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Deep empathy: How AI can strengthen doctor-patient connections

Some experts may worry that AI will depersonalize health care, but others see its potential to deepen relationships.

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  • Today's rate of innovation and change has made it difficult for patients and physicians to effectively integrate technology into medical best practices.
  • Experts agree that physicians need more time in their day to build bonds with patients.
  • Dr. Eric Topol believes that artificial intelligence may help restore that time, creating what he calls "deep medicine."


Today's rate of technological change is as unprecedented as it is unpredictable. This speed of innovation has created medical marvels that improve and save lives. Other technologies, however, have proven more difficult for physicians and patients alike to integrate successfully into health care practices.

"Exhibit A is the electronic health record (EHR), which has made the blood of countless physicians boil with frustration," writes Michael Dowling, president and CEO of Northwell Health, in his book Health Care Reboot. Created to store, track and share patient records, "[t]he EHR can be a cruel taskmaster, demanding a doctor's attention during a patient visit and requiring numerous clicks to enter even basic data."

Physicians spend an average of six hours per workday logging clinical data into the EHR—and face-time with patients suffers. The average doctor-patient consultation clocks in at 18 minutes, and a fair amount of that time goes to logging information.

Like it or not, technology is part of the patient experience. One study found that barriers to widespread adoption of electronic personal health records will likely include computer anxiety and concerns for security and privacy.

For better or worse, technology is affecting the doctor-patient relationship. According to Eric Topol, executive vice president of Scripps Research, the most beneficial change can come if we properly navigate artificial intelligence.

Will AI replace doctors?

With AI taking on the routine work, doctors will have more time to be actively involved with patients and referring physicians.

Photo: Tom Werner/Getty Images

That may sound counterintuitive. Technology like EHRs have affected doctor-patient interactions, and when we speak of AI entering a job market, it's with premonitions of the robopocalypse. Consider America's roughly 2 million truckers, who may lose their jobs to self-driving vehicles.

Yet blue-collar jobs are not the only ones subject to AI takeover. Some jobs that require the most advanced education are more likely to become obsolete, according to entrepreneur Andrew Yang. "Doctors, lawyers, accountants, wealth advisers, traders, journalists, and even artists and psychologists who perform routine activities will be threatened by automation technologies," he writes in The War on Normal People.

Day-to-day workplace routines will determine whether AI can perform a job, because the technology can perform routine tasks faster and more accurately than people, without needing a break.

To pick one example from medical practice, radiologists spend much of their time analyzing patient films. It takes years of education to develop that skill. Even then, certain diagnoses can be tricky and human deficiencies, such as confirmation bias and inattentional blindness, can lead to mistakes.

Deep learning could streamline the process of analyzing medical images. One day, AI may be able to read more medical images more quickly and compare them to a catalog exponentially larger than anyone could memorize. It may also detect anomalies too fine for detection by the human eye. And you only have to develop an AI once, as opposed to the extensive costs of training and maintaining human radiologists.

AI is unlikely to eliminate the need for radiologists, but rather it may enable radiologists to be more actively involved with patients and referring physicians as part of the care team. We're years away from AI becoming commonplace in radiology departments. However, the principles are sound and the technology is already under development. Some day, when AI can manage standalone diagnosis for routine cases, radiologists will be free to focus on the most challenging cases.

AI will free up radiologists' time to work on the most challenging cases. Here, neuroradiologists in Paris operate on a patient affected with an arteriovenous deformation.

Photo GERARD JULIEN/AFP/Getty Images

Deep learning, deeper empathy

In Deep Medicine, Topol suggests that well-implemented AI can free physicians from repetitive tasks, providing more face time to meet, inform, reassure and follow up with patients. It can also minimize burnout and improve health care quality. Topol cites one study from the National Bureau of Economic Research that found for every extra minute a home visit lasts, risk of readmission was reduced by 8 percent.

The same gains may be possible with EHRs. Integrated AI can make it easier to log entries, consolidate records, and draw data from external sources such as a patient's smartwatch or mobile device.

"Human performance is unlikely to change materially over time. But machines will progressively outperform humans for various narrow tasks," Topol writes. "To take humans to the next level, we need to up our humanist qualities, that which will always differentiate us from machines." He calls deep learning's potential to support medical empathy and outcomes "deep empathy."

A humane pairing

Busywork and routine labor so severely cut into physician schedules that Danielle Ofri, an associate professor of medicine at New York University School of Medicine, has suggested imposing fines on hospitals that detract too much from patient face-time.

As the National Bureau of Economic Research survey suggests, health care is a field where literally every minute counts.

"Most importantly ... when people are sick, they need empathy," Topol told Big Think in an interview. "They need the person who is their doctor to be with them, to understand what they're going through, because being in pain and being sick is the loneliest thing in the world. And if you don't have a doctor that is empathic, that is the worst-case scenario. We've got to get that back."

But Topol indicates a caveat: Implementing AI in health care just as an efficiency tool would counteract potential gains in doctor-patient relationships.

Michael Dowling agrees. As he told Big Think in an interview: "A lot of publicity has been given to a lot of these [big tech] players. But the core of the care being delivered to people who are very sick is still being done at hospitals and doctors' and ambulatory sites."

And that core must be building a humane — and, indeed, human — doctor-patient relationship.

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A massive star has mysteriously vanished, confusing astronomers

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  • The massive star in the Kinsman Dwarf Galaxy seems to have disappeared between 2011 and 2019.
  • It's likely that it erupted, but could it have collapsed into a black hole without a supernova?
  • Maybe it's still there, but much less luminous and/or covered by dust.

A "very massive star" in the Kinman Dwarf galaxy caught the attention of astronomers in the early years of the 2000s: It seemed to be reaching a late-ish chapter in its life story and offered a rare chance to observe the death of a large star in a region low in metallicity. However, by the time scientists had the chance to turn the European Southern Observatory's (ESO) Very Large Telescope (VLT) in Paranal, Chile back around to it in 2019 — it's not a slow-turner, just an in-demand device — it was utterly gone without a trace. But how?

The two leading theories about what happened are that either it's still there, still erupting its way through its death throes, with less luminosity and perhaps obscured by dust, or it just up and collapsed into a black hole without going through a supernova stage. "If true, this would be the first direct detection of such a monster star ending its life in this manner," says Andrew Allan of Trinity College Dublin, Ireland, leader of the observation team whose study is published in Monthly Notices of the Royal Astronomical Society.

So, em...

Between astronomers' last look in 2011 and 2019 is a large enough interval of time for something to happen. Not that 2001 (when it was first observed) or 2019 have much meaning, since we're always watching the past out there and the Kinman Dwarf Galaxy is 75 million light years away. We often think of cosmic events as slow-moving phenomena because so often their follow-on effects are massive and unfold to us over time. But things happen just as fast big as small. The number of things that happened in the first 10 millionth of a trillionth of a trillionth of a trillionth of a second after the Big Bang, for example, is insane.

In any event, the Kinsman Dwarf Galaxy, or PHL 293B, is far way, too far for astronomers to directly observe its stars. Their presence can be inferred from spectroscopic signatures — specifically, PHL 293B between 2001 and 2011 consistently featured strong signatures of hydrogen that indicated the presence of a massive "luminous blue variable" (LBV) star about 2.5 times more brilliant than our Sun. Astronomers suspect that some very large stars may spend their final years as LBVs.

Though LBVs are known to experience radical shifts in spectra and brightness, they reliably leave specific traces that help confirm their ongoing presence. In 2019 the hydrogen signatures, and such traces, were gone. Allan says, "It would be highly unusual for such a massive star to disappear without producing a bright supernova explosion."

The Kinsman Dwarf Galaxy, or PHL 293B, is one of the most metal-poor galaxies known. Explosive, massive, Wolf-Rayet stars are seldom seen in such environments — NASA refers to such stars as those that "live fast, die hard." Red supergiants are also rare to low Z environments. The now-missing star was looked to as a rare opportunity to observe a massive star's late stages in such an environment.

Celestial sleuthing

In August 2019, the team pointed the four eight-meter telescopes of ESO's ESPRESSO array simultaneously toward the LBV's former location: nothing. They also gave the VLT's X-shooter instrument a shot a few months later: also nothing.

Still pursuing the missing star, the scientists acquired access to older data for comparison to what they already felt they knew. "The ESO Science Archive Facility enabled us to find and use data of the same object obtained in 2002 and 2009," says Andrea Mehner, an ESO staff member who worked on the study. "The comparison of the 2002 high-resolution UVES spectra with our observations obtained in 2019 with ESO's newest high-resolution spectrograph ESPRESSO was especially revealing, from both an astronomical and an instrumentation point of view."

Examination of this data suggested that the LBV may have indeed been winding up to a grand final sometime after 2011.

Team member Jose Groh, also of Trinity College, says "We may have detected one of the most massive stars of the local Universe going gently into the night. Our discovery would not have been made without using the powerful ESO 8-meter telescopes, their unique instrumentation, and the prompt access to those capabilities following the recent agreement of Ireland to join ESO."

Combining the 2019 data with contemporaneous Hubble Space Telescope (HST) imagery leaves the authors of the reports with the sense that "the LBV was in an eruptive state at least between 2001 and 2011, which then ended, and may have been followed by a collapse into a massive BH without the production of an SN. This scenario is consistent with the available HST and ground-based photometry."

Or...

A star collapsing into a black hole without a supernova would be a rare event, and that argues against the idea. The paper also notes that we may simply have missed the star's supernova during the eight-year observation gap.

LBVs are known to be highly unstable, so the star dropping to a state of less luminosity or producing a dust cover would be much more in the realm of expected behavior.

Says the paper: "A combination of a slightly reduced luminosity and a thick dusty shell could result in the star being obscured. While the lack of variability between the 2009 and 2019 near-infrared continuum from our X-shooter spectra eliminates the possibility of formation of hot dust (⪆1500 K), mid-infrared observations are necessary to rule out a slowly expanding cooler dust shell."

The authors of the report are pretty confident the star experienced a dramatic eruption after 2011. Beyond that, though:

"Based on our observations and models, we suggest that PHL 293B hosted an LBV with an eruption that ended sometime after 2011. This could have been followed by
(1) a surviving star or
(2) a collapse of the LBV to a BH [black hole] without the production of a bright SN, but possibly with a weak transient."

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