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.

中文翻译：

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使用慢性病数据设计数字治疗内容：重点改善泌尿功能障碍。

近年来，人工智能、大数据、虚拟现实、增强现实等信息通信技术的进步，带动了数字医疗诊疗领域的大幅增长，从而提高了人们的生活质量。自 2010 年代中期开始，随着数字医疗应用的出现，并因 2019 年冠状病毒病的影响而进一步加速，数字治疗产品对社会产生了深远的影响。然而，数字治疗的扩张遇到了与监管障碍、与一般数字医疗保健的区别以及可信度的必要性相关的挑战，这些都导致了进展速度放缓。本研究提出了一个3P内容模型——包括预教育、预测/诊断/治疗和后管理——以提高数字治疗的可信度。3P内容模型的设计包括与医疗机构建立网络的基本结构，旨在提高数据利用的可靠性并促进与医疗决策支持系统的集成。对于案例开发，该研究引入了一个利用慢性疾病泌尿功能障碍数据的移动应用程序原型，展示了 3P 内容模型中固有的循环结构。