What can healthcare learn from Formula One racing?
According to Dr. Sadiqa Mahmood, SVP of medical affairs and life sciences for Health Catalyst, race support teams leverage about 30TB of baseline data to create a digital twin of the car, track, and racer for simulation models that drive decisions at each race.
Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions.
But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.
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Thirty cents of every dollar spent on healthcare in the United States is waste. That’s over $1 trillion dollars. To pinpoint what to fix and how to fix it, the industry needs advanced data, analytics, and expertise to identify the problems contributing to this waste. Nearly everyone will experience healthcare, and data-informed knowledge and decision making can improve that experience.
As an increasingly vital ingredient in the development of new drugs, devices, and digital medicine solutions, data is creating new opportunities and challenges. At the November 2019 Frontiers Health conference, Dr. Sadiqa Mahmood, SVP of Medical Affairs for Health Catalyst, discussed the vast amounts of real-world data healthcare generates. She explained how this data is delivering on its promise to improve healthcare and how life sciences organizations can partner with Health Catalyst to gain actionable insights to drive discovery and innovation.
Industries outside of healthcare are already harnessing the potential of expanded data ecosystems. By example, Dr. Mahmood referenced Formula One Race Car Support teams, which use a range of sensor-measured car data (e.g., transmission fluid temperature, engine oil levels, tire temperature, etc.) from previous events to inform race-day decisions. This adds up to lugging about 30TB of baseline data from race to race around the world to create a digital twin of the car, track, and racer for simulation models. During a weekend race, each driving team might have 30 employees analyzing data at the track, while anywhere from 30 to 200 workers are analyzing the data back at the racing headquarters. With so much data and a finite number of eyes to study it, racing support teams only look at the anomalies, making informed decision quickly based on those differences.
Dr. Mahmood encourages healthcare to use the patient digital twin similarly to the race car support approach: by using data to not only develop precise and targeted therapies but also for population health management and public health programs, healthcare and life sciences can find insights to support unmet medical needs and R&D.
Thanks to inroads in healthcare technology and analytics, the elements for a digital healthcare ecosystem already exist. Innovators have digitized biological and medical data, developed wearable devices and chatbots, and introduced data platforms that integrate data from hundreds of sources—all with a global virtual presence. So far a limiting factor has been the silos where data resides. When healthcare shifts towards the massively transformational purpose of a real-time, connected, healthcare ecosystem, society will move closer to a world in which most disease will be prevented, diagnosed, and treated for all citizens.
See Dr. Mahmood’s full presentation here to learn more about the potential of a global healthcare ecosystem, real-world data, and evidence.
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