ai in healthcare: current applications and future directionsai in healthcare: current applications...

AI in Healthcare: Current Applications and Future

Directions

Brent I. Fox, PharmD, PhD Associate Professor

Harrison School of Pharmacy Auburn University

[email protected]

American Society for Automation in Pharmacy 2019 Annual Conference January 23–25 • Kiawah Island, S.C. • www.asapnet.org • #ASAPAnnual

Objectives

1.  Describe the technical underpinnings of artificial intelligence. 2.  Compare and contrast current artificial intelligence initiatives

in health care. 3.  Describe expected benefits of artificial intelligence for patients

and providers.

2


Cognitive Computing vs. Artificial Intelligence

• Synonyms? • Data sources • Methods • Who is in charge….

4


https://sequre.world/wp-content/uploads/2018/11/AI-confusion.jpg

Question Answering Machines

Goal: Understand semantic and contextual variations in sentences to identify a single best answer •  Natural language processing •  Curated knowledge base •  Ingestion •  Training •  Hypothesis and conclusion

http://www.datasciencecentral.com/profiles/blogs/what-exactly-is-watson

5 https://sequre.world/wp-content/uploads/2018/11/AI-confusion.jpg


AI Encounters?

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Lonely Cat or Dog?

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Brain Computer Interfaces

• No keyboard, mouse, monitor

• Trauma • Neurological disease • AI -> decode neural

activities associated with specific actions (e.g., movement, speech, etc.)

10


Advanced Imaging Analytics • Goal -> develop imaging techniques that eliminate the need for tissue

samples

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• Radiomics, per Nature •  “High-throughput mining of quantitative

image features from standard of care medical imaging that enables data to be extracted and applied within clinical decision support systems to improve diagnostic, prognostic, and predictive accuracy”

• AI challenges •  Large scale data sharing to validate

algorithms to characterize phenotypes and genetic properties of cancer tissue

Wearables and Patient-reported Data

12 American Society for Automation in Pharmacy 2019 Annual Conference January 23–25 • Kiawah Island, S.C. • www.asapnet.org • #ASAPAnnual

AI in a Community Hospital •  Flagler Hospital •  Patients treated similarly are grouped; groups

analyzed for differences with other groups •  Goldilocks groups selected •  AI defines care path for Goldilocks group, from

admission to DC – including timing •  Order sets are adjusted to align with care path


Pneumonia $1356 direct savings per patient Decrease LOS by 2 days Readmissions: 2.9% -> 0.4%

AI for Payers

•  Manage customer interactions •  Manage membership and billing •  Automate claims processing •  Manage network and providers •  Engage members in health

management •  Automate reporting and

regulatory updates


Identifying Disease •  Northern Pathology Imaging Cooperative (UK)

–  9 NHS hospitals –  10 medical tech companies –  7 universities

•  Digital pathology images from northern NHS hospitals –  760,000 images/year (~1.2 petabytes) –  For training AI systems

•  Develop integrated methods to collaborate across regional clinical pathology services –  Collaboration between clinicians, researchers, and industry to develop new AI tools


Predicting Disease: Alzheimer’s

Treatment vs. Prevention Early identification methods AI algorithm – diagnostic data AI algorithm – PET scans Both suggest prediction


Systems-based Risk Prediction Tools

• Goal -> Predictive identification of an outcome

• Relies on un/anticipated associations in data

• AI -> algorithm development • Challenges

•  Data quality, structures, completeness

•  Predicting an outcome vs. an ICD-10 code

•  Biases in algorithms

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Systems-based Risk Prediction Tools

•  Experts vs. AI •  52 patients •  3 panelists •  Panelists’ review identified

– 0 Brand and 0 Generic cases

– Event Probability and Drug Causality > Probable (67)

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60 patients identified for review (30 B/30 G)

8 underwent single review (4 B, 4 G; Table

1)

52 underwent dual review (26 B,

26 G)

Did Not Undergo Consensus

34 patients (14 B, 20G; Table 2)

Underwent Consensus

18 patients (12 B, 6 G; Table 3)

Event Probability of Probable or Certain

(Q2 >67)

3 B (75%), 3 G (75%)


(Q2 > 67)

10 B (71%), 13 G (65%)


(Q2 > 67)

9 B (75%), 5 G (83%)

Drug Causality of Probable or Certain

(Q3 > 67)

0 B, 0 G


(Q3 > 67)

0 B, 0 G


(Q3 > 67)

0 B, 0 G


AND Drug Causality of Probable or

Certain

0 B, 0 G


Preventable Readmissions •  Grady Health & Jvion's Cognitive Clinical

Success Machine •  Complete DC list (inpatient and ambulatory) •  All patient data •  Socioeconomic information (transportation

issues, predicted medicine adherence issues)

•  AI identified those at risk for readmission •  EMS visited and addressed issues •  Measure prevention?


FDA-approved AI Algorithms 1.  Viz.ai Contact – CDS for CT scans to identify potential

strokes 2.  Idx-DR – autonomous detection of diabetic retinopathy 3.  MaxQ AI Accipio – workflow to prioritize patients with

potential intracranial hemorrhage 4.  Imagen OsteoDetect – analyzes x-rays for distal radius

fracture 5.  DreaMed Advisor Pro – CDS for insulin dosing 6.  AliveCor AF algorithm – smartphone ECG for a fib


FDA-approved AI Algorithms, cont.

7.  Apple – ECG smartwatch to detect irregular heart rate 8.  Bay Lab EchoMD AutoEF – review and selection of

echocardiographs to calculate ejection fraction 9.  Zebra Medical Vision Coronary Calcium Scoring –

coronary artery calcification score based on ECG 10. Arterys Oncology – analyze MRIs and CT scans for

signs of liver and lung cancer


Nuance AI Marketplace •  70% of US radiologists

–  PowerScribe One – voice-enabled reporting software that incorporates AI for decision support and diagnostics

–  PowerShare – image exchange network

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•  Marketplace –  An “app store for algorithms” –  40+ developers –  Working with American College of Radiology to develop API standards for

workflow, EHR, and PAC integration American Society for Automation in Pharmacy 2019 Annual Conference January 23–25 • Kiawah Island, S.C. • www.asapnet.org • #ASAPAnnual

AI Code of Ethics Who defines AI ethics?

Who enforces them?

23 https://www.technologyreview.com/s/612318/establishing-an-ai-code-of-ethics-will-be-harder-than-people-think/


AI Code of Ethics • NYPD database of 17,500 • Presumes gang activity

• Facial recognition adopted globally as a tool to find known criminals

•  Protect public safety



AI Code of Ethics – MIT Moral Machine

26 http://moralmachine.mit.edu/


AI Code of Ethics

• Risk assessment in judicial sentences

Health care •  Bias in the data used to build models (intentional and unintentional) • Unethical design • Differing goals of AI builders and AI users • Over reliance on AI – AI becomes part of the care team •  Is it ethical to withhold a patient’s information from AI system?

27 https://datasociety.net/output/governing-artificial-intelligence/ https://www.nejm.org/doi/full/10.1056/NEJMp1714229


#1 Obstacle to Implementing AI?


AI Usage and Trust


Technology industry should •  Address the “black box” perception of AI •  Start with clinicians who are ready for change •  Start where fatal error is less likely •  Highlight benefits for all involved •  Get engaged in the regulatory process

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• Diagnosis is central • Oncology is important domain • AI becoming accessible to smaller

institutions • Prediction and risk stratification • Much remains unanswered • Challenge – people or technology or both?

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AI in Healthcare: Current Applications and Future

Directions

Brent I. Fox, PharmD, PhD Associate Professor

Harrison School of Pharmacy Auburn University

[email protected]


ai in healthcare: current applications and future directionsai in healthcare: current applications...

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