Innovative potential based on global experience. In March 2019, Fresenius Medical Care set up the global medical office to strengthen the activities of the patient care company. This global feature represents an important step in transforming healthcare globally, based on the company’s vertically integrated business model.
Every time he visits the dialysis center, Len Usvyat sees not only the center’s patients and staff, but also the data. The special noises in the dialysis room, the patient’s last blood test – almost every aspect of the station’s daily life can be turned into fingerprints, explains the graduate economist and vice president of advanced analysis at Fresenius Medical Care.
He turned data assessment into his mission: to use the universal language of numbers to improve the treatment of people with kidney disease. “I am glad that behind each data set there is a patient with his own feelings and emotions, which we can positively influence,” says Usvyat.
We are looking for models in our data that we can translate into useful and practical information for health professionals.. Usvyat leads a diverse team of epidemiologists, IT professionals, engineers and pharmacologists at Fresenius Medical Care. “My team’s role is to bring innovative data-driven solutions to every corner of our organization,” he says. “We are looking for models in our data that can be translated into useful and practical information for health professionals.”
Big data scientists use computer algorithms to describe, predict the evolution of kidney disease and change direction for the better. Experts describe these three different approaches as descriptive, predictive, and prescriptive analyzes. The Usvyat team can benefit from a huge data set: Fresenius Medical Care has access to data from over 1.9 million dialysis patients worldwide, 1.7 billion laboratory tests and 500 million completed dialysis treatments.
However, scientists analyze not only traditional medical data, but also a variety of other information, such as weather forecasts, demographics and traffic data. “At first it may not seem like it, but all this data can be valuable to us,” says Usvyat. Computers look for huge data sets to repeat patterns, striking discrepancies, or previously unknown correlations.