For decades, Rigshospitalet has partnered with companies to influence the development of its products. In recent years, these partnerships have become even closer.
Academic institutions have been partnering with the industry for years, but, with new possibilities to deploy technologies such as machine learning and artificial intelligence (AI), collaboration is becoming even more fruitful.
“We know what competencies we have and what we are experts at, but we are also aware that there are areas where we lack enough experience. If treatment and diagnostics are to be improved, new people must be invited in,” says Rasmus Møgelvang, head of the cardiac centre at Rigshospitalet.
By collaborating, the hospital’s departments can enter the ”engine room” where systems and products are developed, so they can actively shape the very core of solutions. As a result, collaborations have become much tighter in recent years.
Collaboration for the benefit of cardiac patients
The technology superusers at the cardiac centre are not only expected to learn how to use new solutions, but are also encouraged to contribute to their design. By inviting companies inside through partnerships, clinical experts at the hospital have a chance to work closely with the technological and digital engineers of the institution’s industrial partners. This speeds up the development process, benefitting both parties, and, ultimately, the patients.
Through the latest partnership of this kind, Rigshospitalet and GE Healthcare aim to deploy data-driven technologies in order to achieve improved outcomes for cardiology patients. The goal is to automate echocardiography, ultrasound of the heart, and to help cardiologists predict outcomes for patients with some of the toughest and most common cardiovascular diseases.
“The collaboration formally started with agreements to reach a goal where both partners have committed resources to developing the product,” Møgelvang says.
One part of the collaboration is to build tools that will make echocardiograms faster.
“Today it takes 45 minutes to image and measure 60-80 parameters and everything is done manually. It’s time-consuming but we are used to it and carry it out 12,000 times a year at Rigshospitalet. It would be so much faster and easier if the machine could do the scanning automatically and all our doctors would have to do is validate the results,” Møgelvang explains.
Automated scaning would not only save time but would also provide more accurate measurements than the manual method. Algorithms that can measure and analyse echocardiography data accurately and consistently each time would have an additional benefit: they could forecast how often a patient needs to be monitored and how the patient is expected to progress.
Need to be proactive
The need for a new solution originated from Rigshospitalet itself, because it is a very lengthy process to use echocardiography to diagnose patients, and the volume is much larger than the number of operations. The rate of cardiac patients who need echocardiography is also expected to increase 3-10 times over the next 10 years.
“If there are 3 times as many patients, we need to be more effective in examining them. We can’t just multiply the amount of our current resources and get 3 times as many systems, nurses and doctors. Instead, we need to make the systems smarter and more time-saving. Making them also more accurate and reliable would be the icing on the cake,” Møgelvang points out.
Rigshospitalet has been proactive and decided to look for companies to collaborate with. By partnering up with companies that offer solutions to the hospital’s medical niche, the cardiac department gets to codetermine the future of technology involved in patient care.
“Working next to each other means that there are fewer loops to complete before a change is made. Clinicians tell us what specific improvement they would like to see, and, 14 days later, their idea has been validated and implemented,” Møgelvang says.
Rigshospitalet is also working with GE Healthcare to help predict outcomes for patients with aortic stenosis and atrial fibrillation – two common cardiac diseases in the western world, especially amongst the elderly – by developing AI tools that can analyse retrospective data.
The goal is to offer more personalised treatment plans for patients suffering from these two diseases and, most importantly, to improve patient recovery rates.