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What Is ROI in Medical Research?

Question: How much is the NIH's funding process affected by politics?

Francis Collins: We’re fortunate at NIH, that the Congress has in general has adopted a view that making priority decisions about scientific research is best done by scientists.  People talk about areas in other parts of the government where there’s earmarking or sometimes in a less friendly way called "pork" funding; we are relatively free of that.  Congress will certainly indicate to us when they think there is an area that needs more attention, but rarely will they attach a specific dollar figure to that.  They’ll just ask us to look at that a little more carefully.  So we have a very good working relationship.  

Likewise with the administration.  The administration is interested in seeing NIH be very productive and they want to hear all the time about how we’re spending our money and have us defend why it’s the right way to go.  But they’re generally reluctant to say, “Well you should spend X dollars on Y disease.”  And that just doesn’t seem like the way to make the choices.

Question:
How much say should the public have about what federal research money is spent on?


Francis Collins: Well the public does have a lot of say.  We have many disease advocacy groups who are constantly putting forward their case for why more research needs to be done on their condition, and of course, I would love to meet all of those requests, but we are often stuck in a situation where we’re limited in resources, and so we can’t do everything.  

But certainly we work I think pretty effectively with a lot of those groups to identify where are the areas that are most ripe for investment.  And sometimes that means coming up with an RFA because something is about to break.  Sometimes it’s organizing a workshop and trying to survey the field of a disease that seems to have gotten stuck for a while and figure out how to get it unstuck, and figure out how to get some new ideas and new scientific minds working on the problem.  I would say, for the most part, we have very productive, synergistic, friendly relationships with disease advocates who understand how the process works, are anxious to see resources put into their disease, but want it to be done in a fashion that’s scientifically productive and not just throwing money at the problem.

Question:
Are there areas of medicine or technologies where research dollars go farther?

Francis Collins: Return on investment is always an interesting question when it comes to medical research.  Well, what would you call "return?"  Is it that you’ve published a certain number of papers?  Well, that is one metric I suppose and that they are in high-impact journals, that’s another metric.  But really what we’re about is trying to help people.  

So the real return you’re looking for is clinical benefits, diagnostics, therapeutics, preventive measures.  The lead time on those is often measured in years.  And so it maybe quite difficult to assess when you’re just looking at a program that’s been underway for three or four years, how does it measure up in terms of what you’re getting for your dollars compared to some other program that similarly is sort of in an early stage of moving into clinical benefits?  But we try to do that to the extent we can and I think we should.  This is taxpayers' money; the taxpayers believe in us as the place that is gonna make that next breakthrough.  They want to be assured that we’re using those dollars in the most effective way possible.  Sometimes people think NIH is just, you know, playing around in the lab.  I can assure that’s not the view of people here, but we need to be prepared at any moment to defend the choices we’ve made as having had the best chance of benefiting real people out there who are counting on us to use their money wisely.  It is their money.

Recorded September 13, 2010
Interviewed by David Hirschman

The real "return" on research investments is in clinical benefits, diagnostics, therapeutics, and preventive measures. The lead time on those is often measured in years, so they can be hard to directly correlate to investments.

Does conscious AI deserve rights?

If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.

Videos
  • Does AI—and, more specifically, conscious AI—deserve moral rights? In this thought exploration, evolutionary biologist Richard Dawkins, ethics and tech professor Joanna Bryson, philosopher and cognitive scientist Susan Schneider, physicist Max Tegmark, philosopher Peter Singer, and bioethicist Glenn Cohen all weigh in on the question of AI rights.
  • Given the grave tragedy of slavery throughout human history, philosophers and technologists must answer this question ahead of technological development to avoid humanity creating a slave class of conscious beings.
  • One potential safeguard against that? Regulation. Once we define the context in which AI requires rights, the simplest solution may be to not build that thing.

A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

Photo by Alexander Hassenstein/Getty Images
Technology & Innovation
  • Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
  • The blend of three polymers provides enough flexibility and durability to mimic the knee.
  • The next step is to test this hydrogel in sheep; human use can take at least three years.
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Hints of the 4th dimension have been detected by physicists

What would it be like to experience the 4th dimension?

Two different experiments show hints of a 4th spatial dimension. Credit: Zilberberg Group / ETH Zürich
Technology & Innovation

Physicists have understood at least theoretically, that there may be higher dimensions, besides our normal three. The first clue came in 1905 when Einstein developed his theory of special relativity. Of course, by dimensions we’re talking about length, width, and height. Generally speaking, when we talk about a fourth dimension, it’s considered space-time. But here, physicists mean a spatial dimension beyond the normal three, not a parallel universe, as such dimensions are mistaken for in popular sci-fi shows.

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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
Technology & Innovation
  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

Image source: Creators Collective/Unsplash

Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

person leaning their head on another's shoulder

Image source: Külli Kittus/Unsplash

Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

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