Exploring Statistical Relationships for Risk Assessment and Value Computation of Digital Health Technologies in India

Abstract

In this research, we explore the statistical models that have contributed to determining the value of digital health technologies (DHTs) in global contexts and demonstrate their critical application using diverse datasets within the Indian context. We highlight the incorporation of several key healthcare analytical approaches, such as propensity score matching to evaluate treatment effects in cardiovascular research, structural equation modeling to examine psychosocial factors contributing to academic burnout among college students, and random survival forest classification methods for identifying genetic markers associated with breast cancer prognosis. We utilize a college-level social burnout survey and a comprehensive Kaggle dataset to show the application of these approaches. This is the first study of its kind to highlight both these datasets and key analytical methods, tools that are underutilized in India, while showing their practical relevance for guiding digital health investments and addressing healthcare challenges in the country. This study draws on several case studies with datasets to present a future perspective, where key statistical methodologies play a central role in improving healthcare productivity and promoting personalized care.