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We Can’t Reinvent the Automobile

Question: What is the major problem right now in transportation?

Michael Schrage: The major problem with transportation technology and transportation policy is that you have a battle between utopians who believe that they can get whatever they want, faster, better, cheaper, and the pragmatist, the hard ball economic types who say, "No, no, no, let's ban the car from the city, let's create calming zones, zero-emission vehicles," etc., etc.  You have a real dialectic, a real tension, and my view is, when you have that kind of schizophrenia, when you have that kind of chasm, not just of technologies and expectations, but of values, I think you get into a lot of gridlock--pun intended.

Question: Are there any promising advancements that will take us into that next era of transportation?

Michael Schrage:  Oh, my gosh!  There's so many things going on that are so exciting that it's not clear to me what's the best bet.  We have smart vehicles, smart devices, smarter people, better sensors, more information, the ability to have the technology act in a more nimble way.  We have the ability to play with tax policy and congestion charges.  So to me, the question is going to be, there's a tension between disruptive innovation and rapid iderative incremental innovation.  But if you have enough incremental innovation in a short period of time, you got a revolution.  You know, the simple model for that is like a Mapquest, or putting an accelerometer in an iPhone.  The fact of the matter is, between mobile phones, between iPhones, between Rim's, between GPS, every car in the world, every bicycle in the world, can be made smarter.  Heck, we can make every street corner, every streetlamp smarter, more nimble, more agile.  The issue is, what's the organizing principal underlying that?  Haven't a clue. 

Question: Do you think we need to build on what we have, or revolutionize mobility?

Michael Schrage:  Being an MIT person, I'm very, very sympathetic to that view of, gee, let's just start from scratch, let's reinvent everything.  But basically, I think it's a bunch of crap.  You know, that's basically like saying, gee, we've sequenced the human genome, let's reengineer human beings so they'll make less, they'll take up less space, they'll do less waste, they'll be more energy efficient.  As I said earlier, there's always going to be a tension between the incremental and the revolutionary.  Folks at MIT tend to self-select towards the revolutionary side, but ordinary human beings, typical human beings, human beings who have, shall we say, real lives, not MIT lives, are more prepared to have a mix of the disruptive innovation and the incremental innovation.  And that's how I think that's going to play out, particularly in transportation policy.  I don't believe people are going to give up on the wheels of a car for the foreseeable future.

Question: Is there something we’ll have to give up in order to get there?

Michael Schrage:  Well, you know, frankly I don't care if there's an internal combustion engine in a car, a fuel cell in a car, a battery in a car—I don't care.  My friends who are incredibly green care a lot.  Now, do I think they've actually done the math and calculated the carbon footprint of fuel cells versus batteries versus internal combustion engines throughout the entire supply and value chain?  Heck, no.  And I think that's where we're going to see a lot of the policy battles because there are all manner of ways of allocating costs and responsibilities for the carbon footprint of an automobile, for the carbon footprint of a person.  Or should we say the carbon tire track?

Question: Will we ever not have a congestion problem?

Michael Schrage:  We will always have congestion problems.  The issue is what's tolerable.  If you had told me that I would be on a 40-minute commute, I would say you're crazy, but I've gotten adjusted to it.

By the way, let's deal with that as an example. 25 years ago, being stuck in traffic was a horrible, horrible waste of time.  If you got a mobile phone, if your kids are watching TV in the back seat, it's not so bad!  It's not so bad.  Now, what transportation planner anticipated the impact of mobile phones in the car and TV screens, LCD's in the back seat?  None of them!  None of them!  And heck, I've read a lot of science fiction; I didn't see those scenarios being played out by Isaac Asimov or Harlan Ellison either.

Recorded on January 22, 2010

"I don't believe people are going to give up on the wheels of a car for the foreseeable future," says the transportation researcher.

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

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  • Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
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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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