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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<ul><li> Predictive Analytics: Its 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 </li> <li> 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 companys long long term success or failure. Its not just facilities, people, and products anymore. </li> <li> 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 doesnt it? How much should we expect from it and when? What about Long Term Care? </li> <li> 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 &amp; Control Counter Threat Program Joint Chiefs of Staff Strategic Execution Decision Aid 4 </li> <li> Key Messages &amp; 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 dont 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 </li> <li> Alignment of Motives Human, Societal, Corporate, and Software 6 </li> <li> 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 </li> <li> 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. </li> <li> Fee-for-Service vs. Fee-for-Quality Percentage of healthcare dollars spent on fee-for-quality, fixed-fee contracting </li> <li> General Concepts of Predictive Analytics 10 </li> <li> Challenge of Predicting Anything Human 11 </li> <li> The Basic Process of Predictive Analytics </li> <li> 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 </li> <li> The Human Data Ecosystem 14 </li> <li> Predictive Precision vs. Data Content 15 </li> <li> Our Healthcare Sampling Rate 16 </li> <li> We Are Not Big Data in Healthcare, Yet 17 </li> <li> The Odd Parallels Nuclear Weapons Delivery, Terrorism, and Healthcare Delivery </li> <li> 19 </li> <li> Where And How Can A Computer Help? Reduce variability in decision making &amp; improve outcomes </li> <li> Desired Political-Military Outcomes 1. Retain US society as described in the Constitution 2. Retain the ability to govern &amp; 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 </li> <li> 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</li></ul>