The Digital Nephrology group of Charité awarded by the German Society of Nephrology

The Digital Nephrology group of Charité awarded by the German Society of Nephrology

26 October, 2020

The German Society of Nephrology (DGfN) has awarded the German Future Award of Nephrology to the working group “Digital Nephrology” of Charité – Universitätsmedizin Berlin represented by Dr. Wiebke Düttmann, Dr. Fabian Halleck, Prof. Dr. Klemens Budde and the projects they are working on such as BigMedilytics. The prize recognizes the digital innovations for integrative outpatient care in patients after kidney transplantation and in patients with chronic kidney disease.

Patients after kidney transplantation and with chronic kidney disease require special medical attention to avoid short-term and long-term complications. The working group “Digital Nephrology” at Charité strives to improve outpatient care and adherence using digital solutions. Through publicly funded projects, different projects were conducted successfully and help to gain knowledge in this field.

In collaboration of the working group consisting of computer scientists, nurses, medical doctors, and pharmacists with lawyers, app designers, and patients, we designed a user-friendly care concept. This includes an electronic health record (EHR), smartphone apps, and a platform that enables secure data flow fulfilling current data protection regulation requirements (e.g. we use HL7 FHIR).

A telemedicine team reviews home-measured vital signs of patients and takes action, if necessary. The data include also the patient’s medication plan and their updates from the outpatient department or associated general practitioner, alert functions, intake tracking, and communication tools, which also can send documents. All data are shared in nearly real-time and can be shared with external health care records.

The overall aim is to improve communication as well as data flow between patient, transplant center, and general practitioner to detect problems as early as possible and thus to avoid hospitalizations and complications. Furthermore, we use all data to establish prediction models for graft loss, rejection, infection, and loss of adherence. For that EHR data as well as home-measured data were used. These big data methods are standardized and can be scaled to other medical fields.