Welcome to the Smart Hospital

Day-to-day life in medical centers will be completely transformed by digitalization in coming years. German hospitals are at the forefront of the digital transformation, the healthcare market in Germany is booming and foreign investors are flocking.

The surgeon’s focus is as sharp as his instruments. He is preparing for a procedure to remove a cerebral tumor in a patient’s brain – a complicated operation that will take hours. There are no monitors or computers in the operating room. Instead the doctor is wearing HoloLens – ‘mixed reality’ smart glasses produced by Microsoft. Hidden behind the dark lenses is intelligent computer software that allows the surgeon to look into his patient three-dimensionally before and during surgery. Activated by voice command, the glasses show CT or MRI images of the patient created prior to surgery and then projected onto the lenses.

Virtual surgery is an emerging field that will change the practice of operations. “We assume that soon every doctor will wear a HoloLens Mixed Reality device,” predicts Sirko Pelzl, CEO of apoQlar. The Hamburg-based start-up is partnering with Microsoft and distributes HoloLens devices to hospitals in Germany and to foreign countries such as Singapore and Thailand. ApoQlar has already equipped seven hospitals in Germany with its Virtual Surgery Intelligence (VSI) solution, including Essen’s Universitätsklinikum and the Marienkrankenhaus Hamburg. “These glasses could completely replace computers and monitors in the OR (operating room),” Pelzl says. VSI costs start at EUR 1,000 a month. The fee includes the HoloLens, the software and server performance. In addition, after two to three years, apoQlar will exchange the glasses for a newer version.

Virtual surgery is just one example of how digitalization could improve medical care for patients, doctors and nurses alike. New technological possibilities are entering the market at a rapid pace. By the year 2021, all Germans are to have an electronic medical record that stores all medical data compiled from physicians and clinics. There are also highly innovative medtech companies like apoQlar driving change. Thus, it’s not surprising that many overseas investors are taking a major interest in the German healthcare sector.

While critics of digitalization in healthcare fear that the hospitals of the future will have less human interaction and increased anonymity, the benefits outweigh the concerns. New technologies provide greater efficiency and therefore reduce costs within the entire healthcare system. Consulting firm McKinsey estimates that digitalization in the German healthcare sector could have cut around EUR 34 billion of total health expenditure in 2018 – a potential saving of 12 percent.

Paperless data-storage solutions like the electronic medical record promise to deliver the biggest cost reduction, some EUR 9 billion. Online interactions between clinics and patients, algorithms that support physicians’ decision-making, automated workflows and patient self-service are other ways to cut costs, McKinsey says.

While critics of digitalization in healthcare fear that the hospitals of the future will have less human interaction and increased anonymity, the benefits outweigh the concerns. New technologies provide greater efficiency and therefore reduce costs within the entire healthcare system. Consulting firm McKinsey estimates that digitalization in the German healthcare sector could have cut around EUR 34 billion of total health expenditure in 2018 – a potential saving of 12 percent.

Paperless data-storage solutions like the electronic medical record promise to deliver the biggest cost reduction, some EUR 9 billion. Online interactions between clinics and patients, algorithms that support physicians’ decision-making, automated workflows and patient self-service are other ways to cut costs, McKinsey says.

But cost advantages aren’t all that makes the digitalized hospital a must. Patients can also look forward to greater information thanks to new applications that can help them achieve their health goals and that support doctors in making more accurate diagnoses. For example, data from wearables or smart watches can uncover irregularities in a patient’s health, which can then be analyzed by intelligent algorithms. The roles of doctors and nurses will also change.

As machines perform an increasing number of daily, time-consuming tasks such as compounding drugs and searching for irregularities in diagnostic images, doctors will have more time to interact with patients. Moreover, their overall workload will diminish. So fewer mistakes will be made.

Trendsetting in Essen

Essen’s Universitätsklinikum is a state-of-the-art German hospital that aims to become a model of digitalization – indeed, digitalization is at the very core of its corporate goals. The hospital is currently piloting smart algorithms that perform routine work in radiology: checking images from CT scans and evaluating screenings for signs of cancer. The algorithm is a form of artificial intelligence that is fed with data and is constantly learning. The more data, the better the results. The hit rates in this pilot phase are high: metastases in cervical cancer can be detected at an early stage with 95 to 97 percent accuracy.

Even the time-consuming task of preparing drugs in the university hospital pharmacy is done by a robot. The intelligent machine picks up the prescription, weighs and mixes the ingredients and then produces the drug. A human pharmacist checks to ensure the mixture is correct. Robots are also at work in the biobank of the clinic, where blood, body fluids and tissue samples from patients are stored. In the past excess material was thrown out, but it can now be processed by a machine for use in further research. The machine fills remnants of blood or urine into small plastic containers.

Sensors like this are “already common practice in German hospitals,” says Andreas Bollmann, senior rhythmologist at the Leipzig Heart Institute and CEO of Leipzig Heart Digital – a subsidiary of Leipzig Heart Center, which is part of the Helios Kliniken group. “Processing the data, feeding it into databases and drawing conclusions – that’s the real challenge,” Bollmann says.

Since 2015, the Leipzig Heart Institute has established a reputation as a pioneer in cardiovascular clinical research. It is currently the largest cardiovascular clinical research facility in Germany. In 2018 the Leipzig Heart Institute raised EUR 2m for research and published over 200 scientific studies.

Bollmann is currently researching machine learning in the context of atrial fibrillation ablation, a routine operation performed on cardiac patients to treat atrial flutter. Last year, he developed the SAFER system, short for ‘HelioS Atrial Fibrillation ablation rEgistRy.’ The system draws upon data from 29 clinics within the Helios Group to minimize the risk of complications during the procedure. Currently the data set contains more than 28,000 procedures from 2010 to 2019. “This makes it the largest German register of atrial fibrillation ablations and allows us to create variables for quality assurance,” says Bollmann. Bollmann was also part of the June 2019 ‘Healthcare Hackathon’ organized by the University of Mainz and supported by the Federal Ministry of Health. The object of the event was for healthcare professionals and entrepreneurs, working together in teams of 18, to find solutions to challenges currently facing hospitals.

The task for Bollmann and his team was to use machine learning to design models that allow for better characterization and classification of atrial fibrillation. Using this process, they were able to draw conclusions about the length of hospital stays and the risk of serious complications during the procedure.

Revolutionizing preventive care

If Bollmann’s team’s findings make it to market, they could revolutionize prediction and preventive care. The method developed during the Hackathon might lead to personalized risk forecasting. “Before a procedure, we inform patients about their risk,” Bollmann explains. “To evaluate the risk, we use average values. However, the risk of a somewhat healthy 50-year-old is different from that of an 80-year-old with severe comorbidities.” Using special software, physicians could enter data such as age, gender and previous illnesses and then calculate the individual risk of the patient.