The Digital Health Cluster (DHC) comprises five tenured Digital Health Professorships and Chairs with emphasis in digital global public health (Prof. Lothar Wieler), machine learning (Prof. Christoph Lippert), data analytics and computational statisticsand (Prof. Bernhard Renard), connected healthcare (Prof. Bert Arnrich)  and economics & policy (Prof. Ariel Stern) as core DHC faculty.

Global Public Health (GPH) is the science of protecting and improving population health on a global, multi-country scale based on the Essential Public Health Operations (EPHOS). These central functions are a guiding force for public health and aim for health equity, population participation and inter-sectoral governance models. GPH thus supports and complements individual health care. The Chair for Digital Global Public Health at HPI uses data-driven digital applications, emphasizes on public health communication and dedicates its research and activities towards disease prevention and population-level health promotion while improving circumstances (e.g. access to digital health) and health behavior within and across communities.

Technical advances in imaging and DNA sequencing enable diagnosis of disease earlier and more accurate than ever. Innovative use of data promises to revolutionize clinical practice and to turn medicine into a data science. In the research group “Digital Health – Machine Learning”, headed by Christoph Lippert, we work on Machine Learning and Artificial Intelligence algorithms and novel applications in medicine. We develop models to detect disease patterns in images and molecular data and statistical models for the quantitative analysis of large cohorts.

The Research Group Digital Health - Connected Healthcare headed by Bert Arnrich aims to pave the way for transforming healthcare systems from purely managing illness to maintaining wellness.  Ubiquitous sensing and computing technologies are foreseen as the key enabler for pushing the paradigm shift from the established centralized healthcare model to a user-centered and preventive overall lifestyle health management that is available everywhere, anytime and to anyone.

The research group Data Analytics and Computational Statistics develops statistical and IT methods to automatically evaluate large amounts of data, filter out relevant signals, and integrate pertinent prior knowledge. Emphasis is placed on the statistical estimation of rates of error as well as software implementation. Crucial thereby is the targeted, customization of procedures for specialized, practical problems. Biomedical issues are just one focus of the application area.

The research group Digital Health, Economics & Policy headed by Ariel Dora Stern focuses on interdisciplinary research at the intersection of technology management, public policy and innovation in health care, using methods from econometrics and data science. The research group studies the regulation, strategy, and economics of health care, with a focus on emerging health care technologies and delivery modalities.