Join the Zurich University of Applied Sciences (ZHAW) at its inaugural Digital Health Lab Day this October 3, 2019. Organized by the ZHAW Digital Health Lab, the event will feature interesting discussions by researchers and practitioners on the latest trends and solutions in digital health, exciting presentations on ZHAW research projects, as well as interactive workshops.

I recently spoke with Prof. Dr. Sven Hirsch, the Scientific Chair of the event and director of the lab, on why interdisciplinary collaboration is necessary for healthcare innovation, and what event attendees can expect at the first ZHAW Digital Health Lab Day.

Aisha Schnellmann: Why was ZHAW Digital Health Lab founded, and what is the role it intends to play in the healthcare industry in Switzerland and internationally?

Prof. Dr. Sven Hirsch: The ZHAW Digital Health Lab is a virtual ZHAW-wide competence centre established at the end of 2018. It brings together experts from the fields of technology, healthcare, applications and health economics within ZHAW. This strong interdisciplinary collaboration is what enables the ZHAW Digital Health Lab to develop patient-oriented solutions and innovation that meet the current challenges of digitisation in healthcare.

AS: Tell us more about your role at the ZHAW Digital Health Lab.

SH: We manage the lab together with our board of directors and are in the phase of ramping up our visibility. Our lab cooperates with national associations, start-ups, established companies, hospitals, insurers, health service providers, and university partners. The next step we are working on is internationalisation. We have currently established contacts in the Greater Boston Area, USA, and India to institutionalise cooperation, and are in discussion with partners in the EU.

AS: You are currently working on a project that uses sensor technology in disease characterization of intracranial aneurysms. What do you hope to achieve with this work, and what is your personal motivation behind this endeavour?  

SH: Through this research project, we intend to improve the analysis and prediction of intracranial aneurysms – little pouches in brain vessels – that could be dangerous to patients. To look for disease patterns, we have built statistical tools enhanced with machine learning to better analyse large amounts of clinical data. These insights directly benefit patients by improving the decision process of disease management.

We have always collaborated closely with clinicians to solve real-world problems with better quantitative or mental disease models. Because our purpose-driven research is highly interdisciplinary, we work with clinicians, biologists, computer scientists, and engineers to benefit patients. It is definitely rewarding to see our research saving lives and to be a part of such a diverse research community intent on improving healthcare.

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AS: How would you describe the digital health landscape in Switzerland? Who are the main drivers of digital health innovation in the country?

SH: Switzerland is a hub for artificial intelligence, machine learning, and health technology supported by excellent corporate and academic research. But in my opinion, the digital health landscape in Switzerland is still fragmented.

Nationally, innovation in digital health is strongly driven by the bigger universities. There is an increasing number of digital health start-ups, but these are still in their early stages. The big pharmaceutical multinational companies are primarily interested in the development of new drugs or in improving their existing products. The hospital and care provider landscape is further fragmented, with the electronic patient record still pending after years in development.

We have all the right ingredients, but nobody is putting these together to prepare a meal.

AS: What is certain though is that new technology will continue to impact healthcare delivery in significant ways. What are some examples of ongoing ZHAW projects that are leveraging on such new technology, that will be showcased on the Digital Health Lab Day?

SH: At the Digital Health Lab Day, we will discuss the latest trends and solutions in digital health such as technology-assisted movement training for people with limited functional capacity, the significance and reliability of smartphone accelerometers, and the state of AI in healthcare.

AS: New applications of health data will also be discussed at the event. What are some new ways ZHAW projects are processing and using health data to advance medical research and development?

SH: For example, the ZHAW Digital Health Lab is working on a collaborative customer-oriented project with the Department of Health and a major insurance company involving evidence-based tips on health topics and mobile apps to improve healthcare behaviour. The lab is also working on FairCare, an EU project focused on improving the coordination of formal and informal care.

AS: What do you think are the top three emerging technologies that will transform the healthcare industry and how medical care is provided?

SH: I believe a paradigm shift needs to happen in healthcare. Healthcare systems need to be human-centric with a focus on providing truly personalised medical care. Harnessing and unifying health data in smarter ways is one step in this direction. Connecting patient data into registries, reducing barriers to use of health data for statistical purposes, analysing health data from wearables, and enabling the interpretation of sensor data in real-time in clinics are examples of potential game-changers. Data fusion and machine learning will also become increasingly key tools in drug discovery and efficient clinical trial management.

At the same time, it is important to highlight that health data security and privacy should constantly be a top priority. Healthcare is highly regulated, and for good reasons. We should therefore collectively push healthcare innovation while safeguarding ethical rules.

AS: What can participants expect from its inaugural Digital Health Lab Day?

SH: The Digital Health Lab Day is a milestone in the work of the still young ZHAW Digital Health Lab. We are looking forward to keynotes from Prof Dr. Tavpritesh Segti (Indian Institute of Information Technology), Prof. Dr. Claudia Witt (University Hospital Zurich) and Dr. Ignacio H. Medrano (Savana Médica). In addition, there will be practice-oriented workshops in the afternoon. The event will end with a networking dinner where you can make new contacts and shape the future of healthcare with us!

We look forward to seeing you there! For more information, please visit:

 

 


About Prof. Dr. Sven Hirsch

Sven Hirsch

Prof. Dr. Sven Hirsch is a researcher and lecturer in the field of complex biomedical systems at Zurich University of Applied Sciences (ZHAW). He heads the research group biomedical simulation and directs the ZHAW Digital Health Lab. In his work he merges statistical approaches like machine learning with mechanistic modelling to reproduce disease mechanisms and clinical pathways. He is active in health research to develop new disease biomarkers from clinical images, time series signals, and patient data to improve diagnosis and care. He has contributed to the understanding of intracranial aneurysm by simulating blood clotting and angiogenesis. His research activities now converge on digital health technologies and on making these promising tools useful.

 


About the author

Aisha Schnellmann is a Singaporean sociologist by training, interested in healthcare, education, and sustainability issues. She is passionate about producing content that promotes meaningful dialogue, focusing on print and digital content that resonates with a strong call-to-action. Based in Zurich, her interest in digital healthcare grew from the conversations she had with committed medical staff in rural hospitals in Asia, who remain hard-pressed with the technology available to them.

Artificial intelligence (AI) is set to transform the healthcare industry, poised to impact the cost, quality, and access to healthcare worldwide. From streamlining the drug research process, enabling personalized patient care, to fixing inefficient miscommunication within medical institutions, the list of ways in which AI will shape how we deliver patient care continues to grow. In fact, according to a research report by Global Market Insights, the global ‘Healthcare Artificial Intelligence Market’ is expected to surpass USD13 billion by 2025. The healthcare industry however, continues to present challenges that threaten to slow down this progress. What are the key opportunities and obstacles facing companies and institutions developing healthcare artificial intelligence, and how will they affect you? 

 We recently spoke to Susanne Suter, software engineer at Supercomputing Systems AG, at the ‘Women in Digital Health’ event where she was a guest speaker. She shared with us about these opportunities and challenges, and the innovative healthcare projects her team is currently working on to harness AI technology for good.

How do AI and machine learning work?

 “When we talk about AI nowadays, we mean data-driven systems and machine learning,” explained Susanne. During a training process, machines are essentially fed a tremendous volume of high-quality data, what each piece of data means. When this learning phase is completed, the machines are able to predict or classify any new data that is inputted based on its stored knowledge – hence developing its intelligence.

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This ability of AI to analyze tremendous volumes of medical data and recognize meaningful patterns within these data sets has proven groundbreaking in healthcare innovation.

The benefits of AI

 For example, AI technology has made it possible to detect potentially life-threatening medical issues in patients early enough so that they can be treated quickly. Supercomputing Systems AG is working on a project with Prof.Dr.med. Emanuela Keller from the University Hospital in Zurich, to develop an AI research system that monitors and analyses real-time medical data of patients at the neuro-intensive care unit, predicting for example the occurrence of brain hypoxia in patients before it happens so that effective preventive action can be taken.

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Pharmaceutical companies are also exploring ways to adopt AI technology in drug development; speeding up the drug discovery process and reducing costs. For example, Berg Health is using massive volumes of data from patients with diseases such as prostate cancer in order to identify new targets and develop new drugs. In Europe, global biopharmaceutical company Sanofi recently signed a 250 million Euros collaboration-deal with leading British drug design company Exscientia to discover bispecific small-molecule drugs against metabolic diseases.

The success of AI technology in healthcare, therefore, hinges fundamentally on its access to quality medical data – and lots of it. Collecting quality medical data however continues to present a real challenge for many companies working on harnessing AI technology for good.

Powering AI technology is challenging

Collecting quality medical data is time-consuming. “From my experience, 30 to 50% of time needed to develop an AI system is spent collecting quality data,” shared Swiss Supercomputing AG software engineer Susanne Suter. Furthermore, medical data in most countries continue to be siloed in disconnected systems that are difficult to access. In some cases, this data still resides in handwritten records stored in file cabinets in the doctor’s office. As expressed in Forbes, “Data must flow freely through AI systems to achieve real results but extracting data from handwritten patient files or PDFs is cumbersome for us, and difficult for AI.”

Successful collaboration by experts from fields including medicine and data science is also necessary in developing useful AI systems in healthcare. In a project with Dr. med. Peter Maloca from the Institute of Molecular and Clinical Ophthalmology Basel (IOB), Susanne’s team is developing automated analysis systems that can detect retinal tissues and medical conditions such as tumors in eyes. The team used over 2,000 images taken from an estimated sample of 650 different eyes. Each image had to be marked by experts, i.e. the retinal tissues are drawn on each image, before this data was fed to AI systems as part of the machine-learning process.

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Additionally, companies developing AI solutions for healthcare have to actively consider issues related to cybersecurity and data protection. For AI systems that need to be connected to the internet to make use of powerful cloud-based backends, sufficient cybersecurity needs to be put in place to protect it against hacking. Because AI systems depend on the integrity of large medical data sets to be effective, sufficient measures also need to be put in place to ensure that medical data (e.g. from databases maintained by hospitals and other medical institutions) remain protected.

What’s next

The potential AI in technology has in transforming how we receive medical care continues to grow. However, much of this progress still depends on the speed at which the healthcare industry successfully undergoes digital transformation. Additionally, as its success hinges on its access to a high quantity of medical data, questions remain about how the development of AI technology in healthcare will benefit from current efforts to return data ownership to the people – efforts that thereby free medical data previously siloed in medical institutions and restricted by data consent laws, back in the control of people who can consent their transaction.

“AI will ultimately help us in decision-making, but we are still quite far from a reality where machines can think and draw their own conclusions. And in thinking about how to collect quality data, it’s important to remember, the person who owns the data has the power,” concluded Susanne.

The future of AI in healthcare looks bright, and it will surely be an exciting area of healthcare innovation to watch over the next few years.

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Susanne Suter, Dr. sc. Computer Science University of Zurich, has been successfully involved for over 15 years in multidisciplinary innovative projects at the interface between computer science, biology and medicine (including scientific prizes, third-party funds and publications). Since four years, she is working for Super Computing Systems as a software project leader and engineer producing custom-tailored medical software systems such as a patient monitoring system at a neuro-intensive care unit, second-opinion case-review systems for medical doctors, and an automated surveillance service to track the health condition in human eyes.

About the author

Aisha Schnellmann is a Singaporean sociologist by training, interested in healthcare, education, and sustainability issues. She is passionate about producing content that promotes meaningful dialogue, focusing on print and digital content that resonates with a strong call-to-action. Based in Zurich, her interest in digital healthcare grew from the conversations she had with committed medical staff in rural hospitals in Asia, who remain hard-pressed with the technology available to them.

For more information about the next “Women in Digital Health” events