Susann Keohane is the global research leader for the Aging Initiative at IBM. Her current research focus is on cognitive IoT sensor systems for eldercare, applying advanced analytics and machine learning techniques to generate new insights that reduce risk and cost of care and significantly improve quality of elder life. She founded and runs the IBM Aging-in-Place Research Lab, which is designed to demonstrate IBM’s IoT-based AI solutions to support aging and create an environment to co-develop solutions with clients.
What kind of research are you currently focused on? If you are successful, what changes in healthcare?
I’m interested in how we can use emerging technologies, like AI and machine learning, to better understand what successful aging looks like. We’re experiencing a demographic shift across the globe where there are more people over 65 than under 5 for the first time in history. In many countries, there’s a shortage of care providers to go around. We know this trend will disrupt the entire healthcare industry and have long-term societal impacts. To better support our clients now and in the future, we felt this space was crucial for IBM to study the application of advanced analytics in elder care.
We just began a five-year longitudinal study with UC San Diego called AI for Healthy Aging. We know we can develop excellent technology to monitor patterns, prevent risk and reduce cost – but if no one adopts it, then it’s not going to have a great impact. So, what does adoption mean? How do you reduce the barrier between consumers and this technology? We’re learning that consent and adoption go hand in hand. If consumers understand and consent to using a technology, they are more likely to adopt it.
The elders of today are not the elders of tomorrow. A digital, connected environment is upon us where people are more curious about their daily patterns and are tracking their lifestyle with their devices. If we can turn these data points into insights, we may change behaviors that could help augment the aging experience.
How would you describe the current state of AI in healthcare? What big challenges need to be addressed?
With any AI or machine-learning initiative, I think it is critical to consider scale, security and personalization. How do we do sustain this? How do our solutions address cultural differences? And all the while, how can we maintain privacy and protection? As our digital health markets grow and data becomes more easily available, we need secure ways of managing patient data. Not everyone should have access to this data – a patient owns that information. At the heart of this, my team asks: How do we respect our client’s digital dignity?
How has healthcare data changed over the years? Is it just ‘bigger?’
I’ve learned from my colleagues at IBM Tokyo to approach people holistically. The body is an intersectional study. For example, a change in mobility often corresponds to cognitive decline. A variety of health (genetics, diet, clinical information) and social determinants (literacy, zip code, income) can impact a patient’s well-being. Health data isn’t your traditional health data anymore; we need to separate quality data from noise to gain new insights and drive better outcomes.
I imagine we’ll see a lot of progress in managing the influx of data in the years to come because that’s the promise of deep learning – to make correlations we might not first see. But with so many disparate data sources, organizations need to ask themselves: How are we going to process and manage our data? What infrastructure offers the right support for us?
Looking back at your own career – what particulars shaped your own development? What promoted or enabled your own productivity?
I’m what they call a lifelong learner. I think interest and curiosity are key characteristics in this field. What excites you? What are you curious about? Remain teachable and seek out the right people to teach you.
A colleague recently asked me: What advice would you give to aspiring technical leaders? You must be brave. Even if other people don’t understand your work or research, you need to inspire them and prove why you think it’s important. And through that bravery, you make your own opportunities. There’s a lot of research that can be done at IBM, but my team studies aging because we did the due diligence to show its importance to society and why it’s a challenging problem to be tackled.