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Perspectives on analytics in the health care industry

I was invited by Cal State University to speak to faculty and students in a 'weekend professionals' series of discussions held by the Mihaylo School of Business on the Fullerton campus. I presented this discussion on "Perspectives in Analytics in the Healthcare Industry" to people from both the Fullerton and Long Beach campuses.

This is in part an expansion on a panel discussion I participated in last spring at the University of Southern California Marshall School of Business on "Big Data applications in Healthcare".

It was great to have a fun and interactive discussion with both the teaching staff and Masters program students. This was another great opportunity to both give back to the next generation of analysts as well as help identify great new talent coming up in the world.


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Perspectives on analytics in the health care industry

  1. 1. A High Level Review of Major Healthcare Vertical Utilization for Students and Faculty Presented September 26, 2015 California State University, Fullerton Mihaylo College of Business and Economics PERSPECTIVES ON ANALYTICS IN THE HEALTHCARE INDUSTRY
  2. 2. TODAY’S TOPICS • Who am I? • What is a HealthCare Vertical, and what types are there? • Common themes: BI and data integration • Reporting vs Analysis vs Modeling • Analytic approaches: dash boarding, scenarios, trending • Modeling approaches: P4P and BF Skinner, Epidemiology, predictive risk modeling • Practical implementations of Advanced Analytics in HealthCare • Future tools: Mobile, Internet of Things™, pre-clinical intervention via modeling • A thought about tools • What about careers? • Open discussion! Don’t fall asleep
  3. 3. Who am I? • Michael Phipps Director Clinical & Operational Analytics New Century Health https://www.linkedin.com/in/michaelwphipps • Administrator of Analytics & HCA Internship program • Business owner of the “BI Stack” • Former management of Advanced Analytics @ Fortune 200 company • 20+ years on both Provider / Payor side in health care • IT and Business experience • 5+ SQL dialects, 4+ BI GUI solutions, several certifications, dozens of data warehouses • I am an analyst first and a leader second. I love talking shop. • Avid Dr. Who fan, desperately waiting for Fallout 4 and the Star Wars premier
  4. 4. Ground Rules • Geeking out is part of the job and a big part of making it fun. Please relax  • If you have a passion about anything, you can apply your passion to analytics. • Good analysts are invariably educators. This is part of my ethos. • There is no greater compliment one analyst can pay another than to help them become better at their craft. • Please ask questions, challenge assertions, and above all ENGAGE! • I promise not to take any of this too seriously, and I encourage you to as well.
  5. 5. WHAT IS A HEALTHCARE VERTICAL? A vertical, or channel, is how we describe the major domains within the healthcare industry. Each vertical will have specializations in analytics, but they will also have commonalities! Pharma •Development / testing •Marketing •Finance Insurance •Government Programs •Commercial •Exchange •Specialty Delivery •Hospitals •Physician groups •Ancillary providers •Social work Research •Devices •Procedures •Drugs •Other Academia Regulation •State •Federal •Oversight agencies Others •Software developers •Systems support •Revenue Cycle The patient exists across each domain, but few recognize non-delivery aspects Fun fact: my vertical is approximately 7”
  6. 6. • Regardless of the vertical, analytics plays a key component • While specialization exists, core competencies cross domains especially in entry level roles • Business Intelligence (BI) isn’t all about the fancy tools. Consider MS Excel- the most popular BI tool on the planet • Major concepts to consider • Situational awareness • Process improvement • Trend analysis • Predictions • Previously, many of these verticals were highly siloed • Even within the vertical itself, sub-silos were common • Early integration efforts focused on financial analysis and research support, a lot of data warehouses / marts / stores / etc. • These days, ‘lateral analytics’ and similar nonrelational approaches are replacing DWs using metadata • This results in massively complex capabilities with specialized needs COMMON THEMES: BI AND DATA INTEGRATION
  7. 7. • “What next & control” • Highly technical & heavy stats • Focused on predictive data • Most data pulls are model training • Not typically client facing • Context is mostly strategic • “Who, what, & where” • More technical • Focused on established metrics • A lot of data pulls • Context is mostly operational • Visualizations REPORTING VS ANALYSIS VS MODELING Reporting Analysis • “Why and how” • Technical, creative, and math • Development of metrics • Data pulls, visualizations, and explanations • Blend of operational and strategic Modeling Each of these roles are critical in Analytics, but very different applications
  8. 8. ANALYTIC APPROACHES: DASH BOARDING, SCENARIOS, TRENDING • Dash boarding is a favorite executive “buzz word” • Good dashboards are RARE. Most are basically brochure-ware and not actionable • Interactivity, trending, drilling, and directed observation are game-changers • Focus on the “call to action” • Scenarios and “walled garden” approaches are powerful tools to drive enterprise thought • Never perfect, always evolving • Excellent for mid-level leadership and external consumers • Outstanding mobile opportunities • Trending and benchmarking is fundamental to any analysis. Context is everything. • Delivering data is easy(ish). Insight is hard. Action requires vigilance and maintenance. • Outliers vs artifacts and data cleansing is critical to quality outcomes
  9. 9. MODELING APPROACHES: P4P AND BF SKINNER, EPIDEMIOLOGY, PREDICTIVE RISK MODELING • Modeling is one of the hottest segments of analytics these days (even hotter than “Big Data”) • Free or open source tools, benchmarks, and datasets are available that are actually usable by the average analyst • The “Democratization of data” is accelerating, spurred by EMR adoption, increasing standardization, mobile platform adoption, and integration of BI concepts into “tribal knowledge” • Pay for Performance and Evidence Based Medicine are becoming the standard, and in some case legally required. The analytic burden of evidence in these cases is significant! • Reinforcement via funding drives prescribing behavior and is saving lives (and money)- see HAC/POA, Outcome based Physician compensation agreements • Member level risk modeling is now used in 100% of Medicare Part C & D populations, most Medicaid populations, and in several commercial implementations. This is a major analytic advance integrating clinical outcomes, financial trending, and predictive modeling
  10. 10. PRACTICAL IMPLEMENTATIONS OF ADVANCED ANALYTICS IN HEALTHCARE Non-Margin driven revenue (AKA Float or Treasury income) 1 2 3 4
  12. 12. FUTURE TOOLS: MOBILE, INTERNET OF THINGS™, PRE-CLINICAL INTERVENTION VIA MODELING • Mobile BI is revolutionizing healthcare. Consider “Safewatch”, Visible Body, and more • Mobile no longer means one-way communication. Telemedicine is becoming more common and is leaving the hospital • Even the FDA has released formal guidelines for mobile apps and analytic use. • Devices are now talking to your doctor for you. From scales to blood testing to heart monitoring, all of these devices result in massive biomarker datasets • Pre-clinical interventions have really become a bleeding edge tool! Depression screening is one example (Facebook) • Epidemiologic surveillance now integrates social media, mobile platforms, and telemedicine. This surveillance is increasing efficacy of treatments and reducing time to care. From this: To this:
  13. 13. • SAS, R, MATLAB • Business Objects, EXCEL, Tableau • MSSQL, Oracle, Teradata • PMP, CAPM, MS Project • Outlook, Lync, SalesForce, ConstantContact • LinkedIn, Facebook, Branchout • Informatica, Infosphere, Embarcadero • DVO, TALEND, Solix • Kimball approach, Inman paradigm • Statistical modeling techniques • GUI driven BI tools • ANSI SQL • Project Management techniques • Communication Skills (Staff, Leadership, and Executive levels) • Building relationships • Data Architecting methodologies • Validation approaches • Data Pump concepts A THOUGHT ABOUT TOOLS Focus on this: Not this: A poor mechanic blames their tools. A wise mechanic improves their hands.
  14. 14. HOW TO GAIN SKILLS Analytics is EVERYWHERE!
  15. 15. WHAT ABOUT CAREERS? http://www.bloomberg.com/news/articles/2015-09-22/these-are-the-careers-where- workers-are-seeing-the-highest-pay-growth Analytics professionals have many hats, and many names: • Business Analyst • Data Analyst • Business System Analyst • Programmer • Operator Best advice for your career search: • Get in the door any way you can • Find your passion by jumping in • Create your own roles Health care is an enormous industry and a small community