With so many significant developments in the battle against Covid-19, it’s critical that healthcare IT leaders stay educated. John Halamka, MD, president of Mayo Clinic Platform and frequent healthsystemCIO.com webinar speaker, recently returned to blogging after a 7-month hiatus; below, we’ve combined two recent pieces offering insights on different components of the Covid-19 response: the emergence of network medicine, and the digital transformation that will carry the industry forward.
New Insights into Disease Susceptibility
In the past year, we’ve become familiar with the factors that can make a person more vulnerable to COVID-19 infection. The elderly are more at risk, as are those who smoke and are already dealing with other diseases, such as cancer and Type 2 diabetes.
At a deeper level, though, there are dozens of other factors that may come into play and influence a person’s susceptibility to disease. A recent analysis of hospitalized COVID-19 patients in 14 states found that among patients ages 50-64, obesity was the most prevalent underlying medical condition. Similarly, there’s growing evidence to suggest that vitamin D deficiency contributes to COVID-19 infection.
The emerging field of network medicine, powered by this type of digital analysis of large data sets, sheds light on the interplay between microbial virulence and the ability of a person’s immune system to defend against diseases such as COVID-19. Network medicine allows researchers and physicians to look beyond the traditional root causes of disease and take a more holistic approach to identify agents that can influence a person’s susceptibility to disease.
In an article I co-authored with Paul Cerrato and Adam Perlman, MD, MPH, for Mayo Clinic Proceedings: Innovations, Quality and Outcomes, we describe how the analytic power of supercomputers and the emergence of big data sets has given researchers new insights into the causal relationships that influence susceptibility to disease. This technology dramatically improves our ability to assess the relative strengths and weaknesses of factors as contributing agents.
Some of these agents are not surprising — nutritional status, for one, and environmental factors. Others may be harder to assess, like sleep habits, exercise, physical and psychosocial stressors, obesity, protein-calorie malnutrition and emotional resilience. Genetic variations such as single-nucleotide polymorphisms also are examined as possible agents affecting a person’s vulnerability to disease.
With possible factors identified, deep learning algorithms can assess each’s likely strengths and weaknesses as contributing factors to disease and help identify therapeutic options.
Using machine learning-enhanced algorithms to analyze risk factors and their interactions can help determine which ones can predict a person’s risk of COVID-19 infection or the prognosis for someone who already has tested positive.
At a time when we’re all looking for reasons for hope and encouragement — and the national rollout of a COVID-19 vaccine is a big one — it’s good to remember that our capabilities to gain essential insights from AI, network medicine and deep learning algorithms are ever-growing and that we have the potential not only to resolve this pandemic more quickly but to completely redesign how we respond to pandemics in the future.
Recently, the Washington Post gave an in-depth analysis from the frontlines of the pandemic in Eau Claire, Wis. The piece portrays the compassion and ingenuity needed from frontline providers to meet patient needs during a COVID-19 surge — and is a reminder of why so many people pursue careers in healthcare.
The article highlights the deployment of a hospital-at-home model to increase hospital capacity for the surge. Rita Huebner’s experience with Mayo Clinic’s advanced care at home offering provides a great exemplar of how technology facilitates patient-focused change within the health care system.
Recently, Paul Cerrato and I published “The Digital Reconstruction of Health Care,” where we explored the digital transformation in healthcare that in turn will facilitate care delivery change. Artificial intelligence and remote monitoring enable new knowledge generation and cost efficiencies, and expand the care continuum. Our analysis examines the transition from brick-and-mortar to online care, providing a rationale for the shift.
Many industries have undergone digital transformation. Nine years ago, Uber launched an app and ride-sharing service that focused on connecting users seamlessly to the ubiquitous “black” cars prevalent in the major cities. This service’s facilitation through Uber’s platform grew in popularity, expanding to ordinary cars and flipping the taxicab industry on its head.
Healthcare is experiencing a similar digital renaissance that will change how some elements of care are delivered. COVID-19 has accelerated the adoption of telemedicine, hospital-at-home and remote patient monitoring. The capabilities offer valuable methods for scaling the health care system, achieving cost efficiencies, and expanding the care continuum. However, as we see in Wisconsin, people will remain at the heart of the strategies and care.