As part of Medical Design & Outsourcing‘s ongoing series of conversations about the cloud’s contributions to the medical device industry and its future, Lynch offered her perspective from the Alphabet (NASDAQ:GOOGL) subsidiary’s cloud-computing business. The following exchange has been edited for clarity and length.
MDO: What are some surprising and inspirational examples of what cloud computing enables with regard to medical device/drug development, manufacturing and capabilities/performance?
LYNCH: First is using data and artificial intelligence (AI) to quickly respond to emerging crises. COVID-19 accelerated the pace of innovation in the healthcare industry, including the development of vaccines, adoption of telehealth platforms and the use of data and AI to solve urgent public health problems. For example, Google partnered with HCA Healthcare in an effort that united private industry, the public sector, and the nation’s hospitals to create the COVID-19 National Response Portal that promotes secure data-sharing about the pandemic and how it’s spreading to help hospitals and communities best prepare and respond. It provides information like the number of ICU beds and ventilators available and in use, as well as COVID-19 test results and geographic regions down to the county level. Google Cloud also created an expanded virtual agent to promote vaccine equity by helping people schedule vaccine appointments and ask common questions through a virtual agent, in up to 28 languages and dialects, via chat, text, web, mobile or over the phone.
Second is unlocking insights from vast data. Google Cloud is helping organizations gain a centralized, holistic view of data from multiple systems and sources. Sepsis, an autoimmune response to infection, is one of the deadliest and most expensive conditions treated in U.S. hospitals, affecting approximately 750,000 Americans each year. Early detection and prevention could dramatically save lives, money and resources, yet there’s no reliable way to diagnose sepsis quickly. Researchers at Emory University used Google Cloud to create an algorithm that uses 71 factors to predict the onset of sepsis in patients within four to six hours with 85% accuracy, improving the time to diagnosis.
Third is advancing scientific innovation. DeepMind’s AlphaFold machine learning system recently helped solve the protein structure problem, which has been a grand challenge in biology for 50 years. Determining protein structures experimentally is a time-consuming and painstaking process, but AlphaFold demonstrated that AI could accurately predict the shape of a protein, at scale and in minutes, down to atomic accuracy. Importantly, research organizations around the world can now leverage AlphaFold to better understand complex diseases and accelerate the development of new treatments, such as life-saving cures that disproportionately affect poorer parts of the world. We’re now making AlphaFold available for data scientists across the globe to use on Google Cloud.
Cloud computing is also being used to accelerate Industry 4.0, or what’s called “the fourth industrial revolution,” in manufacturing and supply chain. Many life sciences companies are adopting AI and machine learning to drive more automation and enable connected computers to make decisions with minimal human involvement. Factories can leverage AI to replace heavily manual processes and help reduce errors while freeing people to focus on higher-level tasks. This could come in the form of using voice commands to control certain operations, using computer-vision-based quality inspections of the factory floor and production line, or applying predictive analytics to the monitoring and maintenance of devices and equipment.
Google Cloud also has a solution that allows companies to create a digital twin of the supply chain that models a real-world scenario. Companies can plot the optimal routes and schedules for each vehicle using real-world traffic data from Google Maps and input origins, destinations, truck configurations and facility timing restrictions for pickup and delivery. They can then move fulfillment locations closer to end demand and determine if there are opportunities to trim the fleet in certain regions, reducing both costs and environmental impact. In one pilot program, Google Cloud helped a company reduce its fleet by 25%.
MDO: What are some previously unthinkable advances that now seem increasingly likely to become reality?
LYNCH: AI can enable faster, more accurate diagnosis, increase productivity and efficiency of care delivery, and improve access to better care and outcomes for patients. Google has years of expertise leveraging computer vision in other areas of our business such as Google Image Search and Google Photos, and we’re now working with healthcare organizations to help them accelerate development of artificial intelligence/machine learning for medical imaging using the same tools that power Google. As more and more medical images are digitized and moved into the cloud, companies then have the secure, scalable storage needed to handle petabytes or even exabytes of data — image files are extremely large — and the computing power required to perform advanced analytics and AI model training. Google has worked with many organizations to apply AI to help physicians prioritize critical cases for review, provide a “second opinion” to support treatment decisions, and also to expand access to screenings in areas where there are shortages of doctors. While it’s still early on the adoption curve for AI in medical imaging, it truly has the potential to improve patient care and save lives.
Another advancement I’d like to talk about is creating a 360-degree view of the patient, or a more holistic longitudinal patient record. Connecting data across the continuum of care empowers the healthcare ecosystem to better understand what’s driving variability in care and outcomes. Currently, this is mostly focused on data from electronic medical records, lab results and other clinical data, and it doesn’t include much data from medical devices. As companies invest in connected and intelligent devices — I’m referring to medical devices and also consumer devices like wearables and sensors — this will offer the potential to not only detect and manage health conditions, but also to predict and prevent diseases and deliver more personalized care. What’s needed to complete the longitudinal patient record, though, is device data interoperability. There’s no common data standard for devices, and they generate large volumes of streaming data that makes the data difficult to use. In a Harris Poll last year, nearly 90% of physicians said they’re looking for ways to bring together all patient data into a single place for a more complete view of health. At Google Cloud, we’re working to make the world’s health data accessible, interoperable and useful for organizations to help accelerate better insights, care and outcomes for patients.
MDO: And finally, what sort of big, futuristic dreams do these or other advancements inspire?
LYNCH: With the exponential growth of data in healthcare, the future of medtech will be about connected care. And in the future of connected care, technology will be a gateway to healthcare. I think it’s important to say that while AI has the potential to expand access to better care, we need to find ways to ensure equity, access and improved outcomes for everyone. AI technologies should work for the common good. I truly believe that big data and AI can help transform healthcare from reactive care to predictive, personalized and proactive care. But it will take all of us working together across the ecosystem — medtech and Big Tech, governments, health plans, health systems, research institutions and other stakeholders — to make this vision a reality.