Regulated mobile health applications are called digital health applications (“DiGA”) in Germany. To qualify for reimbursement by statutory health insurance companies, DiGA have to prove positive care effects in scientific studies. Since the empirical exploration of DiGA cost-effectiveness remains largely uncharted, this study pioneers the methodology of cohort-based state-transition Markov models to evaluate DiGA for depression. As health states, we define mild, moderate, severe depression, remission and death. Comparing a future scenario where 50% of patients receive supplementary DiGA access with the current standard of care reveals a gain of 0.02 quality-adjusted life years (QALYs) per patient, which comes at additional direct costs of ~1536 EUR per patient over a five-year timeframe. Influencing factors determining DiGA cost-effectiveness are the DiGA cost structure and individual DiGA effectiveness. Under Germany’s existing cost structure, DiGA for depression are yet to demonstrate the ability to generate overall savings in healthcare expenditures.

在德国，受监管的移动健康应用程序称为数字健康应用程序 （“DiGA”）。要获得法定健康保险公司的报销资格，DiGA 必须在科学研究中证明积极的护理效果。由于 DiGA 成本效益的实证探索在很大程度上仍未被发现，因此本研究开创了基于队列的状态转换马尔可夫模型方法来评估 DiGA 对抑郁症的影响。根据健康状态，我们定义了轻度、中度、重度抑郁、缓解和死亡。将 50% 的患者接受补充 DiGA 通路的未来情景与当前的护理标准进行比较，发现每位患者增加了 0.02 个质量调整生命年 （QALY），这在五年内每位患者的额外直接成本为 ~1536 欧元。决定 DiGA 成本效益的影响因素是 DiGA 成本结构和单个 DiGA 有效性。在德国现有的成本结构下，用于治疗抑郁症的 DiGA 尚未证明有能力在医疗保健支出方面产生整体节省。