(My son Alexandre #keepingussafe during the pandemic)
International efforts to contain the spread of Coronavirus have reached new levels, especially in hard-hit areas like Italy, Spain, and New York City. As the pandemic spreads and takes root in more isolated areas it becomes clear that data plays an essential role. Data will help us understand the virus and data will help us contain it.
Data has always been the key to good public health
As researchers collect and analyze more data on the path of COVID-19, they are better able to track the virus, learn more about how it spreads, and try to predict its spread. Access to an open source of data is key for researchers who are working to develop solutions. Data has served as the building block of public health policy for over a century.
Access to factual information based on good data is key for the public who need and deserve to know about the pandemic so they can take the necessary precautions and stay safe. Lack of good information on the virus, combined with unclear or conflicting direction from leadership can trigger panic.
But this is the 21st century, and we can expect more from our data, can’t we? Could medtech have prevented the pandemic in the first place?
Key innovations can lessen the impact of infectious diseases
The foundation of an effective response to infectious disease is quick and accurate establishment of the cause of the disease. Is it a virus? A bacteria? In other words, what is the microbial agent? A continuing challenge of modern medicine is that patients do not always receive the appropriate therapy when they have infectious diseases.
This is because their doctor hasn’t identified the infectious agent — in the case of Covid 19, it’s a novel agent, making it all but impossible to correctly identify the pathogen during the early stages of the outbreak.
Innovations in technology can speed up the process of identifying a disease that’s clinically known but even with improved diagnostic testing, unless it translates into fast action on patient care, it’s not much of a solution. Testing and patient care units must be more fully integrated before the full value of medtech can be realized. Healthcare systems are still working on improving their electronic medical records systems. The infrastructure just isn’t there yet.
Once that is in place, however, the electronic reporting of lab test results will facilitate sharing of those results with patients and the data with other entities, including other doctors and public health researchers. As we stand now, there is still much need for data integration projects in the healthcare systems of the world. Simply unleashing AI before the infrastructure is ready isn’t going to bring us the results we need. So, we’re not there yet.
Artificial Intelligence can make predictions
Artificial intelligence (AI) runs scenarios and can predict outcomes. So, could AI have predicted Coronavirus? It may have been able to come across outcomes arising from either of the two present theories about the virus’s origins: the “wet market” of Wuhan or researchers studying bat diseases but we’re by no means there yet. The amount of data required — on what’s selling at the Wuhan market, for example, or what a small research lab in Wuhan is studying and how — would require an Internet of Things (IoT) infrastructure that’s far more pervasive than what exists now.
Medtech as we know it today may not have prevented the outbreak of Covid 19 — but it could have helped to develop tests faster and to ensure medical supply chains were sufficient. As it stands now, the Governor of New York State is pleading for much-needed, life-saving ventilators while the federal U.S. government sits on stockpiles of unused units. A U.S. Navy medical ship is weeks out from arriving in NY and Los Angeles, where it is predicted those areas will need the temporary extra hospital beds that ship will provide.
Is there a role for wearable medtech?
The development of tests is a process that’s data-intensive, which carves out a special role for medtech. Wearables can collect data on infected patients. But unless everyone is wearing them, and all the data is made public to researchers, AI won’t have much to work with.
Making wearable fitness app data available to researchers on an open data platform invites a new, troubling dimension to the matter: data privacy. Now the question isn’t, “Can medtech prevent pandemics?” but rather, “Should private medtech (wearable) data be used publicly?”.
Better data modeling could have helped with supply chain issues
Medtech could certainly have prevented many of the supply chain issues that are currently plaguing leaders. In the United States, the Centers for Disease Control (CDC) were so rushed in their efforts to roll out the diagnostic tests needed for Covid 19, they made used contaminated reagents in the kits, which essentially rendered them useless. Had they known sooner about what was happening in Wuhan, this would have been less likely to happen.
The respirator shortage could effectively be solved by efficiently shifting resources from region to region as needed, as the outbreak balloons in various epicenters. For example, New York City desperately needs them now. In a few weeks, once their cases subside but cases in other regions are only beginning to ramp up, those same ventilators could be transported to those regions that need them the most.
AI could also have predicted the shortage of masks. Leaders could have galvanized commercial enterprises to increase production. As it stands now, fashion brands are stepping up to help but cannot keep up with demand. AI could also have helped stores maintain appropriate inventory of basic necessities like toilet paper and hand sanitizer.
Massive data sharing, in real-time
In order to perform at levels where it’s capable of predicting the next Coronavirus, medtech must be enabled by an open-source data platform for medical researchers, practitioners, and public health officials. Massive amounts of personal data will have to be collected and shared in real-time, then accessed and analyzed by the best professionals in their field, as well as sophisticated AI. We have to move past the current system of handling data in analog style: data that’s not digital, not shared, and not accessible to the AI systems that could predict the next pandemic, thus saving lives.
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