Digital health for all: How digital health could reduce inequality and increase universal health coverage

Digital transformation in health care has a lot of opportunities to improve access and quality of care. However, in reality not all individuals and communities are benefiting equally from these innovations. People in vulnerable conditions, already in need of more care and support, are often not participating in digital health programs. Fortunately, numerous initiatives worldwide are committed to make digital health accessible to all citizens, stimulating the long-cherished global pursuit of universal health coverage. Unfortunately initiatives are not always familiar with each other and miss connection to jointly make a significant positive impact. To reach universal health coverage via digital health it is necessary to facilitate mutual knowledge exchange, both globally and locally, to link initiatives and apply academic knowledge into practice. This will support policymakers, health care providers and other stakeholders to ensure that digital innovations can increase access to care for everyone, leading towards Digital health for all.

Spectral analysis of flow cytometric data: design of a special-purpose low-pass digital filter

Spectral decomposition of flow cytometric datafiles of arbitrary dimension reveal information of both the signal and the noise components that constitute the histograms. This spectral information is used to construct a low-pass digital filter, which removes the high-frequency noise from the actual data. It is shown that this procedure guarantees non-trivial smoothing of the flow cytometric data in accordance with the local experimental situation. As a consequence optimal reconstruction of the signal is possible, which facilitates unambiguous interpretation of the data files and mathematical estimation of the statistical parameters.