Session #23 There’s A 90% Probability That Your Son Is Pregnant: Predicting The Future Of Predictive Analytics In Healthcare

Dale Sanders SVP Strategy Health Catalyst

Poll Question #1

To what degree is your organization using predictive analytics to improve care were reduce cost?

a) We are not using any predictive analytics, that I know about b) We are experimenting with predictive analytics in small use cases, but as yet have seen no improvements in care or cost c) We are using predictive analytics in a small number of use cases and the results have been positive d) We are using predictive analytics in a large number of use cases and the results have been positive e) Unsure or not applicable 2

Acknowledgements

Dr. Eric Siegel, Columbia University Ron Gault, Aersospace Corporation 3

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Key Themes Today

Action Matters:

Predictive analytics (PA) without actions and interventions are useless

Human Unpredictability:

Humans behavior, like the weather, is inherently difficult to predict with a computer

Socio-Economics:

Most of healthcare’s highest risk root causes lie outside the care delivery system’s ability to intervene

Missing Data:

We are missing key data in healthcare, particularly clinical outcomes data, required for accurate predictive models… so we need to leverage collective wisdom of experts until we close the data gap

Wisdom of Crowds:

In the pursuit of objectivity of analytics, don’t forget the wisdom of subjective experts sitting right next to you

Social Controversy:

Even with accurate PA, are we socially prepared to act? Do we want to know? Are we intruding on people’s future?

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Common Concepts & Provocative Thoughts

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Man vs. Machine

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Subjective Objective Man + Machine

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Financial Industry Got It, Long Ago

become almost as important as the money itself.”

Walter Wriston, former chairman and CEO of Citicorp, awardee of Presidential Medal of Freedom, 1989 • • • Could you cut and paste “health” for “money”? What if we gave healthcare away at a discount– or for free-- just so we could collect the data for its analytic value?

What if Health Catalyst started a healthcare delivery system so we

could collect and control the ecosystem for the downstream value of the data?

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The Basic Process of Predictive Analytics

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“Beyond math, there are no facts; only interpretations.”

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Friedrich Nietzsche 10

Challenge of Predicting Anything Human

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Sampling Rate vs. Predictability

The sampling rate and volume of data in an experiment is directly proportional to the predictability of the next experiment

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Healthcare and patients are continuous flow, analog process and beings But, if we sample that analog process enough, we can approximately recreate it with digital data

Thank you for the graphs, PreSonus

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We are asking physicians and nurses to act as our “digital samplers”… and that’s not going to work

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The Human Data Ecosystem

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We Are Not “Big Data” in Healthcare, Yet

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Predictive Precision vs. Data Content

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The Wisdom of Crowds & Suggestive Analytics

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The Wisdom of Crowds*

The Criteria For Designing A “Good” Crowd

Criteria

Diversity of opinion Independence Decentralization Aggregation

Description

Individual members of the group possess personal insights or facts on a topic, even if it’s simply an unusual interpretation of data and facts on that topic Individual members of the group form their own opinions and are not prone to the overt and predictable influence from other members of the group Knowledge on a given topic does not reside in central decision making bodies, and important decisions can be made by members of a local, decentralized crowd who most readily feel the consequences of those decisions There are methods and techniques for gathering and aggregating the collective intelligence of the crowd *--James Surowieki 19

Poll Question #2: Guess The Weight Of The Steer

c 2014 Southwest Washington State Fair c Levi Wallace, Guessor Charlie Brown, 8-yr old Swiss steer; the Guessee Dave Fenn, Owner 20

2,767 pounds…!

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Amazon: Predictive or Suggestive?

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Poll Question #3 How many physicians were working in Utah in 2010?

2012 Physician Workforce Report from the Utah Medical Education Council 23

5,596

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Predictive Analytics Outside Healthcare

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US Strategic Command, underground command center… prior to 9/11 26

Nuclear Operations

How And Where Can A Computer Help?

Reduce variability in decision making & improve outcomes Launch prematurely?

Launch too late?

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Desired Political-Military Outcomes

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Retain U.S. society as described in the Constitution 2.

Retain the ability to govern & command U.S. forces 3.

Minimize loss of U.S. lives 4.

Minimize destruction of U.S. infrastructure 5.

Achieve all of this as quickly as possible with minimal expenditure of U.S. military resources 29

Odd Parallels

Healthcare Delivery and Nuclear Delivery “Clinical” observations • • Satellites and radar indicate an enemy launch Predictive “diagnosis” Are we under attack or not?

Decision making timeframe • < 4 minutes to first impact when enemy subs • launch from the east coast of the US “Treatment” & intervention Launch on warning or not?

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• • • •

S

ubjective

O

bjective

A

ssessment

P

lan 31

NSA, Terrorists and Patients

The Odd Parallels of Terrorist Registries and Patient Registries 32

Predicting Terrorist Risk

Risk

=

P

(

A

) ×

P

(

S

|

A

) ×

C

• Probability of Attack • Probability of Success if Attack occurs • Consequences of Attack (dollars, lives, national psyche, etc.) • What are the costs of intervention and mitigation?

• Do they significantly outweigh the risk?

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• • • • •

Nuclear Weapons Risk Scenarios

What are the “adverse events” we were trying to predict and avoid?

NUCFLASH  Accidental or unauthorized launch that could lead to the outbreak of war Broken Arrow  Accidental or unexpected event, e.g., nuclear detonation or non-nuclear detonation or burning Empty Quiver  Loss, theft, seizure, destruction of nuclear weapon Bent Spear  Damage to a weapon that requires major repair, and has the potential to attract public attention Dull Sword  A nuclear safety deficiency that cannot be resolved by the local unit 34

“Mr. Sanders, while your 9-year tenure as an inmate has been stellar, our analytics models predict that you are 87% likely to become a repeat offender if you are granted parole. Therefore, your parole is denied.”

2014, 80% of parole boards now use predictive analytics for case management* *- The Economist, “Big data can help states decide whom to release from prison”, Apr 19th 2014 35

“Evidence Based” Sentencing

20 States use predictive analytics risk assessments to inform criminal sentencing

Thank you Sonja Star, New York Times 36

Recidivism Risk Assessment: Level of Service/Case Management Inventory (LS/CMI)*

15 different scales feed the PA algorithm Criminal History Barriers to Release Education/Employment Family/Marital Leisure/Recreation Companions Alcohol/Drug Problems Antisocial Patterns Pro-criminal Attitude Orientation Case Management Plan Progress Record Discharge Summary Specific Risk/Needs Factors Prison Experience - Institutional Factors Special Responsivity Consideration 42.2% of high risk offenders recidivate within 3 years. *--Nov 2012, Hennepin County, MN, Department of Community Corrections and Rehabilitation 37

“Since the publishing of Lewis' book, there has been an explosion in the use of data analytics to identify patterns of human behavior and experience and bring new insights to fields of nearly every kind .”

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eHarmony Predictions

• • • • • • “Heart”  of the system: Compatibility Match Processor (CMP) 320 profiling questions/attributes per user 29 dimensions of compatibility ~75TB 20M users 3B potential matches daily 60M+ queries per day, 250 attributes Thank you, Thod Nugyen, eHarmony CTO 39

Twenty-Nine Dimensions of Compatibility

Thank you, Ryan Barker, Principal Software Engineering – Matching, eHarmony 40

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The Good Judgment Project

• • Funded by Director of National Intelligence, brainchild of Philip Tetlock Can groups of non-experts with access only to open source information, predict world events more effectively than intelligence analysts with access to classified information? What about “internationally recognized” experts?

• Since 2011: 5,000 forecasters, 1M forecasts, 250 topics  “…from Eurozone exits to Syrian civil war” • Non-expert forecasters are 65% better than the experts, 30-60% better than predictive algorithms 42

Predictive Analytics Inside Healthcare

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True Population True Population Health Management

Thank you, for the diagram, Robert Wood Johnson Foundation, 2014

*Congressional Budget Office, IOM, “Best Care at Lower Cost”, 2013

Very Little ACO Influence >/=30% Waste* 100% ACO Influence Very Little ACO Influence

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Socioeconomic Data Matters

Not all patients can functionally participate in a protocol At Northwestern (2007-2009), we found that 30% of patients fell into one or more of these categories: • Cognitive inability • Economic inability • Physical inability • Geographic inability • Religious beliefs • Contraindications to the protocol • Voluntarily non-compliant 45

The key to predictive analytics in the future of healthcare will be the ability to answer this two part question: What’s the probability of influencing this patient’s behavior towards our desired outcome

and

how much effort (cost) will be required for that influence?

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Example Variables: Readmission Drivers

Newborn delivery Multiple prior admissions High creatinine High ammonia High HBA1C Low Oxygen Sats Age Admitting physician is pulmonologist or infectious diseases Prior admission for CHF traumatic stupor & coma Prior nutritional disorders Diabetic drugs Weighted Predictive Model Thank you, Swati Abbott Risk of Readmission Which evidence based Intervention? How much will it cost? How much will it reduce risk?

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Most Common Causes for Readmission

Robert Wood Johnson Foundation, Feb 2013

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Patients have no family or other caregiver at home 2.

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Patients did not receive accurate discharge instructions, including medications Patients did not understand discharge instructions 4.

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Patients discharged too soon Patients referred to outpatient physicians and clinics not affiliated with the hospital 48

What Else Are We Trying to Predict?

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Common applications being marketed today

Identifying preventable re-admissions: COPD, MI/CHF, Pneumonia, et al Sepsis Risk management of decubitus ulcers LOS predictions in hospital and ICU Cost-per-patient per inpatient stay Likelihood of inpatient mortality Likelihood of ICU admission Appropriateness of C-section Emerging: Genomic phenotyping 49

Closing Thoughts & Questions

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Action Matters:

What is the return in investment for intervention? Are we prepared to invest more... or say “no”… to patients who score low on predicted engagement?

Human Unpredictability:

The mathematical models of human behavior are relatively immature.

Socio-Economics:

Can today’s healthcare ecosystem expand to make a difference?

Missing Data:

Without patient outcomes, the PA models are open loop.

Wisdom of Crowds:

Suggestive analytics from “wise crowds” might be easier and more reliable than predictive analytics, until our data content improves

Social Controversy:

How much do we want to know about the future of our health, especially when the predictive models are uncertain?

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Financial Industry Got It, Long Ago

“Falling sick is not just an individual’s problem. Nations crumble when their people are not strong. History is full of events riddled with diseases that brought societies to their knees.”

Kofi Annan, former Secretary-General of the United Nations 51

Sometimes, the predictions are wrong

 Arthur Henning, the Nate Silver of the 1930s-1950s, missed this one… 52

Analytic Insights Questions &

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Session Feedback Survey

On a scale of 1-5, how satisfied were you overall with this session?

1) 2) 3) 4) 5) Not at all satisfied Somewhat satisfied Moderately satisfied Very satisfied Extremely satisfied What feedback or suggestions do you have?

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On a scale of 1-5, what level of interest would you have for additional, continued learning on this topic (articles, webinars, collaboration, training)?

1) 2) 3) 4) 5) No interest Some interest Moderate interest Very interested Extremely interested 54