received an overall rating of one (0.8%). Using a standardized evidence rating framework (eg, Grading of Recommendations Assessment, Development and Evaluation) is significantly associated with a better overall rating (P < .001). The multivariable analysis showed that items 9, 12, and 15 had the highest influence on the overall AGREE II rating. Domain 6 (editorial independence) did not have an influence on the overall rating in a multivariable analysis. Methodological rigor, particularly the use of a standardized evidence rating framework, is essential for a good overall AGREE II rating and thus for high-quality CPGs. The results from this analysis can assist different stakeholders who also conduct AGREE II appraisals, develop CPGs, or are charged with implementing CPG recommendations.

The growing proportion of older adults in the population necessitates improved methods for assessing functional recovery. Objective, continuous monitoring using wearable sensors offers a promising alternative to traditional, often subjective assessments. This study aimed to investigate the utility of inertial measurement unit (IMU)-based data, combined with deep learning, to predict postoperative mobility, activities of daily living, and discharge destination in older adults following surgery. Data from the SURGE-Ahead project was analyzed, involving 39 patients (mean age 79.05 years) wearing lumbar IMU sensors for up to five postoperative days. Deep learning models (TabPFN) were applied and validated using leave-one-out cross-validation to predict the Charité Mobility Index (CHARMI), the Barthel Index, and discharge destination. The TabPFN model achieved R² values of 0.65 and 0.70 for predicting CHARMI and Barthel Index scores, respectively, with moderate to strong agreement with human assessments (weighted kappa ≥ 0.80). Discharge destination was predicted with an accuracy of 82%. The z-channel IMU data and parameters related to walking bouts were most predictive of outcomes. IMU-based data, combined with deep learning, demonstrates potential for automated functional assessment and discharge decision support in older adults following surgery.