When well designed and implemented, DTs can support and simplify health financing tasks and lead to higher speed and/or accuracy in their execution. This contributes foremost to enhanced efficiency and transparency and more equitable resource distribution, ultimately supporting the final universal health coverage goals. In particular, DTs can help overcome challenges inherent in paper-based data systems and enable otherwise hardly implementable policy options, especially options that rely on near real-time exchange of data.

There are also several positive effects resulting from the emerging applications of artificial intelligence and machine learning in health financing. For example, this can help to reveal patterns in the available data which could otherwise have remained hidden, thus, for example, informing improved tailoring of services and leading to better quality of care and utilization in line with need. Efficiency, transparency and accountability can also increase for instance through automated fraud detection in claims management.

Yet, depending on the context, DTs might also come with negative effects, which will ultimately have an impact on progress towards the intermediate and final universal health care objectives. For example, existing inequities can be exacerbated by digital divides, such as when certain population groups experience more difficulties in using and thus benefiting from DTs, such as mobile phone applications. Caution is also warranted when DTs contribute to a pooling architecture with limited redistributive capacity, an example being mobile wallets for individual savings, without any pooling across people. Finally, insights derived from artificial intelligence and machine learning applications might be misused to exclude selected high-cost patients or conditions from health coverage or to raise insurance premiums. This might worsen or consolidate inequities in financial protection at the detriment of vulnerable and disadvantaged population groups.

More evidence and country experiences in using DTs and their effects on health financing functions and tasks are needed to share lessons on what works well and what does not. This will inform policy design and implementation practice and can equally feed into national digital health strategies as well as respective regulatory provisions to maximize the benefits of DTs for health financing. The existing WHO guide with key questions and exemplary indicators to orient such country studies can help generate evidence on the effects of DTs for health financing in a systematic manner.

Ultimately, every application of a DT for health financing requires a careful, context-specific assessment of the benefits and risks. Whether the effects of using a DT will be positive or negative will mainly depend on how and for which purpose the DT is applied in health financing as well as on effective mitigation of the risks.