“Due to my previous illness, this is not my first stay in hospital, but the current situation makes it very difficult for me to keep my hopes up alive,” said Isolde Peters (name changed), 92-year-old heart patient who has been at Heidelberg University Hospital for seven days. Due to the nationwide COVID-19 protection measures, she is largely isolated from the social world outside. “I hardly see my children and my grandchildren live in the US. I worry every day.”
On initiative of the Heidelberg research network Informatics for Life the “#Hearts-against-Covid” campaign was launched bringing patients and their relatives virtually together. Thanks to a generous donation from the Klaus Tschira Foundation, tablet computers for video telephony were purchased enabling patients without smartphones to access virtual communication. “Before, I could not have imagined this, but when you are isolated in hospital, the greatest joy of my day is to see my grandchildren smile at me and to see that everyone is doing well,” says Isolde Peters visibly relieved.
“We have created a new way of communication for patients during these difficult times. Seeing their family is more than just entertainment. It is medicine,” said Benjamin Meder, member of the board of directors of Informatics for Life. “In the meantime we have not only created new possibilities for our patients, but we are also conducting all physician training courses via the digital channel.” Heidelberg medical students are also involved in the project helping patients to use the new technology and providing them with advice and support.
Professor Meder’s team also views the digital approaches as a long-term solution to various issues. For example, virtual meetings can help to conserve valuable resources and to reduce greenhouse gases in the future.
Foto: Medical student from Heidelberg is taking part in a virtual training course at the Department of Cardiology, Angiology and Pneumology (Director: Hugo A. Katus). The students are also very committed to explaining the new technology to patients.
How long should a patient stay hospitalized after surgery, what conditions affect the recovery and how can we care for our patients in an optimal way?
With the help of Artificial Intelligence and a specially programmed algorithm, our assistant physician, Dr. Bruna Gomes, addresses the questions in her current research project on patients with heart valve diseases after a Transfemoral Catheter Assisted Aortic Valve Replacement (TAVI).
Computer programs with artificial intelligence (AI) are initially “fed” with numerous patient data sets and search this mass of digital data for predictive factors in order to predict certain events (e.g. complications or mortality).
While traditional statistical methods and classical risk scores take into account only about 10 characteristics of a patient in this probability calculation, the algorithm developed by Dr. Gomes – through Machine Learning – captures about 80 characteristics, learns continuously and gives a much more differentiated picture of the patient. “You can compare it to a photo that traditionally has 500 pixels, but our algorithm now allows us to display this image with 4000 pixels, giving us a much higher resolution image,” Dr. Bruna Gomes explains.
With this highly-differentiated image of the patient, Gomes and her colleagues in Prof. Benjamin Meder’s research team hope to improve the assessment of each individual patient in order to enable the best possible prediction of the course of our patients’ disease according to TAVI.
Currently, the algorithm is based on the already evaluated findings of a patient, but the goal is to evaluate imaging diagnostics (CT, MRI, Echocardiography) also by AI, to integrate them into the algorithm and to combine all available information.
When patients are admitted to the hospital with chest pain, it is crucial to determine as quickly as possible whether or not a heart attack has occurred.
By combining artificial intelligence and RNA molecules, scientists at the German Centre for Cardiovascular Research (DZHK) have developed a method by which unstable angina pectoris, a condition that can result in myocardial infarction, could in future be detected earlier and more reliably.