#HASummit14 Session #23 There’s A 90% Probability That Your Son Is Pregnant:...

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Transcript of #HASummit14 Session #23 There’s A 90% Probability That Your Son Is Pregnant:...

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  • #HASummit14 Session #23 Theres 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 2
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  • #HASummit14 Acknowledgements Dr. Eric Siegel, Columbia University Ron Gault, Aersospace Corporation 3
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  • #HASummit14 Key Themes Today 1. 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 healthcares highest risk root causes lie outside the care delivery systems 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, dont 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 peoples future? 5
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  • #HASummit14 Common Concepts & Provocative Thoughts 6
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  • #HASummit14 Man vs. Machine 7 Man + Machine Subjective Objective
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  • #HASummit14 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? 8
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  • #HASummit14 The Basic Process of Predictive Analytics 9
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  • #HASummit14 Beyond math, there are no facts; only interpretations. - Friedrich Nietzsche 10
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  • Challenge of Predicting Anything Human 11
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  • #HASummit14 Sampling Rate vs. Predictability The sampling rate and volume of data in an experiment is directly proportional to the predictability of the next experiment 12
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  • #HASummit14 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 13
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  • #HASummit14 We are asking physicians and nurses to act as our digital samplers and thats not going to work 14
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  • #HASummit14 The Human Data Ecosystem 15
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  • #HASummit14 We Are Not Big Data in Healthcare, Yet 16
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  • #HASummit14 Predictive Precision vs. Data Content 17
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  • #HASummit14 The Wisdom of Crowds & Suggestive Analytics 18
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  • #HASummit14 The Wisdom of Crowds* CriteriaDescription Diversity of opinion Individual members of the group possess personal insights or facts on a topic, even if its simply an unusual interpretation of data and facts on that topic IndependenceIndividual members of the group form their own opinions and are not prone to the overt and predictable influence from other members of the group DecentralizationKnowledge 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 AggregationThere are methods and techniques for gathering and aggregating the collective intelligence of the crowd The Criteria For Designing A Good Crowd 19 *--James Surowieki
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  • #HASummit14 c c Poll Question #2: Guess The Weight Of The Steer Levi Wallace, Guessor Dave Fenn, Owner Charlie Brown, 8-yr old Swiss steer; the Guessee 2014 Southwest Washington State Fair 20
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  • #HASummit14 2,767 pounds! 21
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  • #HASummit14 Amazon: Predictive or Suggestive? 22
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  • #HASummit14 Poll Question #3 How many physicians were working in Utah in 2010? 2012 Physician Workforce Report from the Utah Medical Education Council 23
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  • #HASummit14 5,596 24
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  • #HASummit14 Predictive Analytics Outside Healthcare 25
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  • #HASummit14 US Strategic Command, underground command center prior to 9/11 26
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  • #HASummit14
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  • Reduce variability in decision making & improve outcomes Launch prematurely? Launch too late? Nuclear Operations 28 How And Where Can A Computer Help?
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  • #HASummit14 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 29
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  • #HASummit14 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? 30 Healthcare Delivery and Nuclear Delivery
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  • 31 Subjective Objective Assessment Plan
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  • #HASummit14 NSA, Terrorists and Patients The Odd Parallels of Terrorist Registries and Patient Registries 32
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  • #HASummit14 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? 33
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  • #HASummit14 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? 34
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  • #HASummit14 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
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  • #HASummit14 Thank you Sonja Star, New York Times Evidence Based Sentencing 20 States use predictive analytics risk assessments to inform criminal sentencing 36
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  • Recidivism Risk Assessment: Level of Service/Case Management Inventory (LS/CMI)* 37 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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  • #HASummit14 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. 38
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  • #HASummit14 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
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  • #HASummit14 Twenty-Nine Dimensions of Compatibility Thank you, Ryan Barker, Principal Software Engineering Matchi