Dale Sanders SVP Strategy Health Catalyst

1. 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

2. 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

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

5. 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?

6. Common Concepts & Provocative Thoughts

7. Man vs. Machine  Objective Subjective Man + Machine

8. 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?

9. The Basic Process of Predictive Analytics

10. “Beyond math, there are no facts; only interpretations.” - Friedrich Nietzsche

11. Challenge of Predicting Anything Human

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

13. 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

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

15. The Human Data Ecosystem

16. We Are Not “Big Data” in Healthcare, Yet

17. Predictive Precision vs. Data Content

18. The Wisdom of Crowds & Suggestive Analytics

19. The Wisdom of Crowds* • The Criteria For Designing A “Good” Crowd *--James Surowieki

20. Poll Question #2: Guess The Weight Of The Steer 2014 Southwest Washington State Fair Charlie Brown, 8-yr old Swiss steer; the Guessee Dave Fenn, Owner Levi Wallace, Guessor

21. 2,767 pounds…!

22. Amazon: Predictive or Suggestive?

23. Poll Question #3 How many physicians were working in Utah in 2010? 2012 Physician Workforce Report from the Utah Medical Education Council

24. 5,596

25. Predictive Analytics Outside Healthcare

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

28. Nuclear Operations • How And Where Can A Computer Help? Reduce variability in decision making & improve outcomes Launch prematurely? Launch too late?

29. 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 resources

30. 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?

31. Subjective • Objective • Assessment • Plan

32. NSA, Terrorists and Patients The Odd Parallels of Terrorist Registries and Patient Registries

33. 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?

34. 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

35. “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

36. “Evidence Based” Sentencing 20 States use predictive analytics risk assessments to inform criminal sentencing Thank you Sonja Star, New York Times

37. Recidivism Risk Assessment: Level of Service/Case 
Management Inventory (LS/CMI)* • 15 different scales feed the PA algorithm 42.2% of high risk offenders recidivate within 3 years. *--Nov 2012, Hennepin County, MN, Department of Community Corrections and Rehabilitation 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

38. “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.”

39. 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

40. Twenty-Nine Dimensions of Compatibility Thank you, Ryan Barker, Principal Software Engineering – Matching, eHarmony

42. 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

43. Predictive Analytics Inside Healthcare

44. True Population Predictive Risk Management Very Little ACO Influence True Population Health Management >/=30% Waste* 100% ACO Influence Very Little ACO Influence Thank you, for the diagram, Robert Wood Johnson Foundation, 2014 *Congressional Budget Office, IOM, “Best Care at Lower Cost”, 2013

45. 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

46. 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?

47. 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 Risk of Readmission Which evidence-based Intervention? How much will it cost? How much will it reduce risk? Thank you, Swati Abbott

48. Most Common Causes for Readmission Robert Wood Johnson Foundation, Feb 2013 Patients have no family or other caregiver at home Patients did not receive accurate discharge instructions, including medications Patients did not understand discharge instructions Patients discharged too soon Patients referred to outpatient physicians and clinics not affiliated with the hospital

49. 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

50. Closing Thoughts & Questions 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?