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 * 07-11-22


MEET THE STARTUPS USING AI TO HELP DOCTORS FIGHT BURNOUT


A NEW WAVE OF HEALTHCARE STARTUPS IS USING ARTIFICIAL INTELLIGENCE TO REDUCE THE
LOAD ON MEDICAL PROFESSIONALS.

[Photo: Clay Banks/Unsplash]
 * 
 * 
 * 
 * 

More Like This
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of China
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By Adam Bluestein 6 minute Read

Even before COVID-19, 40% of physicians said they felt burned out. But the
pandemic was a tipping point. Working in jerry-rigged PPE in overcrowded,
understaffed ICUs, more than 3,600 U.S. healthcare workers died in the first
year of the pandemic alone. After bearing witness to the lonely deaths of some 1
million patients, holding the phone as they shared their final minutes with
family members via FaceTime, more doctors are deciding to retire early,
exacerbating a looming shortage. A report last year by the Association of
American Medical Colleges predicts a shortage of up to 124,000 physicians by
2034. That includes a gap of as many as 48,000 primary care physicians, who
report higher levels of burnout than other specialties. And it’s not just
doctors: In a January 2022 survey by Prosper Insights & Analytics, just 50% of
all healthcare workers said they were “happy” at work.




Happiness won’t be bought overnight. Staffing gaps will take time to fill. But
in the meantime, proponents say, artificial intelligence (AI) could be used to
help ease the burden on maxed-out MDs. “We need to turn every physician into a
super-physician,” says Farzad Soleimani, an assistant professor in emergency
medicine at Baylor College of Medicine and a partner at 1984 Ventures, a San
Francisco-based VC firm. “At the end of the day, what clinicians do is to learn
to recognize patterns. That’s the power of AI.” 

Of course, there are doubters. An April 2019 Medscape survey of 1,500 doctors
across Europe, Latin America, and the U.S. found that a majority were anxious or
uncomfortable with AI, with U.S. physicians expressing the most skepticism
(49%). Relying on algorithms for patient care also presents ethical, clinical,
and legal concerns. AI may bring considerable threats of privacy problems,
ethical concerns, and medical errors. Developers may unknowingly introduce
biases to AI algorithms or train them using flawed or incomplete datasets. Data
used to train AI systems could be vulnerable to hacking. By turning over aspects
of decision-making to machines, physicians could lose their traditional autonomy
and authority—and notions of liability will be tested should AI-guided
recommendations result in patient harm. 

Nevertheless, healthcare AI companies—including nearly 500 early-stage
startups—raised a record $12 billion in funding last year, according to CB
Insights. Here are just a few ways that tech companies are using deep-learning
algorithms and natural language processing to automate routine tasks in
hospitals, cut down hours medical providers spend on paperwork, and reduce
mistakes caused by fatigue.




SPEEDING UP PRE-VISIT EVALUATIONS

Managing patients and preventing provider burnout starts before care recipients
even show up to the office or hospital. San Francisco-based Health Note
streamlines patient intake with a text-based AI chatbot that collects patient
information pre-visit and automatically writes up notes for their doctor,
reducing intake and documentation time by up to 90%, according to the company.
Decoded Health—a spinoff of SRI International, the nonprofit research
organization that developed the tech behind the computer mouse, ultrasound, and
Siri—offers what it calls a “virtual medical resident” that prescreens patients
using natural language processing, creating a summary of their medical
complaints with actionable care recommendations. Keona Health focuses on helping
nurses and non-medical staff conduct triage over the phone, guiding them through
symptom checking, offering care recommendations, and automating appointment
scheduling.  


HELPING WITH TRIAGE

When the ER gets slammed, AI triage tools are designed to help flag patients who
need critical care and might otherwise be missed, flagging the most serious
cases and prioritizing them for care. The first major clinical application of AI
triage tools has been in radiology; companies including RapidAI, Viz.ai, and
Arterys all have FDA approval for algorithms that detect signs of strokes, brain
bleeds, and pulmonary embolisms from CT scans. Imagen‘s FDA-approved OsteoDetect
analyzes wrist X-rays to detect distal radius fractures, one of the most common
injuries to the joint. Mednition‘s real-time triage-guidance tool, KATE,
analyzes EHR data and patient vitals collected at intake to help emergency
nurses spot warning signs of sepsis, which accounts for more than 50% of
hospital deaths. It is being used throughout the Adventist Health system and
others to head off ER admissions through earlier treatment. ERs run by Johns
Hopkins University are using Stocastic‘s TriageGO, which analyzes vital signs
and other intake data, along with patient demographics and medical history to
make rapid care recommendations, reducing “door to decision” time by up to 30
minutes. 


TRANSCRIBING DOCTORS’ NOTES

A recent study found that physicians spend an average of about 16 minutes on
electronic health records for each patient visit. DeepScribe is a voice-based
digital assistant that allows a doctor to have a normal conversation with their
patient, transcribing it, pulling out key information, and automatically fitting
it into the proper sections of the medical records. In January 2021, the San
Francisco-based startup raised $30 million. Competitors include Nuance, Suki,
and Corti. 



There’s also Rad AI’s Omni software, a virtual assistant designed specifically
for radiologists that helps write a formal “clinical impression” based on
dictated notes, automatically inserting guideline recommendations and spotting
potential errors.


MANAGING THE BILLING PROCESS

“When folks talk about staffing shortages in healthcare they often think of
nurses, doctors, and frontline care staff, but the issue is organization-wide,”
says Ben Beadle-Ryby, co-founder of South San Francisco-based Akasa, a provider
of AI services for healthcare operations. According to recent surveys by the
Healthcare Financial Management Association, more than 57% of health systems and
hospitals have 100-plus open back-office roles—in billing, registration, and
scheduling—to fill. A survey by Change Healthcare found that 65% of healthcare
leaders are already applying AI in their “revenue cycle management,” and by
2023, 98% anticipate doing so.

Akasa provides services for more than 475 hospitals and health systems and
8,000-plus outpatient facilities in all 50 states, using a constantly learning
AI system to help them automate insurance claims status checking, prior
authorization, eligibility, and denials management. Privia Health provides
scheduling and billing tools for some 3,300 independent physicians, using
robotic processing automation—in which an intelligent system learns a scripted
process for handling repetitive billing tasks like a human would.




AIDING WITH TESTING

Lab tests shape roughly two-thirds of decisions made by physicians. Before
COVID-19, medical lab professionals performed some 13 billion lab tests a year.
In a February 2020 survey by the American Society for Clinical Pathology, more
than 85% of medical lab workers reported burnout; 36.5% complained of inadequate
staffing. That was before the additional burden of conducting well over 900
million COVID-19 tests since the pandemic began. Many hospital labs are running
with 10% to 35% staff vacancies.

Automating repetitive work could let fewer people do more, and perhaps improve
outcomes, too. In a 13-month pilot, the University of Texas Medical Branch
hospital in Galveston used Biocogniv‘s “laboratory intelligence platform” to
help process more than 325,000 COVID-19 tests and make personalized
interpretations based on PCR and antibody testing, patient vitals, and medical
history. The result: a near doubling in efficiency, lower rates of escalation to
intensive care, and reduced mortality rates. “COVID-19 was a time of immense
change both clinically and operationally,” says Peter McCaffrey, MD, an
assistant professor of pathology at the hospital and director of its pathology
informatics and laboratory information systems. “With Biocogniv’s platform, we
were able to scale interpretation and guidance for COVID and coordinate everyone
during this time of unprecedented uncertainty.” In the company’s pipeline:
laboratory-based prediction tools for sepsis, respiratory failure, and acute
heart failure.

Whether AI proves itself in each of these areas or not, there is no turning
back. “By minimizing or offloading repetitive diagnostic tasks, [AI can help]
physicians devote more time to sophisticated clinical reasoning and judgment,
and inherently human work such as engaging with multidisciplinary care teams to
support patient care,” says Mark Schuster, MD, a pediatrician and founding dean
and CEO of the Kaiser Permanente Bernard J. Tyson School of Medicine in
Pasadena, Calif. In addition to addressing physician specialty shortages in
areas like radiology, where AI has proven highly accurate, Schuster anticipates
that clinical care algorithms will become more powerful, with a “a gradual
increase in precision and personalization of diagnosis and treatment.” Still, he
acknowledges the potential danger that AI could reinforce biases that already
exist in the healthcare system. “We recognize,” he says, “that there remains
substantial risk for unmeasured biases to be introduced through machine-learning
in AI.” 





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 * 07-11-22


MEET THE STARTUPS USING AI TO HELP DOCTORS FIGHT BURNOUT


A NEW WAVE OF HEALTHCARE STARTUPS IS USING ARTIFICIAL INTELLIGENCE TO REDUCE THE
LOAD ON MEDICAL PROFESSIONALS.

[Photo: Clay Banks/Unsplash]
 * 
 * 
 * 
 * 

By Adam Bluestein 6 minute Read

Even before COVID-19, 40% of physicians said they felt burned out. But the
pandemic was a tipping point. Working in jerry-rigged PPE in overcrowded,
understaffed ICUs, more than 3,600 U.S. healthcare workers died in the first
year of the pandemic alone. After bearing witness to the lonely deaths of some 1
million patients, holding the phone as they shared their final minutes with
family members via FaceTime, more doctors are deciding to retire early,
exacerbating a looming shortage. A report last year by the Association of
American Medical Colleges predicts a shortage of up to 124,000 physicians by
2034. That includes a gap of as many as 48,000 primary care physicians, who
report higher levels of burnout than other specialties. And it’s not just
doctors: In a January 2022 survey by Prosper Insights & Analytics, just 50% of
all healthcare workers said they were “happy” at work.

advertisement

advertisement



Happiness won’t be bought overnight. Staffing gaps will take time to fill. But
in the meantime, proponents say, artificial intelligence (AI) could be used to
help ease the burden on maxed-out MDs. “We need to turn every physician into a
super-physician,” says Farzad Soleimani, an assistant professor in emergency
medicine at Baylor College of Medicine and a partner at 1984 Ventures, a San
Francisco-based VC firm. “At the end of the day, what clinicians do is to learn
to recognize patterns. That’s the power of AI.” 

Of course, there are doubters. An April 2019 Medscape survey of 1,500 doctors
across Europe, Latin America, and the U.S. found that a majority were anxious or
uncomfortable with AI, with U.S. physicians expressing the most skepticism
(49%). Relying on algorithms for patient care also presents ethical, clinical,
and legal concerns. AI may bring considerable threats of privacy problems,
ethical concerns, and medical errors. Developers may unknowingly introduce
biases to AI algorithms or train them using flawed or incomplete datasets. Data
used to train AI systems could be vulnerable to hacking. By turning over aspects
of decision-making to machines, physicians could lose their traditional autonomy
and authority—and notions of liability will be tested should AI-guided
recommendations result in patient harm. 

Nevertheless, healthcare AI companies—including nearly 500 early-stage
startups—raised a record $12 billion in funding last year, according to CB
Insights. Here are just a few ways that tech companies are using deep-learning
algorithms and natural language processing to automate routine tasks in
hospitals, cut down hours medical providers spend on paperwork, and reduce
mistakes caused by fatigue.

advertisement



SPEEDING UP PRE-VISIT EVALUATIONS

Managing patients and preventing provider burnout starts before care recipients
even show up to the office or hospital. San Francisco-based Health Note
streamlines patient intake with a text-based AI chatbot that collects patient
information pre-visit and automatically writes up notes for their doctor,
reducing intake and documentation time by up to 90%, according to the company.
Decoded Health—a spinoff of SRI International, the nonprofit research
organization that developed the tech behind the computer mouse, ultrasound, and
Siri—offers what it calls a “virtual medical resident” that prescreens patients
using natural language processing, creating a summary of their medical
complaints with actionable care recommendations. Keona Health focuses on helping
nurses and non-medical staff conduct triage over the phone, guiding them through
symptom checking, offering care recommendations, and automating appointment
scheduling.  


HELPING WITH TRIAGE

When the ER gets slammed, AI triage tools are designed to help flag patients who
need critical care and might otherwise be missed, flagging the most serious
cases and prioritizing them for care. The first major clinical application of AI
triage tools has been in radiology; companies including RapidAI, Viz.ai, and
Arterys all have FDA approval for algorithms that detect signs of strokes, brain
bleeds, and pulmonary embolisms from CT scans. Imagen‘s FDA-approved OsteoDetect
analyzes wrist X-rays to detect distal radius fractures, one of the most common
injuries to the joint. Mednition‘s real-time triage-guidance tool, KATE,
analyzes EHR data and patient vitals collected at intake to help emergency
nurses spot warning signs of sepsis, which accounts for more than 50% of
hospital deaths. It is being used throughout the Adventist Health system and
others to head off ER admissions through earlier treatment. ERs run by Johns
Hopkins University are using Stocastic‘s TriageGO, which analyzes vital signs
and other intake data, along with patient demographics and medical history to
make rapid care recommendations, reducing “door to decision” time by up to 30
minutes. 


TRANSCRIBING DOCTORS’ NOTES

A recent study found that physicians spend an average of about 16 minutes on
electronic health records for each patient visit. DeepScribe is a voice-based
digital assistant that allows a doctor to have a normal conversation with their
patient, transcribing it, pulling out key information, and automatically fitting
it into the proper sections of the medical records. In January 2021, the San
Francisco-based startup raised $30 million. Competitors include Nuance, Suki,
and Corti. 

advertisement


There’s also Rad AI’s Omni software, a virtual assistant designed specifically
for radiologists that helps write a formal “clinical impression” based on
dictated notes, automatically inserting guideline recommendations and spotting
potential errors.


MANAGING THE BILLING PROCESS

“When folks talk about staffing shortages in healthcare they often think of
nurses, doctors, and frontline care staff, but the issue is organization-wide,”
says Ben Beadle-Ryby, co-founder of South San Francisco-based Akasa, a provider
of AI services for healthcare operations. According to recent surveys by the
Healthcare Financial Management Association, more than 57% of health systems and
hospitals have 100-plus open back-office roles—in billing, registration, and
scheduling—to fill. A survey by Change Healthcare found that 65% of healthcare
leaders are already applying AI in their “revenue cycle management,” and by
2023, 98% anticipate doing so.

Akasa provides services for more than 475 hospitals and health systems and
8,000-plus outpatient facilities in all 50 states, using a constantly learning
AI system to help them automate insurance claims status checking, prior
authorization, eligibility, and denials management. Privia Health provides
scheduling and billing tools for some 3,300 independent physicians, using
robotic processing automation—in which an intelligent system learns a scripted
process for handling repetitive billing tasks like a human would.

advertisement



AIDING WITH TESTING

Lab tests shape roughly two-thirds of decisions made by physicians. Before
COVID-19, medical lab professionals performed some 13 billion lab tests a year.
In a February 2020 survey by the American Society for Clinical Pathology, more
than 85% of medical lab workers reported burnout; 36.5% complained of inadequate
staffing. That was before the additional burden of conducting well over 900
million COVID-19 tests since the pandemic began. Many hospital labs are running
with 10% to 35% staff vacancies.

Automating repetitive work could let fewer people do more, and perhaps improve
outcomes, too. In a 13-month pilot, the University of Texas Medical Branch
hospital in Galveston used Biocogniv‘s “laboratory intelligence platform” to
help process more than 325,000 COVID-19 tests and make personalized
interpretations based on PCR and antibody testing, patient vitals, and medical
history. The result: a near doubling in efficiency, lower rates of escalation to
intensive care, and reduced mortality rates. “COVID-19 was a time of immense
change both clinically and operationally,” says Peter McCaffrey, MD, an
assistant professor of pathology at the hospital and director of its pathology
informatics and laboratory information systems. “With Biocogniv’s platform, we
were able to scale interpretation and guidance for COVID and coordinate everyone
during this time of unprecedented uncertainty.” In the company’s pipeline:
laboratory-based prediction tools for sepsis, respiratory failure, and acute
heart failure.

Whether AI proves itself in each of these areas or not, there is no turning
back. “By minimizing or offloading repetitive diagnostic tasks, [AI can help]
physicians devote more time to sophisticated clinical reasoning and judgment,
and inherently human work such as engaging with multidisciplinary care teams to
support patient care,” says Mark Schuster, MD, a pediatrician and founding dean
and CEO of the Kaiser Permanente Bernard J. Tyson School of Medicine in
Pasadena, Calif. In addition to addressing physician specialty shortages in
areas like radiology, where AI has proven highly accurate, Schuster anticipates
that clinical care algorithms will become more powerful, with a “a gradual
increase in precision and personalization of diagnosis and treatment.” Still, he
acknowledges the potential danger that AI could reinforce biases that already
exist in the healthcare system. “We recognize,” he says, “that there remains
substantial risk for unmeasured biases to be introduced through machine-learning
in AI.” 


advertisement

advertisement

advertisement

advertisement

advertisement



#FCFestival returns to NYC this September! Get your tickets today!




VIDEO

Duolingo teaches ‘Game of Thrones’ fans High Valyrian
Leading up to the August premiere of HBO’s new ‘Game of Thrones’ spin-off,
‘House of the Dragon,’ the language-learning app is offering fans lessons in
High Valyrian—and it’s pretty awesome
More Videos


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SATISFACTION WITH TWITTER SURGES AS OTHER SOCIAL MEDIA SITES STUMBLE

Tech


SONOS CEO ON THE LESSONS FROM THE RISE AND FALL OF BLACKBERRY

Tech


STREAMING VIDEO SITES ARE OPENING A WHOLE NEW WORLD OF VIRTUAL PRODUCT PLACEMENT


NEWS

News


WHY IS THERE A PILOT SHORTAGE? 6 FACTORS THAT ARE CONTRIBUTING TO THE AIRLINE
INDUSTRY CRISIS

News


WHY ARE AMAZON, TARGET, AND COSTCO STOCK PRICES DOWN? BLAME WALMART (AND
INFLATION)

News


‘FANTASTICAL CLAIMS’ AND STEPHEN KING: PENGUIN RANDOM HOUSE AND DOJ FACE OFF FOR
ANTITRUST BATTLE


CO.DESIGN

Co.Design


UNTANGLING THE CORPORATE LIES ABOUT PLASTIC

Co.Design


443 FEET AND FALLING: WHY SKYSCRAPERS ARE ADDING SLIDES, STAIRS, DECKS, AND FREE
FALLS

Co.Design


WHY A 1989 POSTER IS STILL A FIXTURE IN THE BATTLE OVER ABORTION RIGHTS


WORK LIFE

Work Life


4 THINGS YOU NEED TO DO BEFORE YOUR SUMMER INTERNSHIP ENDS

Work Life


3 STEPS NAVY SEALS USE TO MAKE DECISIONS

Work Life


IF YOU WANT THE JOB, TAILOR YOUR RÉSUMÉ LIKE THIS

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