1. #HASummit14 John Wadsworth Session #9 Getting The Most Out Of Your Data Analyst Vice President, Technical Operations, Health Catalyst

2. #HASummit14 Today’s Agenda  Unlock the data  Analytic tools  Prove or analyze?  Analytic whiplash  Accepting the truth

3. #HASummit14 Poll Questions 1 - 2 Question #1 - How much time would you estimate your analysts spend gathering data (vs analyzing data)? Question #2 – How much time would you estimate your analysts spend analyzing data? 3

4. #HASummit14 Unlock Your Data 4

5. #HASummit14 5 “I told you I wasn’t a hunter gather. I’m an analyst!”

6. #HASummit14 6 Conversation this week: Shared savings partnerships withholding money because of poor analytics Analysts spending 80% or more gathering data Data exists in multiple sources (EMR, costing, billing, patient satisfaction, etc.) that are not integrated How hard can it be to gather the data for analytics? Analysts - Hunting and Gathering

7. #HASummit14 7 Objective #1: Determine the total value of the money in your team bucket. Rules Work as a team. Everyone at your table needs to participate. Collect your bucket at corresponding colored locations around the room. Send 1 person (runner) from each table. Do not retrieve the bucket until you are given the “Go” signal. All teams start at the same time. With each task that you complete, ring the bell and you will be given an additional task. Complete as many tasks as possible as a team. Time limited, so work quickly! Unlocking Your Data - Exercise

8. #HASummit14 Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Administrative Source Marts Departmental Source Marts Patient Source Marts EMR Source Marts HR Source Mart Diabetes …MANY more! Pneumonia Less TransformationMore Transformation Catalyst Adaptive Data Warehouse FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Epic, Cerner) EMR SOURCE (e.g. Epic, Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) Human Resources (e.g. PeopleSoft) Human Resources (e.g. PeopleSoft) Adaptive Data Warehouse Model Surgery

9. #HASummit14 Poll Questions 3 - 4 Question #3 In your personal opinion, how important is the analyst role in your organization? Question #4 How important is the role of analyst viewed by your organization? 9

10. #HASummit14 Analytic Tools 10

11. #HASummit14 11 Objective #1: Group your coins by denomination AND stack them at least 5 coins high. Rules Work as a team. Everyone at your table needs to participate. Do not open the bucket until you are given the “Go” signal. All teams start at the same time. With each task that you complete, ring the bell and you will be given an additional task. Complete as many tasks as possible as a team. Your entire team MUST use the (hand) tools provided you for the complete exercise. Analytic Tools - Exercise

12. #HASummit14 12 Tools Support Transformation Structured Query Language (SQL or variant) Data analysis Visual representation of information Communicate meaningful story through the data Domain knowledge From Hunter-Gather to Analyst

13. #HASummit14 Recommended Tools for Data-Driven Health System Source systems that support query (SQL) Let them get to the data Business intelligence development tools to build meaningful visualizations Cognos, Crystal Reports, Tableau, Qlikview, Excel An enterprise data warehouse (EDW) Start small and grow as needed Assumes data architects will extract, transform, load (ETL) and model data into warehouse Scalable platform to grow analytics

14. #HASummit14 Poll Questions 5 - 6 Question #5 How often do you act on information provided to you by your analysts? Question #6 Analysts – How often does management act on your analysis and/or recommendations? 14

15. #HASummit14 Prove This 15

16. #HASummit14 16 Analyze the Decision to Build “We need to build an observation patient wing” ‒ 3 year upward trend in observation patient volume through ED ‒ Reimbursements dropping for obs patients  get to inpatient or ED acuity ‒ Historically we had an observation wing Questions they wanted answered ‒ How many beds do we need? »Clinical data informed bed count estimates ‒ What clinical staffing will be needed for the new wing? »HR and clinical data justified staffing model ‒ What will it cost to build the new wing? »Costing data supported estimate of $.5M - $1M/bed for re- purposing existing beds  $2.5M - $5M for 5 bed wing WAIT! Has the decision to build already been made? ‒ If so, do you need an Analyst … or something else?

17. #HASummit14 17 We asked, “What can the data tell us about the observation patients?”  ~70% had chief complaint of chest pain  ~90% existing patients in the hospital system  ~80% with chest pain had former diagnosis of heart failure from cardiology clinic/primary care  ~75% arrived in ED between 5-10 PM  Cardiology clinic closed at 5:00 PM Analyst recommendation  Keep the cardiology clinic open until 10:00 PM  Don’t spend the $2.5M - $5M for an observation wing Analyze the Data to Inform a Decision

18. #HASummit14 Analytic Whiplash 18

19. #HASummit14 19 “ I could catch a trout on a dusty road.”

20. #HASummit14 20 Leadership discovers a problem Analyst assigned to provide insight Analyst & others study problem to define scope Data gathered then analyzed Patterns and correlations begin to emerge Leadership brings another problem for analysis or changes direction. Analyst told to “wrap it up and move to the next problem”. Whiplash Cycle

21. #HASummit14 21 Analyst & others study problem to define scope Data gathered then analyzed Patterns and correlations begin to emerge Assumptions verified/refuted by knowledge experts closest to the work process being measured Adjust logic based on feedback. Iterate through process until all logic validated by process owners (in the trenches) Give sufficient time for analysis, discovery and a recommendation. Considerations for Improved Analytic Insight

22. #HASummit14 22 Insufficient time leads to half-baked analysis Incomplete analysis undermines credibility Lack of credibility creates further dissatisfaction with data and analytics Risks of Under-resourced Analytics

23. #HASummit14 23 Perhaps… but before you hire more analysts, consider asking: ‒ Will more analysts get the needed time to do analysis? ‒ No? Increased capacity for incomplete analysis Analyst needs the time to work smarter, not harder. Do You Need More Analysts?

24. #HASummit14 24 Remove prioritization burden from analysts Leadership become proficient with prioritization Leadership determine projects of highest priority ‒ Unified front – individual agendas undermine execution ‒ Decide what projects will and will not be funded ‒ Resist the lure of shiny, new objects ‒ Commit resources for top projects to completion ‒ Communicate results of prioritization to the masses Minimize the whiplash of “urgent” projects Leadership and Prioritization

25. #HASummit14 Accepting the Truth 25

26. #HASummit14 Poll Questions 7- 8 Question #7 On a scale of 1 to 5, how well do you trust information provided through your analysts? Question #8 On a scale of 1 to 5, how well does your culture support analysts delivering information that may be perceived as negative or undesirable? 26

27. #HASummit14 27 Honesty is the best policy for analytic credibility CLABSI reported or actuals? Confront the brutal facts ‒ “When you turn over rocks and look at all the squiggly things underneath, you can either put the rock down, or you can say, ‘My job is to turn over rocks and look at the squiggly things,’ even if what you see can scare the [heck] out of you.” – Jim Collins Should I report the whole truth?

28. #HASummit14 28 Unlock the data for your analysts Get the right tools for your analysts and organization Leadership become proficient in prioritization Develop a culture of accepting the truth Summary

29. #HASummit14 Analytic Insights A Questions & Answers

30. #HASummit14 Session Feedback Survey 30 1.On a scale of 1-5, how satisfied were you overall with this session? 1)Not at all satisfied 2)Somewhat satisfied 3)Moderately satisfied 4)Very satisfied 5)Extremely satisfied 3.On a scale of 1-5, what level of interest would you have for additional, continued learning on this topic (articles, webinars, collaboration, training)? 1)No interest 2)Some interest 3)Moderate interest 4)Very interested 5)Extremely interested 2.What feedback or suggestions do you have?

31. #HASummit14 Upcoming Keynote Sessions 3:45 PM – 4:40 PM 13.Healthcare Reform 2.0: Anticipating What’s Next Governor Mike Leavitt Founder and Chairman of Leavitt Partners Former Secretary of the Department of HHS 5:15PM – 6:00 PM Reception 6:00PM – 7:00 PM Dinner 7:00PM – 7:50 PM 14.The Acceleration of Technology In The 21 st Century: Impacts on Healthcare and Ray Kurzweil Chairman, Kurzweil Technologies Director of Engineering, Google 7:50PM – 8:30 PM Entertainment 31 Location Main Ballroom