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Python What? The Strategist as Data Geek
The document discusses the role and importance of analysts in healthcare strategy, focusing on their skills, tools, and responsibilities in data analysis and decision-making processes. It highlights the need for analysts to convert data into actionable information, support strategic planning, and utilize various analytical tools, including Python and machine learning. Additionally, it includes insights from a case study on optimizing physician referrals through data utilization.
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Python What? The Strategist as Data Geek
- 1. The opinions expressedare those of the presenter and do not necessarily state or reflect the views of SHSMD or the AHA. © 2015 Society for Healthcare Strategy & Market Development Python What? The Strategist as Data Geek October 14, 2015
- 2. Speakers • Patrick Saale–Manager Strategic Resource Group, LifePoint Health • Lee Ann Lambdin – Vice President Strategic Resources, Stratasan 2 Source: Stratasan & LifePoint Health, 2015
- 3. Outline • Role of the Analyst • Skills of the Analyst •Tools of the Analyst – Python What? – Resources • Data Information Better Decisions • Case Study – LifePoint Hospitals, Physician Referral 3 Source: Stratasan & LifePoint Health, 2015
- 4. Survey Results • Surveyed Stratasan customers for their perspective on the role of the analyst •21 responses are summarized in the following slides • 14 responders were analysts and 7 were users of analysts • Employed primarily by health systems and hospitals 4 Source: Stratasan & LifePoint Health, 2015
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- 6. Skills of theAnalyst Ability to be creative and tell a story with data Analytical Thinking Data Interpretation Summarizing vast amount of information Computer/Arithmetic Skills Excel #1 PowerPoint Mapping Access SPSS/Statistics Critical Thinking/Strategic Thinking Problem Solving Inquisitive Attention to Detail Accuracy Ability to Communicate/Present Time Management 6 Source: Stratasan & LifePoint Health, 2015
- 7. Best Verbatim Commentson Skills 7 “Ability to determine what your customer really needs instead of always just doing exactly what they ask you to do” “Support and influence others in appropriate use of data” “Master necessary programs to analyze data to tell the story” “Analytics tool knowledge (Excel, Access, SPSS, etc.) doesn’t really matter which one as long as you know it” “Ability to understand data trends and use it to tell a story” “Knowledge of the field’s terminology and data available including sources of data” Source: Stratasan & LifePoint Health, 2015
- 8. Tools of theAnalyst: Most Important Data Sources Internal hospital or system data (financial & volume)/company results, E.H.R. State IP, OP, ED, Observation databases (where available) Demographics Mapping software Federal Data (Medicare) Industry and competitor research Google searches Psychographics (Tapestry Segmentation) 8 Source: Stratasan & LifePoint Health, 2015
- 9. Top Questions &Project Requests • What’s my market share? – Reasons for growth/decline? • What’s our outmigration? • What are my competitors doing? • What are my opportunities for growth? – Are there needs in the area not currently being met? • What is the profitability of service lines? • How many cases are coming from ____? • How many doctors do I need? • Where do the doctors need to be located? • Operational performance? 9 Source: Stratasan & LifePoint Health, 2015
- 10. Best Verbatim Comments:Questions 10 “Can we change this? Can we have an update? Can we get it before the deadline?” “Market share reports to determine current volume, potential added volume, capacity and service needs” “Market and finance data reports for specific service lines”“Do we need more or less physicians and where do they need to be located?” “‐ Create a map with data ‐ Summarize the data ‐ Trend the data” Source: Stratasan & LifePoint Health, 2015
- 11. Anything else weneed to know about Analysts? 11 “They need to always be focused on helping planning and marketing generate ROI. Because they are most often the most analytical thinkers of the group, they need to lead the charge in measuring and planning how we can prove the value of what marketing and planning brings to the table.” “Need to be creative and think outside the box. Good communication skills and ability to ask questions about what is trying to be accomplished that will influence data support and analysis.” “They are really smart!” “We're awesome ;)” “good analysts want to spend more time thinking about how to help solve problems by drawing conclusions from data, and less time on mundane task work.” Source: Stratasan & LifePoint Health, 2015
- 12. Hiring: What toLook for in an Analyst • Critical thinking skills • Holistic decision‐making • Use of data to inform decision‐making • Knowledge of how to leverage people who know Python and big data • Understanding that no one person can do it all • Specific skills for specific roles 12 Source: Stratasan & LifePoint Health, 2015
- 13. Best Use ofAnalysts’ Skills 13
- 14. 14 DIKW Pyramid: TheConceptDIKW Pyramid: The Concept WisdomWisdom KnowledgeKnowledge InformationInformation DataData Source: Stratasan & LifePoint Health, 2015
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- 20. Bridging Worlds: Generate Data-DrivenInsight Attributes, Skills and Tools ? ? ? ! ! ? ? ! 20 Source: Bridging Worlds, SHSMD, 2014 p.57; used with permission ! ! ! ! ! !
- 21. What’s important foryou? 21 Source: Bridging Worlds, SHSMD, 2014 p.57; used with permission
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- 23. How Big isBig? 23 Big Data Medium Data Small Data A lot more problems with medium and small data, and opportunities in the data you deal with every day Source: Stratasan & LifePoint Health, 2015
- 24. Look at theData • What is in this data? • What question am I trying to (can I) answer with this data? • How do I leverage the data to answer the question? 24 Source: Stratasan & LifePoint Health, 2015
- 25. Glossary: Analyst asData Geek • Handout –Definitions –Uses 25 Source: Stratasan & LifePoint Health, 2015
- 26. Look at theData • R – R Studio is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms (foundation operating systems are built on), Windows and MacOS. • SPSS – IBM SPSS Statistics is an integrated family of products that addresses the entire analytical process, from planning to data collection to analysis, reporting and deployment. Used for describing large data sets, for example 3 years of patient data. • SAS – Another brand of statistical software • Python – is a programming language that has powerful libraries for data analysis. It also allows you to automate steps of processing or analyzing data. 26 Source: Stratasan & LifePoint Health, 2015
- 27. Actual Python code: scriptthat is loading ICD10 codes into a database from CSV files so we can run queries and joins 27 Source: Stratasan & LifePoint Health, 2015
- 28. Process the Data •How do I make the data useful? • What are we going to do to it? – Rollups, aggregation, curation, cross‐walking – Machine learning (fancy statistics) • Where are we going to do it? – Your laptop – Cloud computing – Hadoop 28 Source: Stratasan & LifePoint Health, 2015
- 29. Nobody Understands theCloud 29 Source: Stratasan & LifePoint Health, 2015
- 30. • Cloud computing – Cloud computing allows you to use computers you don’t own to operate programs you use. Gmail, anything from Google. You can purchase access to warehouses of computers via cloud providers like Amazon, Google, and Microsoft. This allows you to run tools like Hadoop to process large amounts of data. Processthe Data: Cloud 30 Source: Stratasan & LifePoint Health, 2015 Intel has launched Collaborative Cancer Cloud, a new service to enable providers and researchers to securely share genomic, imaging and clinical data among participating organizations across the globe. By 2020, the goal is to have physicians be able to give a patient a diagnosis and generate a specific treatment plan within 24 hours. Over time, the platform will be modified to support other types of research and treatment.
- 31. Process the Data:Machine Learning & Predictive Analytics 31 Source: Stratasan & LifePoint Health, 2015 “For the past 10 years, we have been working on that area,” Ebadollahi said. “We have very advanced machine learning, pattern recognition, on imaging and video in general, most especially in medical imaging. Now, this intent to acquire Merge will bring a conduit to attach those technologies coming out of our research.”
- 32. Analyzing & Presentingthe Data • How to make the data tell a story? –Excel –PowerPoint –GIS –Tableau –JavaScript –D3 32 Source: Stratasan & LifePoint Health, 2015
- 33. Analyzing & Presenting:Excel • Pivot Tables • Macros • Cell Links • V‐Lookup or Index Match • Format Painting • Custom Sorts 33 Source: Stratasan & LifePoint Health, 2015
- 34. Analyzing & Presenting: PowerPoint •Custom color palate and template with logo • Graphs, graphs, graphs • Add maps and photos • Tell a story 34 Source: Stratasan & LifePoint Health, 2015
- 35. Analyzing & Presenting:Tableau • Business analytics software • Business dashboards • Big data analysis • Data discovery • Social media analytics “We are looking to move our market share reporting to Tableau within the year, as the level of detail we’re being asked to report on has grown beyond Excel’s capacities.… It’ll increase automation and decrease errors on our part.” ‐Stratasan customer 35 Source: Stratasan & LifePoint Health, 2015
- 36. Analyzing & Presenting:JavaScript • JavaScript ‐ This programming language is all about presentation layer (charts, graphics, and user interaction). It is the glue that holds Internet together. Every modern browser runs Javascript. • D3 ‐ D3.js is a powerful JavaScript library for producing dynamic, interactive data visualizations in web browsers. 36 Source: Stratasan & LifePoint Health, 2015
- 37. Analyzing & Presenting:GIS • GIS – A Geographic Information System enables you to envision the geographic aspects of a body of data. This lets us visualize, question, analyze, and interpret data to understand relationships, patterns, and trends. (Esri) Used primarily in government, conservation, zoning and construction. – Esri ArcGIS • Very granular demographic data – example patient origin by block group, demographics by block group 37 Source: Stratasan & LifePoint Health, 2015
- 38. 38 C A RD I O L O G Y P RO J E C T E D B L O C K G RO U P VO L U M E C A R D I O L O G Y B L O C K G RO U P S C O R E C A R D Block Groups outlined in green are considered the best targets Block Groups grayed out do not have the desired tapestries Source: Stratasan & LifePoint Health, 2015
- 39. Brentwood Emergency PatientOrigin by Block Group 39 B R E N T WO O D M E D I C A L C E N T E R E M E R G E N C Y PAT I E N T O R I G I N B Y B L O C K G RO U P Source: Stratasan & LifePoint Health, 2015
- 40. Analyzing & Presenting: TapestrySegmentation • ESRI Tapestry data – Tapestry segmentation provides an accurate, detailed description of America's neighborhoods— U.S. residential areas are divided into 67 distinctive segments based on their socioeconomic and demographic composition—then further classifies the segments into LifeMode and Urbanization Groups. Tapestry Segmentation is used to target your population with specific messages that are meaningful to the specific population. 40 Source: Stratasan & LifePoint Health, 2015
- 41. Northern Block Groups are zoomed in the next map 41 D O MI N A N T TA P E S T R Y S E G M E N TAT I O N B L O C K G R O U P • The Tapestry Segmentation and LifeMode (Psychographic Profile) for each Block Group is represented by a Number & Letter combination • This ID helps guide your marketing execution plan D O M I N A N T TA P E S T RY S E G M E N TAT I O N B L O C K G RO U P Source: Stratasan & LifePoint Health, 2015
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- 46. Resources to LearnMore • Coursera – https://www.coursera.org/ • Python website – https://www.python.org/about/ • YouTube – https://www.youtube.com/watch?v=puS8Tu3JPnU – https://www.youtube.com/watch?v=GZpwGt0hzKs • 46 Source: Stratasan & LifePoint Health, 2015
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- 48. Case Study: PhysicianReferrals • Situation: A data set that had not been previously utilized by the organization was introduced • Outcome: The organization reacted to optimize the use of the information through an entirely new set of processes and a change in organizational structure • Next comes the “How”… 48 Source: Stratasan & LifePoint Health, 2015
- 49. Data Science • Step 1 ‐Look at the data: – Source NPI Numbers – Destination NPI Numbers – Shared Patients • Step 2 – Process the Data (Connecting the Dots): – Data will give information about the relationships between physicians. – Enough organizational savvy to know who could use the information (Physician Sales Team) – engage them on the discovery phase. – Identify other sources of information that will help to add context 49 Source: Stratasan & LifePoint Health, 2015
- 50. Data Science • Step 3 ‐Present the Data: – Nicely formatted Excel table – Map – Infographic 50 Source: Stratasan & LifePoint Health, 2015
- 51. Medicare Physician Referral Databases •https://questions.cms.gov/faq.php?faqId=7977 • Files are 1‐7 gigabytes • 30 day referrals 2.4 gigabytes • Smallish data; too big for Excel 51 Source: Stratasan & LifePoint Health, 2015
- 52. Physician Network Intelligence Provider 1 The patient volume when two providers bill Medicare for the same patient within 30 days of each other. Provider 2 52Source: Stratasan & LifePoint Health, 2015; Medicare Referral Database (2014)
- 53. TOTA L VI S I T S B Y Z I P C O D E G R E AT E R H E A LT H S Y S T E M M A R K E T S H A R E Source(s): Stratasan (2014); Esri (2014); Medicare Referral Database (2014)53
- 54. 2014 Medicare Physicianto Physician Network Summary - Primary Care Doctors to Any Orthopods James Kessler William Miller Lawrence Supik Martin Senicki Ryan Slechta William Handley James Kessler (Angel) William Miller (Haywood) Employed Total 27% 12% 15% 34% 88% 7% 5% Anthony Esterwood 0% 0% 23% 78% 100% 0% 0% Beth Bailey 46% 25% 0% 30% 100% 0% 0% Elizabeth Dixon 44% 36% 20% 0% 100% 0% 0% Ewa Susfal 0% 0% 17% 60% 77% 23% 0% Lee Ann Manthorne 58% 42% 0% 0% 100% 0% 0% Randall Provost 60% 25% 15% 0% 100% 0% 0% Steven Queen 58% 18% 0% 0% 76% 0% 24% Todd Davis 16% 0% 18% 45% 79% 9% 11% Private Total 27% 42% 17% 4% 90% 0% 10% Matthew Mahar 29% 35% 8% 0% 73% 0% 27% Ofelia Balta 45% 24% 21% 10% 100% 0% 0% Roy Gallinger 34% 27% 16% 0% 77% 0% 23% Thomas Wolf 27% 44% 19% 11% 100% 0% 0% Grand Total 27% 23% 15% 24% 89% 4% 7% PCP Status/Name Employed Orthopods Employed Total Sources: 1. CMS Physician Referral Patterns 2013 - 2014 30 day interval: https://questions.cms.gov/faq.php?faqId=7977 2. NPI Monthly File; http://nppes.viva-it.com/NPI_Files.html 3. Physician Compare Downloadable Database; https://data.medicare.gov/data/physician-compare Targeted SalesVisit initiated with Dr. Susfal to understand reason for leakage 54 Source: Stratasan & LifePoint Health, 2015
- 55. Results • LifePoint has reduced referrals to Dr. Kessler from Dr. Susfal25%. 55 Source: Stratasan & LifePoint Health, 2015
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- 57. Conclusions • Use data to tell a story and make better decisions • Learn new things all the time •Search for answers • Focus on the important; don’t major in the minors • Acknowledge you can’t know everything, but recognize what you don’t know 57 Source: Stratasan & LifePoint Health, 2015
- 58. The opinions expressedare those of the presenter and do not necessarily state or reflect the views of SHSMD or the AHA. © 2015 Society for Healthcare Strategy & Market Development