To respond effectively to a global pandemic, governments need to have access to very large volumes of healthcare data, collected over a long period of time with patient privacy protected, and sourced from many locations and populations around the world.
Last August, Dr. Eric Topol, Director of the Scripps Research Translational Institute, and Kai-Fu Lee, Chairman and CEO of Sinovation Ventures, published “It Takes a Planet” in Nature Biotechnology. They highlighted the challenge of sharing healthcare data (EHRs not compatible, even in the same country) and the benefit of ethnic diversity of the data.
The issues related to privacy and country-specific regulation could be answered, they argued, by applying “federated learning,” where data never leaves the healthcare system where it is stored but the training of the machine-learning models on each data set is subsequently combined. “[Global] harmonization of key issues, including data security, privacy, rights of data subjects, liabilities and regulatory oversight, would be required to move forward, but are not insurmountable challenges...Think of the truly global learning system for healthcare that awaits development.”
Six months later, the existence of such a global healthcare infrastructure for data sharing and learning would probably have made a difference to the effectiveness and timeliness of each country’s response to the novel coronavirus. In late May, Kai-Fu Lee published in Wireda sober assessment of our global failure: “We have seen how vulnerable our health care systems are: insufficient and imprecise alert responses, inadequately distributed medical supplies, overloaded and fatigued medical staff, not enough hospital beds, and no timely treatments or cures.
” All the more reason to continue to advocate for a planet-wide effort “to move our global healthcare systems to the next level” where “a novel coronavirus could be tracked, traced, intercepted, and cut off before it got going.”
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