AI: Artificial or Augmented Intelligence - SAS: Analytics, Artificial … · 2017-11-28 · should...

© Kaiser Permanente 2016, Internal Use Only 1© Kaiser Permanente 2016, Internal Use Only 1

AI: Artificial or Augmented Intelligence

Jason Jones, PhDKaiser Permanente

© Kaiser Permanente 2016, Internal Use Only 2

I have none

No images or other references to products are an endorsement

Disclosures2


Last updated March 2017 page 3

Northwest

552,651

Northern

California

3,992,501

Southern

California

4,264,119

Hawaii

249,687Colorado

663,240

Georgia

284,213

Mid-Atlantic

States

665,402

Membership by region(As of January 2017)

Washington

651,000

Kaiser Permanente Integrated Care and Coverage

>200K Employees

>52K Nurses

>20K Physicians

38 Hospitals

>650 Medical office buildings


AI: “Artificial” or “Augmented” Intelligence

Since at least the 1950’s there has been some debate about whether AI should replace or enhance human judgment. The focus on artificial intelligence has resulted in computing power and algorithms that have commoditized predictive modeling. We will focus on some practical examples in healthcare—aspirations and solvable gaps that remain in achieving the quadruple aim:

• Vision: Computers as a foundation for quality medical care as a right.

• Experience: Algorithms can improve clinician work life.

• Hard parts: Data preparation and workflow.

• Solvable gaps: Tools to respect preference and provide agency.

Overview4


History Why It Matters

“Instruments are at hand which…will give [us] access to and command over the inherited knowledge of the ages.”Editor in Bush (1945)

“The doubling time of medical knowledge in 1950 was 50 years; in 1980, 7 years; and in 2010, 3.5 years. In 2020 it is projected to be 0.2 years.”Densen (2011)

“’Problem solving’ is largely…a matter of appropriate selection…and as we know that power of selection can be amplified.”Ashby (1956)

“Two 1998 reports validate…40% discordance…diagnoses…compares with 35% in 1938, 39% in 1959, 43% in 1974, and 47% in 1983”Lundberg (1998)

“Quality medical care as a right cannot be achieved unless we can establish need, separate the well from the sick and do that without wasting [clinicians’] time.”Garfield (1970)

Some History on “AI” and Computers in Care5

• Bush (1945): https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881/• Densen (2011): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116346/• Ashby (1956): http://pespmc1.vub.ac.be/books/IntroCyb.pdf • Lundberg (1988): http://jamanetwork.com/journals/jama/fullarticle/188042• Garfield (1970): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3076970/








Overview6


• Watson: By Source, Fair use, https://en.wikipedia.org/w/index.php?curid=31142331• Google Car: By Grendelkhan - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=47467048

Recent Successes of AI7


• Level 0 – No Automation: …

• Level 1 – Driver Assistance: …

• Level 2 – Partial Automation: …

• Level 3 – Conditional Automation: The driving mode-specific performance by an

Automated Driving System of all aspects of the dynamic driving task with the expectation that

the human driver will respond appropriately to a request to intervene

• Level 4 – High Automation: The driving mode-specific performance by an Automated

Driving System of all aspects of the dynamic driving task, even if a human driver does

not respond appropriately to a request to intervene

• Level 5 – Full Automation: The full-time performance by an Automated Driving System

of all aspects of the dynamic driving task under all roadway and environmental conditions that

can be managed by a human driver

Levels of AutomationSAE (Society of Automotive Engineers)

8

• https://www.sae.org/news/3544/


Patient Experience

Population Health

Reduced Cost

Provider Work Life

Quadruple Aim in Health/CareBodenheimer (2014)

• http://www.annfammed.org/content/12/6/573.full


As you know I'm a big fan of clinical decision support. It's not just an abstract notion in my opinion but instead translates into real improvement in the daily lives of our physicians and more importantly in the patients we serve.

A simple example recently occurred with CURB 65. I was working a night shift with a Hospitalist consultant with which I don't always have the best working relationship (not necessarily his fault, we just don't seem to be on the same page sometimes). I had a patient with pneumonia that at first glance didn't appear very ill, and given the clinical appearance and the relationship with the consultant my mind started mentally preparing for discharge. Then I applied the tool, and it reminded me that this patient had a CURB score of 2.

The score changed my direction and I decided to call a consult. The impressive thing was that the interaction with the Hospitalist, with whom I was expecting push back, went

very smoothly once I mentioned the CURB score. It occurred to me that the objectivity of this tool not only made me think twice about my disposition but it also gave a common language for the consultant and I to speak to each other, one that overcame the historical communication gap we shared.

I have found that this type of situation repeats itself. Clinical decision support, when used correctly, can make us better physicians and colleagues as well. In this case it not only saved me from making a possible error in judgment, but also helped facilitate what may have been a difficult conversation. Just thought you and your staff would want to know. Thanks for all that you do.

Todd Newton, MD Director Medical Services, SCPMG Regional Chief - Emergency Medicine








Overview11


• https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

Developed Tools12


Developed Tools(Though we still spend a lot of time hunting and gathering)

13


Authoring

Point of Care Decisions (Inside Transactions)

Config FileLimited

Deploy

Full

Deploy

Engine

End User Interaction

Deployment Decisions (Outside Transactions)

Parameter

ExplorationFunction/

Formula

Case Reviews Cases/

Context

Operational

CharacteristicsPopulation/

System

Data Stores

Backend Analytics

Prediction

Modeling

Performance

Improvement

Formal

Evaluation

Tools Oriented to Augmented Intelligence


• The best medicine is useless in the cabinet

• Self-serviceo Leverages critical knowledge and preference

oProvides agency

oMust have the same underlying quality

Examples:

• Reports with embedded rigor

• Preference sensitive predictive parameters

• Decision support deployment

• Forecasts change the discussion

Underdeveloped Tools16


Facilitate Learning—Embed Appropriate Methods17


Building Predictive Models

Viewable from mobile device

Preferences:• Vital signs highest• Early values higher• Labs a little lower• Prior conditions lower still


Clinical Decision Support by and for Clinicians19


Decision Support Tools as Apps


Changing the Discussion21


• http://www3.weforum.org/docs/WEF_GAC15_Deep_Shift_Software_Transform_Society.pdf

Who Is (Willing To Be) Augmented?Maybe we should ask: which task and under what circumstances?

22


Appendix


Building Predictive Models

0.5

0.6

0.7

0.8

0.9

1.0

CURB-65 + SpO2 eCURB CURB-65 + SpO2 eCURB KP Model

Pe

rfo

rman

ce

"EBM" Applied to KP

Improve Data Quality

"EBM" as Published

Improve ProcessRedefine "EBM"Redefine "Risk"

Interdependence


SIRS ERT(Earliest Recognition Time)The first time 2 or more SIRS criteria are met within 2 hours of each other


SIRS ERT-A Consolidated ViewHow do I know when SIRS ERT was met?

If you hover over the SIRS ERT icon with your mouse in a Patient List Column in your Patient List or on a TrackBoard, you will see the time SIRS ERT was met as a pop-up. This is shown below:

How do I know when SIRS findings are "positive" for my patient?

If you double click on the SIRS ERT icon shown in the Patient List or on a TrackBoard, it will take you to the Clinical Risk Scores Report. SIRS related vital signs and lab results are displayed here. Values that meet SIRS criteria for the specified parameters are highlighted in red.


Severe Sepsis Risk Probability Model

• Leaves parameters continuous

• Considers them together

• Allows finer grained control over alerting

Understanding How A Model Works


Severe Sepsis Risk High at TriageSIRS ERT delayed (CBC slow)


- Span clinical quality and cost

- Span encounter level interaction with population management

30

Operating Characteristics


Supporting Purposeful Deviation

31

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Visualization for Clinicians & Patients

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