Freitag Bettina, Uncovska Marie, Meister Sven, Prinz Christian, Fehring Leonard
Health Care Informatics, Faculty of Health, School of Medicine, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, 58455, Witten, Germany.
Department Healthcare, Fraunhofer Institute for Software and Systems Engineering, Emil-Figge-Straße 91, 44227, Dortmund, Germany.
NPJ Digit Med. 2024 Nov 17;7(1):321. doi: 10.1038/s41746-024-01324-0.
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被称为数字健康应用程序（“DiGA”）。为了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具有积极的护理效果。由于DiGA成本效益的实证探索在很大程度上仍未得到充分研究，本研究开创了基于队列的状态转换马尔可夫模型方法，以评估用于治疗抑郁症的DiGA。作为健康状态，我们定义了轻度、中度、重度抑郁症、缓解期和死亡。将50%的患者获得补充DiGA治疗的未来情景与当前的护理标准进行比较，结果显示每位患者可获得0.02个质量调整生命年（QALY），在五年时间内每位患者的额外直接成本约为1536欧元。决定DiGA成本效益的影响因素是DiGA的成本结构和个体DiGA的有效性。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实现总体节省。
