Dale Sanders SVP Strategy Health Catalyst

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Session #23 There’s A 90% Probability That Your Son Is Pregnant: Predicting T he 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? - PowerPoint PPT Presentation

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Session #23Theres A 90% Probability That Your Son Is Pregnant: Predicting The Future Of Predictive Analytics In HealthcareDale Sanders SVP Strategy Health Catalyst

#HASummit14To 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 aboutb) We are experimenting with predictive analytics in small use cases, but as yet have seen no improvements in care or costc) We are using predictive analytics in a small number of use cases and the results have been positived) We are using predictive analytics in a large number of use cases and the results have been positivee) Unsure or not applicablePoll Question #12#HASummit14AcknowledgementsDr. Eric Siegel, Columbia University

Ron Gault, Aersospace Corporation3#HASummit14

4#HASummit14Key Themes TodayAction Matters: Predictive analytics (PA) without actions and interventions are uselessHuman Unpredictability: Humans behavior, like the weather, is inherently difficult to predict with a computerSocio-Economics: Most of healthcares highest risk root causes lie outside the care delivery systems ability to interveneMissing 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 gapWisdom of Crowds: In the pursuit of objectivity of analytics, dont forget the wisdom of subjective experts sitting right next to youSocial Controversy: Even with accurate PA, are we socially prepared to act? Do we want to know? Are we intruding on peoples future?

5#HASummit14Common Concepts & Provocative Thoughts6#HASummit14Man vs. Machine 7

Man + MachineSubjectiveObjective#HASummit14Financial Industry Got It, Long AgoInformation 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, 1989Could 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#HASummit14

The Basic Process of Predictive Analytics9#HASummit14Beyond math, there are no facts; only interpretations.

- Friedrich Nietzsche10#HASummit14

Challenge of Predicting Anything Human11#HASummit14Sampling Rate vs. PredictabilityThe sampling rate and volume of data in an experiment is directly proportional to the predictability of the next experiment

12#HASummit14

Thank you for the graphs, PreSonusHealthcare and patients are continuous flow, analog process and beingsBut, if we sample that analog process enough, we can approximately recreate it with digital data13#HASummit14

We are asking physicians and nurses to act as our digital samplers and thats not going to work14#HASummit14The Human Data Ecosystem

15#HASummit14

We Are Not Big Data in Healthcare, Yet16#HASummit14

Predictive Precision vs. Data Content17#HASummit14The Wisdom of Crowds & Suggestive Analytics18#HASummit14The Wisdom of Crowds*CriteriaDescriptionDiversity of opinionIndividual 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 topicIndependenceIndividual members of the group form their own opinions and are not prone to the overt and predictable influence from other members of the groupDecentralizationKnowledge 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 decisionsAggregationThere are methods and techniques for gathering and aggregating the collective intelligence of the crowdThe Criteria For Designing A Good Crowd19*--James Surowieki#HASummit14

Poll Question #2: Guess The Weight Of The SteerLevi Wallace, GuessorDave Fenn, OwnerCharlie Brown, 8-yr old Swiss steer; the Guessee2014 Southwest Washington State Fair20cc#HASummit142,767 pounds!21#HASummit14Amazon: Predictive or Suggestive?22

#HASummit14Poll Question #3

How many physicians were working in Utah in 2010?2012 Physician Workforce Report from the Utah Medical Education Council23#HASummit145,59624#HASummit14Predictive Analytics Outside Healthcare25#HASummit14

US Strategic Command,underground command center prior to 9/1126#HASummit14

#HASummit14

Reduce variability in decision making & improve outcomes

Launch prematurely?

Launch too late?Nuclear Operations28How And Where Can A Computer Help?#HASummit14

Desired Political-Military Outcomes

Retain U.S. society as described in the Constitution

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

Minimize loss of U.S. lives

Minimize destruction of U.S. infrastructure

Achieve all of this as quickly as possible with minimal expenditure of U.S. military resources29#HASummit14

Odd ParallelsClinical observationsSatellites and radar indicate an enemy launchPredictive diagnosisAre we under attack or not?Decision making timeframe< 4 minutes to first impact when enemy subs launch from the east coast of the USTreatment & interventionLaunch on warning or not?

30Healthcare Delivery and Nuclear Delivery

#HASummit1431

SubjectiveObjectiveAssessmentPlan#HASummit14NSA, Terrorists and PatientsThe Odd Parallels of Terrorist Registries and Patient Registries

32#HASummit14Predicting 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?

33#HASummit14Nuclear Weapons Risk ScenariosNUCFLASHAccidental or unauthorized launch that could lead to the outbreak of warBroken ArrowAccidental or unexpected event, e.g., nuclear detonation or non-nuclear detonation or burningEmpty QuiverLoss, theft, seizure, destruction of nuclear weaponBent SpearDamage to a weapon that requires major repair, and has the potential to attract public attentionDull SwordA nuclear safety deficiency that cannot be resolved by the local unit

What are the adverse events we were trying to predict and avoid?34#HASummit14Mr. 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 201435#HASummit14Thank you Sonja Star, New York TimesEvidence Based Sentencing20 States use predictive analytics risk assessments to inform criminal sentencing

36#HASummit14Recidivism Risk Assessment: Level of Service/Case Management Inventory (LS/CMI)*3715 different scales feed the PA algorithmCriminal HistoryEducation/EmploymentFamily/MaritalLeisure/RecreationCompanionsAlcohol/Drug ProblemsAntisocial PatternsPro-criminal Attitude Orientation

Barriers to ReleaseCase Management PlanProgress RecordDischarge SummarySpecific Risk/Needs FactorsPrison Experience - Institutional FactorsSpecial Responsivity Consideration

42.2% of high risk offenders recidivate within 3 years.

*--Nov 2012, Hennepin County, MN, Department of Community Corrections and Rehabilitation#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#HASummit14eHarmony PredictionsHeart of the system: Compatibility Match Processor (CMP)320 profiling questions/attributes per user29 dimensions of compatibility~75TB20M users3B potential matches daily60M+ queries per day, 250 attributes

Thank you, Thod Nugyen, eHarmony CTO 39#HASummit14

Twenty-Nine Dimensions of CompatibilityThank you, Ryan Barker, Principal Software Engineering Matching, eHarmony40#HASummit14

41#HASummit14The Good Judgment ProjectFunded by Director of National Intelligence, brainchild of Philip TetlockCan 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 topicsfrom Eurozone exits to Syrian civil warNon-expert forecasters are 65% better than the experts, 30-60% better than predictive algorithms42#HASummit14Predictive Analytics Inside Healthcare43#HASummit14True Population Predictive Risk Management

Thank you, for the diagram, Robert Wood Johnson Foundation, 2014Very Little ACO InfluenceVery Little ACO Influence>/=30% Waste*100% ACO Influence*Congressional Budget Office, IOM, Best Care at Lower Cost, 2013True Population Health Management44#HASummit14Intermountains ability to extend the boundaries and achieve success is in part due to the communal nature of Utah and the lifestyle choices that Utah citizens choose44

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 inabilityEconomic inabilityPhysical inabilityGeographic inabilityReligious beliefsContraindications to the protocolVoluntarily non-compliant45#HASummit14The key to predictive analytics in the future of healthcare will be the abi