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Session #9 Getting The Most Out Of Your Data Analyst

Session #9 Getting The Most Out Of Your Data Analyst. John Wadsworth. Vice President, Technical Operations, Health Catalyst . Today’s Agenda. Unlock the data Analytic tools Prove or analyze? Analytic whiplash Accepting the truth. Poll Questions 1 - 2. Question #1 -

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Session #9 Getting The Most Out Of Your Data Analyst

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

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

  3. 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?

  4. Unlock Your Data

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

  6. Analysts - Hunting and Gathering 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?

  7. Unlocking Your Data - Exercise • 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!

  8. Catalyst Adaptive Data Warehouse Adaptive Data Warehouse Model Metadata: EDW AtlasSecurity and Auditing FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) DEPARTMENTAL SOURCES (e.g. Apollo) Common, Linkable Vocabulary FinancialSource Marts DepartmentalSource Marts Pneumonia AdministrativeSource Marts PatientSource Marts PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) Diabetes ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Surgery …MANY more! EMR Source Marts HRSource Mart Human Resources (e.g. PeopleSoft) EMR SOURCE (e.g. Epic, Cerner) More Transformation Less Transformation

  9. 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?

  10. Analytic Tools 10

  11. Analytic Tools - Exercise • 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.

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

  13. 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. 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?

  15. Prove This 15

  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. Analyze the Data to Inform a Decision 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

  18. Analytic Whiplash 18

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

  20. Whiplash Cycle • 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”.

  21. Considerations for Improved Analytic Insight • 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.

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

  23. Do You Need More Analysts? • 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.

  24. Leadership and Prioritization • 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

  25. Accepting the Truth 25

  26. 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?

  27. Should I report the whole truth? • 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

  28. Summary • 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

  29. Analytic Insights Questions & Answers A

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

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

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