Six Predictions for Digital Health - from Generalization to Specialization or: from “God in White” to Friend in Need (2/6)

When being in discomfort or pain, your first stop usually is the general practitioner (GP), who will take a look at you, listen to your health complaints and suggests next steps or treatment. In Germany, the average number of doctor visits per person is ten per year, a number that has been steadily increasing since 1991 (Statista 2015). Analysts speculate that more than half of these visits are unnecessary and lead to higher strain of physicians, longer waiting times for patients and increasing costs for payers (Weber 2016). As natural byproduct of doctor visits and health monitoring, giant amounts of data are produced per person everyday; why not leverage this data to enable a first diagnostic layer to make the above process lean, safe, and more efficient?

The medical industry has ever since produced massive amounts of data, caused by record keeping, compliance and regulatory requirements, and patient care (Raghupathi & Raghupathi, 2014). The advent of new technologies like mobile sensors, genome sequencing, and electronic health records (just to mention a few) promises a flood of molecular, environmental, and behavioral data, information, and knowledge about patients. Analyses state that data from the U.S. healthcare system alone reached 150 exabytes (10^18 bytes) in 2011. At this rate of growth, it is projected that big data for U.S. healthcare will soon reach the zettabyte (10^21 bytes) scale and, not much later, the yottabyte (10^24 bytes). Ultimately, machine learning algorithms and predictive modeling can help mine the layers of data for patterns and insight. Pulling everything together and finding the underlying connections will be the next challenge.

The second prediction for the future of healthcare is concerning the suboptimal processes described above: We believe that the first line of diagnostic analysis will in future be made remotely, either automated (through remote monitoring technologies screening patterns for anomalies) or through a telemedical approach (e.g. via a smartphone based app), or in a large computer-assisted primary care unit, i.e. a data-driven hospital of the future, which serves as a first point of contact and funnel for further treatment. This will help in case prioritization and categorization of the patient’s complaints and trigger subsequent steps.

The second line of diagnostics, disease management and treatment will happen in specialization centers, which will function as knowledge and competency clusters for respective diseases like Stroke, Chronic Heart Failure, Parkinson’s Disease, or Diabetes. Mid- and long-term monitoring and treatment will mainly be computer-aided and happen remotely.

This process modification with improved, data-driven prioritization and categorization in the first place, eventually, leads to a change of the doctor-patient relationship: The (specialized) doctor will again have more resources to take back the role of an empathic friend and distinct expert, whose job will be to manage, explain, and consult the individual in the decision making process rather than being the omniscient and inaccessible “god in white”.

The benefits of such change are obvious: Time in the diagnostic process is saved, and patients will ultimately be able to receive more precise and personalized diagnosis and care based on a holistic view. Doctors will benefit from decision support tools that could help quickly evaluate the best treatments whereas researchers will be able to gather detailed information from many patients, along with other data, which could lead to new insights into disease and treatment. Physicians are discharged and can focus on a more specialized field with an increased competence and even deeper understanding and knowledge.


References: [1] Anzahl der jährlichen Arztbesuche pro Kopf in Deutschland in den Jahren 1991 bis 2015: [2] Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. 952501-2-3 [3] Weber, N. (2016): Sind wirklich die Hälfte aller Arztbesuche überflüssig? [4]