Designing Digital Therapeutic Content Using Chronic Disease Data: A Focus on Improving Urinary Dysfunction

Abstract

In recent years, advancements in information and communication technologies, including artificial intelligence, big data, virtual reality, and augmented reality, have driven substantial growth in the field of digital medical diagnosis and treatment, thereby enhancing quality of life. Beginning in the mid-2010s with the advent of digital healthcare applications, and further accelerated by the impact of coronavirus disease 2019, digital therapeutic products have profoundly influenced society. Nevertheless, the expansion of digital therapeutics has encountered challenges associated with regulatory hurdles, differentiation from general digital healthcare, and the necessity for trustworthiness, which have contributed to a slower rate of progress. This study proposes a 3P content model–encompassing pre-education, prediction/diagnosis/treatment, and postmanagement–to increase the trustworthiness of digital therapeutics. The design of the 3P content model includes a fundamental structure that establishes networks with healthcare institutions, aiming to increase the reliability of data utilization and to facilitate integration with medical decision support systems. For case development, the study introduces a prototype of a mobile application that utilizes chronic disease urinary dysfunction data, demonstrating the cyclical structure inherent in the 3P content model.