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Harnessing Private Sector Strategies for Health Care

Question: How has Rwanda improved health care outcomes?

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Josh Ruxin: There are still enormous challenges for public health. There are many health centers in the country which lack running water, which lack electricity, and perhaps most importantly which lack the management that’s required to deliver good quality health care.

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Let me just give you one quick example of that. There are health centers which I frequently visit where there might only be three or four staff with a little bit more than high school education who are expected to provide health care for a population of 25,000 people. That would be hard for any three or four professional staff anywhere in the world to do. But in Rwanda where the training has been relatively light and where the resources are also less than ideal this is an enormous challenge, and pretty much unbearable and impossible to really improve the overall quality of health.

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What can you possibly do in order to change that situation? There are different diagnoses of it. A lot of people who will say, “Let’s bring in more nurses. Let’s bring in more doctors.” Others will say, “Let’s bring in new drugs. Let’s help to make sure that there’s a good drug pipeline.”

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In my work in Rwanda, we’ve actually made a related but slightly different differential diagnosis of the situation. Our diagnosis is it’s really about the management. If you’ve got a couple of people who are running a health center, the first thing that they need is access to private sector-type strategies for implementation.

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Do they know QuickBooks? Do they know basic accounting? Do they know how to get the job done, how to schedule their human resources, how to stay on top of the physical infrastructure? Do they have all of those skills first? Because everything else ends up following.

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Our experience so far is that we can actually go into a health center which is in disrepair working with the staff to improve those skills over the period of six months to a year, really get the health center on its feet, move up from seeing perhaps 15 or 20 patients per day, to a hundred patients per day, increase the income, increase the quality of services, and ultimately increase the outcomes, improve the outcomes for the poorest people perhaps in the world, certainly some of the poorest people in Rwanda.

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The big challenge today isn’t so much on the drug procurement side, but rather on the management side. Are there good systems in place for anticipating what type of drugs are going to be required and in what quantities? Do the health centers and the hospitals have good systems? Do they have good checks and balances? Are they able to ensure that they’re procuring the right drugs and they’re not getting counterfeit drugs? These are the types of questions which can be handled best by good health management leadership, and that’s an area that terribly demands new investment today.

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Recorded on: June 3, 2009.

Director of Millennium Villages Project in Rwanda Josh Ruxin urges investment in medical management.

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.

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  • 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

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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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