A Digital Healthcare Team: Caring for the Aging Population

Nicole Flynn, M.Ed. Project Services Manager at The Center for Machine Learning and Health (CMLH)

My role at the Center is to serve as a faculty liaison for CMLH researchers, in assisting them through the entire research process and supporting their transition from lab to market.

Before coming to Carnegie Mellon University, I worked in non-profit healthcare for fourteen years. I’ve been part of a healthcare team, supported a healthcare team, and was even a patient of a healthcare team. During those years, a healthcare team meant to me working with physicians, nurses, allied health professionals, technicians, administrators and staff, IT teams, spiritual leaders, and county health departments – and at the center of it all, the patient.

But what happens to us as patients when we begin to age? Who will be part of our healthcare team? A human, a robot, both? Will we understand what types of sensors are being placed on us to take measurements and why one monitor beeps while another is performing facial recognition? Will we be living at home, with a family member, or in a newly re-designed senior care facility that passively monitors our every move and the food we eat? As discussed in the article by the National Council for Aging Care, ‘What Does the Future Hold for Senior Care?’ questions on senior healthcare could be on the minds of more than 80 million seniors by the year 2050 (U.S. Census Bureau) and government spending of $1.16 trillion on Medicare in 2027, approximately double of what was spent in 2016. As the aging population grows, this will create both unique challenges and opportunities for the healthcare industry and the patient.  Thus in my view, such changes will cause a continuous evolution of a digital healthcare team – a team that encompasses numerous attributes of the traditional healthcare team model of the U.S., woven together with new engineering and design principles, evolving personalized medicine concepts, digital arts, new economic modeling, AI, machine learning, and much more as the world of technology continues to evolve and surpass previous technologies.

“The benefit of changed population pyramids is that they force all of us to scrutinize our old ways of thinking and design new services and ways of delivering care,” as William Haseltine notes in ‘Aging Populations will Change Healthcare Systems All Over the World.’ Also, Haseltine points out, that it’s not only important for governments to plan decades ahead and study economic and social implications of aging, but as societies age, those involved in the healthcare and social care systems must adapt their services, and continuously learn.

Such continuous learning and adaptation is recognized by the PHDA and the importance this has on the aging population. As mentioned in ‘UPMC’s 2019 Community Health Needs Assessments and Implementation Plans – Allegheny County’, there is a sizeable elderly population with high social needs. Allegheny County has a large and increasing number of elderly residents (65+), with a higher percentage living alone, and a higher percentage of Medicare recipients than in the state and nation. To further explore unique challenges surrounding this population, the CMLH staff, along with CMU and Pitt faculty, sat down for an Innovation Session with colleagues from The Aging Institute of UPMC to explore healthcare challenges driven by the Aging Population in our community. The session sought to level-set, then “unpack” a challenge or need in the community, with the goal to enhance discovery and fuel solution building. The next step is to build collaborative teams that aim to develop innovative health solutions with the potential for future research funding.

In the meantime, two PHDA projects are currently underway that address aging challenges: Alzheimer’s disease (AD) and Adult Cardiac Surgery. In the first, researchers are combining areas of expertise and techniques that span machine learning, immunology, and genomics to develop new, low-cost methods of diagnosing AD and perhaps lead to possible treatments in the project ‘Developing a biomarker for Alzheimer’s disease using machine learning and immune cell epigenomics’.

Another project team, ‘Evaluating the Predictive Capability of Machine Learning Algorithms in Adult Cardiac Surgery,’ is investigating the application of machine learning to clinical cardiac surgery in ways that could have profound implications in clinical trials, therapy selection, patient prognostication and counseling, and surgeon and hospital evaluations.

Both of these projects are excellent examples of what the future could hold for creating digital healthcare teams who are working to improve the health of not just senior patients, but all patients. They are combining years of medical information, healthcare experiences, and patient understanding with what I like to call, cool ‘Star Trek-like technologies of the future.’ Let’s just hope that Dr. McCoy’s new machine doesn’t go beep in the middle of the night – as patients, we still need our sleep.

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