This book provides an introduction into the field of digital medicine, its wide spectrum of current clinical applications, and the future practice of medicine. With “digital health” and “digital medicine” at its core, it focuses on the combination of therapeutics with modern hard- and software solutions, including artificial intelligence and advanced diagnostic technologies such as augmented imaging and ingestible or wearable (nano)sensors, to provide best patient care. In the four parts of this book, experts in the field have authored use cases and guiding principles on the visualization of patient data analytics and clinical decision support tools, including robotic-guided interventions, as well as nursing research along with palliative and inpatient care. The book also provides examples of “digital medicine” from almost all clinical disciplines together with technical and e-learning solutions.

Our life has – over many decades – evolved into a very safe and civilized space. Still, we are confronted, ever so often, with risks, fatal accidents, unintended human errors, and seemingly unpredictable illnesses. In many areas, society has reduced these risks with dramatic success through intelligent development and engineering of iterative prevention strategies, particularly in the areas of occupational safety, air, and road transport. We have been relentlessly and constantly turning every stone at every level in order to achieve the vision and goal of zero avoidable deaths. The prominent example, Vision Zero for road traffic, which began more than 30 years ago in Sweden, is based on the premise that making mistakes from time to time is human and natural and has therefore been trying to change the prevailing conditions and systems in such a way that these mistakes do not pose a vital threat to anyone. This is done through official programs at regional, national, and European levels, in many small and large steps, all of which collectively reach enormous proportions. Airplanes hardly ever crash and being at work is statistically safer than staying at home or even in bed. Today, there is a seatbelt obligation for car drivers, it is compulsory to wear a helmet for motorcyclists, there are antilock braking systems, airbags, electronic stability control systems, and much more. When serious injuries or fatal accidents occur at a traffic junction, the situation is always investigated and subsequently translated into counteractions. For example, a roundabout is often built instead, which does not necessarily reduce the number but the seriousness of accidents. When people die frequently because of swerving on the opposite roadway, the lanes are robustly separated and the speed limit is reduced.

Why don’t we transfer this rigorousness of action to other fields and, in particular, to medicine? If people die early or otherwise from avoidable widespread diseases such as cardiovascular diseases or cancer, do we set an investigation commission to prevent the reoccurrence of such events? In fact, and unbelievably so, we do not! Instead, the way we talk about cancer nowadays, ironically resembles how we talked about accidents during the last century. “Bitter fate,” “presumably familial predisposition,” “he smoked heavily,” “she was overweight,” “was not careful,” “went too late to the doctor,” and other excuses are accentuating our shrugging and the non-action. We seem to agree with a death sentence for minor human imperfection. Or to put it in the words of the famous German singer Herbert Grönemeyer: “Wie eine träge Herde Kühe schauen wir kurz auf und grasen dann gemütlich weiter” (“Like a sluggish herd of cows, we briefly look up and then continue to graze in comfort.”)

Well-made digitization can become a vital element in obtaining a Vision Zero in medicine, which will become evident while reading the chapters of this book. Even with our current knowledge, up to half of all widespread diseases could be avoided through prevention and early detection. But as of now, we are not doing what we could and should do!

When two people in one family fall ill with the same problem, it is often not even noticed. This has two reasons: Firstly, we already know a lot about many diseases today, but we do not use knowledge of the known unknowns. If we could use digital tools to analyze genetic information or even only anamneses and case histories, then some diseases could be completely avoided, prevention individualized, and therapies improved. Secondly, we still know very little. For example, our knowledge about familial cancer is rather small because this information has not been collected systematically. Digitized medical care can, however, generate a lot of new knowledge about the many unknown unknowns. Medicine urgently needs the much-quoted, learning healthcare system.

As part of the national network on genomic medicine in Germany, many of us are currently trying to give a tangible example of what this can look like, using lung cancer as an example. There are many forms of molecular diagnosis in this disease. But we do not know much about reproducibility, and the interpretation of the results is based on the approach of the respective institution. Many colleagues are struggling to always find and realize the right therapy recommendations for their patients, not least because much is changing quickly. What we have in mind for these patients is a digitally based network at the interface between academic centers and wide-ranging care, which evaluates the quality of examinations and data from all patients: How often are specific diagnostics requested? What recommendations are made? Tools to analyze this kind of data are available. However, in Germany, data processing tools from the last century are used or not used at all.

The scenario described above for lung cancer would be a huge advancement not only for the research community but also for all patients. If we would systematically collect, although with little delay, the measures that are taken in various constellations and the results achieved in each case, we could make this information available at the point of care, making it highly relevant for patient care. However, the benefits of digitalization start much earlier than all relevant information and findings are made available in a consolidated form in one place. This is the goal of the electronic healthcare record, the EHR, called ePA in Germany. In addition, navigation applications would provide information on therapy and clinical studies for patients, or their families, as well as individual context-related information that differs with illness, condition, place of residence, and so on. Using digital health tools, patients can also get a voice themselves to deliver results and receive answers to their questions. In the right way, digitalization can democratize access to and use of healthcare. Not to mention that it probably also would be much more efficient.

Some may ask “… and data protection?” Well, what about data protection, really? The pandemic has taught us that this topic has been kept in a stranglehold of intransparent pseudo-security in Germany for too long with an “only dead data are good data” mindset. Health data must be particularly safe, no doubt. But data protection in a strict sense means patient protection. Medical data is supposed to help and protect patients, and data protection must have the same goals, without unreasonably compromising data security. So far, we have been looking at it from one side only – we have been trying to protect data optimally, creating barriers to data access and irrational constructions such as data minimization. But in case of doubt, we are also protecting the data from the patients themselves, thus leaving them unprotected from their disease. In the context of health data, patient welfare must be the utmost and mandatory guiding principle for data protection.

Data protection must also be liable for the availability of data. As long as good data protection is considered to be equivalent to the least possible processing of data, we will not make any progress. The processing and analysis of medical data can be life-prolonging, even life-saving. This, in turn, makes an understanding of data protection, which focuses on data use rather than patient use, a danger for patients. Data analysis must not only take place in the ivory towers of a few health service researchers with massive regulation and a time lag. It must take place wherever it is needed, where it helps to protect patients. We know from workshops, for instance, that cancer patients demand this. We have had this discussion time and again, and we were surprised at the beginning but it has become quite clear: The patients want their data to be used. There is a great awareness that the solidarity-based health care system in Germany enables expensive therapies and correspondingly a great willingness to give something back exists. I would even go so far as to state that it is ethically questionable not to allow such analyses of your data.

About the initial considerations concerning the Vision Zero concept, if we have a traffic light at dangerous crossroads, will we turn it off and accept traffic fatalities? But this is exactly what we are doing in the healthcare sector and it must change. This book allows the readers to know how this works. It gives a comprehensive overview of the necessary technical and organizational requirements, including impressive examples for specific implementations in prevention, diagnosis, and therapy, that shows how digital medicine can help to prevent avoidable deaths. Each chapter of this book remarkably reflects the progress made in the digitization of medicine, collectively forming the basis for Vision Zero in medicine.