#hasummit14 session #23 there’s a 90% probability that your son is pregnant: predicting the future...

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#HASummit14 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

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Page 1: #HASummit14 Session #23 There’s A 90% Probability That Your Son Is Pregnant: Predicting The Future Of Predictive Analytics In Healthcare Dale Sanders SVP

#HASummit14

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

Dale Sanders SVP Strategy Health Catalyst

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#HASummit14

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

Poll Question #1

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#HASummit14

Acknowledgements

Dr. Eric Siegel, Columbia University

Ron Gault, Aersospace Corporation

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Key Themes Today1. Action Matters: Predictive analytics (PA) without actions and interventions are useless

2. Human Unpredictability: Humans behavior, like the weather, is inherently difficult to

predict with a computer

3. Socio-Economics: Most of healthcare’s highest risk root causes lie outside the care

delivery system’s ability to intervene

4. 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

5. Wisdom of Crowds: In the pursuit of objectivity of analytics, don’t forget the wisdom of

subjective experts sitting right next to you

6. 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

Man + Machine

Subjective Objective

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Financial Industry Got It, Long Ago“Information about the transactions of money has 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.”

- Friedrich Nietzsche

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Challenge of Predicting Anything Human

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Sampling Rate vs. PredictabilityThe sampling rate and volume of data in an experiment is directly proportional to the predictability of the next experiment

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Thank you for the graphs, PreSonus

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

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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*

Criteria Description

Diversity of opinion

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

Independence 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

Decentralization 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

Aggregation There are methods and techniques for gathering and aggregating the collective intelligence of the crowd

The Criteria For Designing A “Good” Crowd

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*--James Surowieki

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ccPoll Question #2: Guess The Weight Of The Steer

Levi Wallace, GuessorDave Fenn, Owner

Charlie Brown, 8-yr old Swiss steer; the Guessee

2014 Southwest Washington State Fair

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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

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5,596

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Predictive Analytics Outside Healthcare

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

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Reduce variability in decision making & improve outcomes

Launch prematurely?

Launch too late?

Nuclear Operations

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How And Where Can A Computer Help?

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Desired Political-Military Outcomes

1. 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

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Odd Parallels

“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?

Healthcare Delivery and Nuclear Delivery

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• Subjective• Objective• Assessment• Plan

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NSA, Terrorists and PatientsThe Odd Parallels of Terrorist Registries and Patient Registries

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Predicting Terrorist RiskRisk = 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

• 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

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

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“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

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Thank you Sonja Star, New York Times

“Evidence Based” Sentencing

20 States use predictive analytics risk assessments to inform criminal sentencing

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

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15 different scales feed the PA algorithm

Criminal History

Education/Employment

Family/Marital

Leisure/Recreation

Companions

Alcohol/Drug Problems

Antisocial Patterns

Pro-criminal Attitude Orientation

Barriers to Release

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

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“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 attributesThank you, Thod Nugyen, eHarmony CTO

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Twenty-Nine Dimensions of Compatibility

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

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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

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Predictive Analytics Inside Healthcare

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True Population Predictive Risk Management

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

Very Little ACO Influence

Very Little ACO Influence

>/=30% Waste*100% ACO Influence

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

True Population Health Management

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Socioeconomic Data MattersNot 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

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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 DriversNewborn 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

Thank you, Swati Abbott

Which evidence-based Intervention? How much will it cost? How much will it reduce risk?

Weighted Predictive

Model

Risk of Readmission

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Most Common Causes for Readmission

Robert Wood Johnson Foundation, Feb 2013

1. Patients have no family or other caregiver at home

2. Patients did not receive accurate discharge instructions, including medications

3. Patients did not understand discharge instructions

4. Patients discharged too soon

5. Patients referred to outpatient physicians and clinics not affiliated with the hospital

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What Else Are We Trying to Predict?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

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Closing Thoughts & Questions

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1. 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?

2. Human Unpredictability: The mathematical models of human behavior are

relatively immature.

3. Socio-Economics: Can today’s healthcare ecosystem expand to make a

difference?

4. Missing Data: Without patient outcomes, the PA models are open loop.

5. Wisdom of Crowds: Suggestive analytics from “wise crowds” might be easier and

more reliable than predictive analytics, until our data content improves

6. 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

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Sometimes, the predictions are wrong

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

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Analytic Insights

AQuestions &

Answers

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Session Feedback Survey1. On a scale of 1-5, how satisfied were you overall with this session?

1) Not at all satisfied2) Somewhat satisfied3) Moderately satisfied4) Very satisfied5) Extremely satisfied

3. 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) No interest2) Some interest3) Moderate interest4) Very interested5) Extremely interested

2. What feedback or suggestions do you have?