Dynamic Talks Portland: The use of AI in many industries has revolutionized operations and efficiency. In healthcare, the progress is just beginning. Despite the promise of AI, why has the development lagged other industries? What issues are unique to healthcare that create challenges for common approaches? How can data scientists overcome these challenges and deliver on the promise of using data to reach multiple goals of improved quality, decreased cost, and greater patient satisfaction?

1. Dynamic Talks
Bumped HQ
12/4/2019
Peter Graven, PhD
Challenges for AI in Healthcare

2. Background
 Lead Data Scientist at OHSU
 Assistant Professor (Affiliate) at OHSU-PSU
School of Public Health
 PhD in Health Economics from U of MN
 Previous Experience
 Academic research (health policy, methodology,
program evaluation)
 Economic consulting Market research
 Any views expressed are not (necessarily)
the views of OHSU
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3. Why is AI in Healthcare Different?
 Artificial intelligence is changing world in many
sectors
 Predictive typing, internet searching
 Speech recognition
 Visual perception
 Marketing
 Marketing examples
 Product recommendations
 Image recognition (for searches)
 Sentiment analysis (social media)
 Demand based pricing
 Identify customers that might leave
 Chatbots
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4. AI in Healthcare Current Status
 Robotic surgery (simple routine steps)
 Image analysis (x-rays, retina scans)
 Genetic analysis (review large amount of data)
 Pathology (analyze biopsy, not approved)
 Clinical-decision (sepsis, deterioration, risk of
ED/hospital admit, no-show visits,
 Virtual nursing (collect basic info for visit)
 Administration (billing and claims)
 Mental health (use mobile phone for monitoring
depression)
Source: Strickland, E. “How IBM Watson Overpromised and
Underdelivered on AI Health Care”, IEEE Spectrum, Apr 2, 2019.
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5. Basic Challenges in Healthcare
 Decisions need to be right at very high level of
accuracy
 Risk of lawsuits (though none currently known)
 Clinicians are ultimately responsible for
decisions
 Not outsourced to algorithms
 Clinician understanding of algorithms are
mostly in infancy (in terms broad-based
adoption)
 Input factors must be transparent
 Otherwise, predictive risk cannot be acted upon
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6. A Story about Predictive Modeling
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Let’s predict
risk!
Let’s predict
risk of high
costs!
Let’s predict
risk of hospital
admissions!
Let’s predict
who needs
Care
Management
Let’s predict
who will
respond to
Care
Management
How do we
predict who
will respond to
Care
Management?
BASIC SCIENCE

7. Implications for AI in Healthcare
 Flip the script!
 It’s not about the cool modeling
 It’s about finding interventions that work
 Old fashioned approach of trials and experiments and
science
 Then create models to match interventions to
people
 Tailor the model to the intervention
 “There’s a model for that!
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8. Focus on the Decision-making
Peter Graven, PhD 8
(AI)
Artificial Intelligence
(IA)
Intelligent Applications
Black box
Unclear interventions
Minimizes need for humans
Transparent input factors
Oriented around decisions
Tailored to existing workflows

9. Advanced approaches
 If specific interventions exist, build models to feed them
patients. Otherwise,
 Follow workflows and assess places for models to be
inserted
 The workflow is the intervention. Use the model to make it
better
 Embedded improvement process with model simply as new
technology
 Focus on making the decision faster, easier, or more
certain
 Give the user the right information so they feel confident
 Will improve clinician satisfaction
 Organic distribution
 let users get used to the information before workflow is
cemented
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10. More Implications
 As models are deployed within delivery
system, the upkeep and maintenance issues
grow
 Cost of a good model embedded is not
trivial.
 Model itself is just one line of code but easy to
underestimate cost of
 organizing data to estimate model,
 Making model appear in proper location
 Training individuals in what it means
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11. Some realities
 Electronic Medical Record (EMR) systems are
not easy to integrate with
 FHIR and other interoperability tools may help but
will not likely provide the seamless experience
 Very little incentive for EMR companies to really
make integration smooth
 Cloud based options are growing for more
complex (real-time) modeling without being an
on premise solution
 Many lawsuits about improper sharing of data
 Difficult to arrange data for algorithms
 1000’s of tables that are linked but not designed for
analytic purposes
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12. Discussion
 Peter Graven, PhD
graven@ohsu.edu
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