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Predictive Analytics: Dale Sanders Presentation at Plante Moran Healthcare Executive Summit

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What can healthcare executives learn from military decision-making, as it relates to predictiveanalytics in healthcare? As it turns out, quite a lot. Dale Sanders, senior vice president for strategy at Salt Lake City, Utah-based Health Catalyst, drew some surprising parallels between these two vital sectors of the economy during a concluding session at the Plante Moran Healthcare Executive Summit on June 5 in Chicago. His main theme was to remember that in predictive analytic analytics, it's the intervention that matters, noting that much of the industry is seduced by flashy predictive analytics "objects" without thinking through the needed interventions which are needed to get the proper ROI.

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Predictive Analytics: Dale Sanders Presentation at Plante Moran Healthcare Executive Summit

  1. 1. Predictive Analytics: It’s the Intervention That Matters P L A N T E M O R A N H E A LT H C A R E E X E C U T I V E S U M M I T June 4-5, 2014
  2. 2. What Motivates Human Beings? Like it or not, fast or slow, your company now adapts to change, at the speed of software. The decisions you make as executives and leaders about the software that your company uses to run its operations will determine your company’s long long term success or failure. It’s not just facilities, people, and products anymore.
  3. 3. The Agenda  Alignment  Human, societal, and organizational motives with software strategies  General overview of predictive analytics  Nuclear delivery, counter-terrorism, and healthcare delivery  The odd parallels  Predictive analytics in healthcare  When does it work and when doesn’t it?  How much should we expect from it and when?  What about Long Term Care?
  4. 4. Before Healthcare: An Oddly Relevant Career Path  US Air Force CIO • Nuclear warfare operations  TRW  Credit risk scoring, nuclear ballistic missile maintenance and engineering • NSA • Nuclear Command & Control Counter Threat Program • Joint Chiefs of Staff • Strategic Execution Decision Aid 4
  5. 5. Key Messages & Themes 1. Predictions without interventions are useless-- and potentially worse than useless  And those interventions better align with your economic model 2. Some of the most valuable predictions don’t need a computer algorithm  Nurses and physicians can tell you  We already know what the interventions should be 3. Missing data = Poor predictions 4. When it comes to analytics, there is lowering hanging fruit than predictive analytics  Target wasteful healthcare, first 5
  6. 6. Alignment of Motives Human, Societal, Corporate, and Software 6
  7. 7. What Motivates Human Beings?  Mastery: The opportunity to master a skill and be recognized for it  Autonomy: An environment in which people are given the tools and support to work under their own authority  Purpose: Living and working for something larger than themselves  Economics: Enough material wealth to at least live safely and comfortably, if not more With influence from Daniel Pink
  8. 8. Homo Economicus vs. Homo Reciprocans?  Motivated by self-interest or motivated by cooperation?  “…the individual [and company] seeks to attain very specific and predetermined goals to the greatest extent, with the least possible cost.”  “When times are tight, good will takes flight.”
  9. 9. Fee-for-Service vs. Fee-for-Quality Percentage of healthcare dollars spent on fee-for-quality, fixed-fee contracting
  10. 10. General Concepts of Predictive Analytics 10
  11. 11. Challenge of Predicting Anything Human 11
  12. 12. The Basic Process of Predictive Analytics
  13. 13. 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
  14. 14. The Human Data Ecosystem 14
  15. 15. Predictive Precision vs. Data Content 15
  16. 16. Our Healthcare Sampling Rate 16
  17. 17. We Are Not “Big Data” in Healthcare, Yet 17
  18. 18. The Odd Parallels Nuclear Weapons Delivery, Terrorism, and Healthcare Delivery
  19. 19. 19
  20. 20. Where And How Can A Computer Help? Reduce variability in decision making & improve outcomes
  21. 21. Desired Political-Military Outcomes 1. Retain US society as described in the Constitution 2. Retain the ability to govern & command US forces 3. Minimize loss of US lives 4. Minimize destruction of US infrastructure 5. Achieve all of this as quickly as possible with minimal expenditure of US military resources 22
  22. 22. Can We Learn From Nuclear Warfare Decision Making?  “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?
  23. 23. Sortie Turnaround Times The Goal: Predictable, fast turnaround of aircraft to a successful battle 24
  24. 24. Patient Fight Path Profiler The Goal: Predictable, fast turnaround of patients to a good life 25
  25. 25. Healthcare As a Battle Field…?? The Order of Battle and the Order of Care  Demand forecasting: What do we need and when?
  26. 26. NSA, Terrorists, and Patients The Odd Parallels of Terrorist Registries and Patient Registries 27 27
  27. 27. 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? 28
  28. 28. Predicting Patient Risk 29
  29. 29. Predictive Analytics in Healthcare 30
  30. 30. *Apologies for non-attribution. This diagram was taken from a text book many years ago and the specific reference has been lost. COLLECTIVE CONSUMPTION COLLECTIVE CONSUMPTION NATURAL PROGRESSION OF DISEASE PERSONAL CONSUMPTION LOW RISK AT RISK EARLY SIGNS AND SYMPTOMS DISEASE DISABILITY (BODY STRUCTURE AND FUNCTION) CHRONIC CONDITION AND FUNCTIONAL DECLINE DEPENDENCY FOR SELF CARE DEATH GOVERNANCE AND HEALTH SYSTEM ADMINISTRATION COLLECTIVE PREVENTION: Epidemiologic Surveillance and Risk and Disease Control Program Management CURE, TREATMENT, REHABILITATION MAINTENANCE, LTC, PALLIATIVE CARE PERSONAL PREVENTION: Information & Counseling, Immunization, Early Case Detection, Health Condition Monitoring The Healthcare Ecosystem*
  31. 31. True Population Risk Management 32 Robert Wood Johnson Foundation, 2014 Requires a collaborative strategy between leaders in healthcare, politics, charity, education, and business
  32. 32. Healthcare Analytics Adoption Model Level 8 Cost per Unit of Health Payment & Prescriptive Analytics Contracting for & managing health. Tailoring patient care based on population outcomes. Level 7 Cost per Capita Payment & Predictive Analytics Diagnosis-based financial reimbursement & managing risk proactively Level 6 Cost per Case Payment & The Triple Aim Procedure-based financial risk and applying “closed loop” analytics at the point of care Level 5 Clinical Effectiveness & Accountable Care Measuring & managing evidence based care Level 4 Automated External Reporting Efficient, consistent production & agility Level 3 Automated Internal Reporting Efficient, consistent production Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data Level 1 Integrated, Enterprise Data Warehouse Foundation of data and technology Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth
  33. 33. What Are Trying To Predict and Why?  In the current economic model  Those patients and situations that maximize our revenue  In the future economic model  Those patients and situations that maximize our margin  Healthcare predictive analytics vendors are, for the most part, selling concepts that are suited for the latter, not the reality of the former
  34. 34. What Are We Trying to Predict? Why? Common applications being marketed today  Identifying preventable readmissions  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 35
  35. 35. 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  Prior traumatic stupor & coma  Prior nutritional disorders  Diabetic drugs 36Swati Abbott Weighted Predictive Model Now what? Risk of Readmission 36
  36. 36. 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 37
  37. 37. 38 Forecasting: Process Model  Structural Model: Bill of Resources Patient Seen in Emergency Dept Admit Patient: Presumptive Diagnosis: Pneumonia Discharge Monitor Care Delivery Standard Order Sets Equipment Labor Materials Facilities Nursing Orders: Respiratory Therapy: Medication Orders: Resource Demand Day 1 Day 2 Day 3 Day 4 Day 5 Edgewater Consulting
  38. 38. Predictive Analytics: Socioeconomic Data Matters In Healthcare  Not all patients can participate in a protocol  At Northwestern, 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 39
  39. 39. Accounting For These Patients  30% of your patients will have to be treated and/or reached in a unique way • Your predictive algorithms must be adjusted these attributes, especially for readmission • These patients are a unique numerator in the overall denominator of patients under accountable care • You need a data collection & governance strategy for these patient attributes • You need a different interventional strategy for each of the 7 categories • Your physician compensation model must be adjusted for these patient types 40
  40. 40. How Do You Get Started? 41
  41. 41. Start Within Your Scope of Influence We are still learning how to manage outpatient populations 42
  42. 42. Where Do We Start, Clinically? We see consistent opportunities, across the industry, in the following areas: • CAUTI • CLABSI • Pregnancy management, elective induction • Discharge medications adherence for MI/CHF • Prophylactic pre-surgical antibiotics • Materials management, supply chain • Glucose management in the ICU • Knee and hip replacement • Gastroenterology patient management • Spine surgery patient management • Heart failure and ischemic patient management 47
  43. 43. What About Long Term Care? 48
  44. 44. The State of Long Term Care  12 million: The number of Americans expected to need long-term care in 2020.  40%: The percentage of the older population with long-term care needs who are poor or near-poor (income below 150% of the federal poverty level).  78%: Percentage of the elderly in need of long-term care who receive that care from family members and friends.  2.44 years: Average length of stay for current nursing- home residents Morningstar, 2012 The Pending Tsunami
  45. 45. Economics of Long Term Care 50 Kaiser Family Foundation
  46. 46. State of Healthcare IT in LTC  HIT is used primarily for state or federal payment and certification requirements.  There is minimal use of clinical HIT applications.  HIT systems are not integrated.  HIT systems are underused. California Health Care Foundation No Data, No Predictions
  47. 47. Summary 1. Alignment of human, societal, company motives with software strategies is CRITICAL 2. Predictions without interventions are useless 3. Some of the most valuable predictions don’t need a computer algorithm  We already know what the interventions should be 4. Missing data = Poor predictions 5. When it comes to analytics, there is lowering hanging fruit  Target wasteful variability, first  Deming: Where there is variability, there is opportunity 52
  48. 48. Many Thanks…! 53 • Contact information • dale.sanders@healthcatalyst.com • @drsanders • www.linkedin.com/in/dalersanders
  49. 49. Group Discussion 1. What would you like to predict in today’s economic model and why? 2. What would you like to predict in tomorrow’s economic model and why? 3. What data do you need to support precise predictive analytics? 4. What types of new intervention strategies do you need to complement these predictive models? 54 What about Long Term Care?

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