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The Sentinel Initiative — How the FDA Might Know Why You’re Sick Before Your Doctor Does
EAS
Last modified on November 13, 2018 at 3:41 pm
An examination of the potential to harness big data to improve public health and safety using natural language processing.

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On November 15, 2018, EAS commented on Speeding the Drug Discovery Pipeline with Open Innovation :

This is really fascinating. Would this process be applicable to biologics or is it really limited to small molecule products?

I think the competitive advantage concerns that you raise and that others reiterate here are real. One idea is creating a tax or additional exclusivity incentive for drugs that are developed out of this open innovation process would provide the boost needed to spur other larger drug and biotech companies to participate. This would function similar to current exclusivity or tax credits, and could even be extended to a priority review voucher (though I tend to favor these less given the potential safety concerns that using them on blockbuster drugs creates).

On November 15, 2018, EAS commented on The Sentinel Initiative — How the FDA Might Know Why You’re Sick Before Your Doctor Does :

Because the data in Sentinel is aggregated, in theory, no. However, the FDA would need to be sure that they have strong compliance systems to convince providers and EHR vendors that this would in fact be the case. HIPAA violations are complex in their enforcement and doctors are incredible wary of engaging in any activity that has even the faint whiff of a violation. Thus, these compliance assurances would be needed to get providers and vendors to participate.

On November 15, 2018, EAS commented on Repair Time Reductions On Submarines Through Additive Manufacturing :

Super interesting! I completely agree with the clear arguments you make here around efficiency, cost, and reducing fleet downtime. Do you think there are national security risks to having the granular design specifications for submarines and other major vessels stored in AutoCAD format? My initial thought stems from foreign hacking into computer systems — if this information is distributed across many specialized manufacturers, the risk of a full vessel design being lifted seems smaller. The alternative is that all this information is already digitized, in which case there does not appear to be much of a downside to what you propose!

On November 15, 2018, EAS commented on When HHS becomes iOS: Harnessing Open Innovation to Combat the US Opioid Crisis :

I think for long term, non-emergency problems open innovation is an excellent solve. HHS should continue pursuing these open innovation challenges but I think you rightly recognize that computer code alone will not prevent someone with OUD from overdosing. This problem needs resources — and once that part is under control the coding can help manage and deploy those resources. Building out a digital infrastructure through open innovation is an important step towards the Department better responding to public health emergencies, but must happen in parallel with other resources that are deployed through those innovation vehicles.

On November 15, 2018, EAS commented on Recursion Pharmaceuticals: Machine Learning in Drug Discovery :

First, the pictures in this post are so helpful!

Responding to your second question, Recursion would be compelled to share these developments with FDA as they seek approval for the drugs. Outside that, they are under no legal obligation to share the technology unless portions of the basic science that underlie the technology were funded by government money (i.e. National Institutes of Health, National Science Foundation, or Department of Defense funding). In this case, the Bayh-Dole Act would allow the government to assert its ownership rights to the patent and from there they could share the technology as they saw fit.

From a social responsibility perspective, however, I think the question becomes a bit more complicated. An interesting question for the Recursion leadership team — perhaps pursuing joint ventures or partnership models where they can still reap profit but take advantage of the scale of the bigger pharmaceutical companies/spread more good would be a sufficient compromise?

On November 15, 2018, EAS commented on Additive Manufacturing in Medical Device: Can Johnson & Johnson stay ahead of the curve? :

Given the new nature of this manufacturing (and materials, in many cases), it will be interesting to see where the FDA lands in the formal approval process for these devices. Currently, they are allowing them under the 510(K) and de novo pathways, but have required more manufacturing information than a standard 510(k) submission. Given the critical nature of device material to patient safety and the relative novelty of 3D printing, how would your stance on this change if the FDA decides not to allow these pathways but instead creates a new comprehensive pathway that is subject to manufacturing process approval?

On November 15, 2018, EAS commented on Optum: Machine Learning at the center of Health Care :

While I would agree that Optum’s relationship to UHG is part of it’s competitive advantage in that it provides a wealth of data to plug into their system, it is also a weakness. Other private payers are much less likely to want to use Optum’s services for fear that their networks, reimbursement, and patient population data will be co-opted by UHC on the insurance sales side of the house. Given that all this is proprietary and important to making money in insurance markets that are increasingly regulated, I think there is a “ceiling” of sorts for Optum in the payer space.

I’m also interested how much the insights derived from AI in Optum can drive change in the health care system, given how regionally based the economics tend to be.