INTegRated InterveNtion of pSychogerIatric Care: real-world application and implementation of an advanced integrated telehealth system incorporating machine learning.

Introduction: Older individuals who suffer from mental disorders may encounter accessibility difficulties related to factors such as remoteness and socioeconomic status. The present analysis provides empirical evidence from the INTegRated InterveNtion of pSychogerIatric Care (INTRINSIC) and shows that this network could aid towards the incorporation of tele-psychiatry and tele-neuropsychology into primary healthcare. We propose that such integration, situated within comprehensive health digitalization initiatives, represents a scalable approach to expanding mental health access. Methods: 1,143 individuals from 2022 to 2025, from 11 different sites of INTRINSIC were recruited. Data collection was facilitated via the HEllenic Remote MEntal health Services for old-age (HERMES) Digital Platform, including demographic information, Mini-Cog scores, as well as information based on the Old Age Behavioral Risk Factor Surveillance System (OLA-BRFSS). A machine learning (ML) model was developed, trained, and evaluated using nested cross-validation. The classification analysis outcome was the Mini-Cog scores and eighty-three known risk factors were analyzed. Features were selected using Elastic Net regularization. A Random Forest classifier was then trained on the selected feature, and was employed to classify individuals into two Mini-Cog cognitive performance groups. Results: The ML algorithm employed in this study revealed eight features to be positively associated with low Mini-Cog scores, including subjective complaints of cognitive problems, retirement, polypharmacy, and history of falls. Five variables demonstrated a positive association with higher Mini-Cog scores, including prior diagnosis of an anxiety disorder, insomnia, and physical exercise. The model achieved a ROC-AUC of 0.76 (Figure 3 and Table 4), with a BAC of 0.65. Discussion: The present paper presents the first large-scale study on INTRINSIC, including multiple sites and integrating psychiatric, cognitive, medical, as well as sociodemographic variables in state-of-the-art ML models. Our results add to the existing literature on the complex interrelationships of factors affecting cognitive status in older individuals. We propose that INSTRINSIC may function as a benchmark for integrating psychiatric and neuropsychological services within primary healthcare settings, thereby addressing disparities in access to care and diagnostic equity.