How to Transform Health Plan Data Into Meaningful Insights

Does your plan have what it takes to build reliable reports and move from descriptive to prescriptive analytics? Our experts explore the following in this presentation:
• Healthcare data types, sources and formats
• The shift from descriptive to prescriptive analytics
• Best practices for internal/external collaboration
• Must-have skills to manage and manipulate healthcare data
• Reporting tools now available on the market

Transcript

1. ® How to Transform Health Plan Data Into Meaningful Insights • Amy Smith, RN, BSN Manager Business Intelligence • Hemant Lanjewar, MHSA, Vice President, Data Sciences & Clinical Quality
2. 2 Welcome • Today’s webinar is one hour and is being recorded • Questions • We will address questions at the end of the session • Enter a question in the box on the bottom right side of your screen
3. 3 Presenters Amy Smith, RN, BSN Manager Business Intelligence Hemant Lanjewar, MHSA Vice President, Data Sciences & Clinical Quality
4. Purpose • Why is healthcare data so challenging? • How has Altruista Health Analytics dealt with the challenges? • What are our recommendations and considerations to be successful? • How to decide on best tools and strategy? • How does Altruista Health support our clients in a challenging analytics environment? 4
5. 5 Data (raw, non-contextual, building blocks) Information (processed, contextual, summarized/analyzed) Knowledge (patterns, trends) Intelligence (decisioning, predicting, optimizing) Data to Intelligence Data Intelligence
6. Sources of Data Patient Medical Records/EMR Claims Data (Medical, BH, Ancillary) Pharmacy Data Patient Registries HC Operations Systems (UM, CM, CRM) Patient Generated Data Demographic and Social Data Population Data (Benchmarks, Vital Statistics) 6
7. Data Limitations PHI (Protected Health Information) Non-PHI Regulatory Member-preference Proprietary 7
8. Key Characteristics 8
9. COMPLETENESS: IS ALL NEEDED DATA AVAILABLE? VALIDITY: ARE DATA VALUES WITHIN ACCEPTABLE VALUE DOMAINS? ACCURACY: DOES DATA REFLECT A VERIFIABLE SOURCE? CONSISTENCY: IS DATA CONSISTENT BETWEEN SYSTEMS? INTEGRITY: ARE THE RELATIONS BETWEEN ENTITIES AND ATTRIBUTES CONSISTENT? TIMELINESS: IS THE DATA AVAILABLE WHEN IT IS NEEDED? 9 Data Quality Dimensions
10. Establish Business Rules for Data Use • How to identify a member? • What data source to access? • Which tools to manipulate data? • Where to publish the output? • What type of documentation/training is made available? • What is the frequency of updates? • How are users notified of updates? 10
11. Stakeholders in a Reporting/Analytics Cycle Teamwork and Collaboration Customer - Clearly-defined Ask/Problem System - Valid, good-quality data Support – IT infrastructure Expertise – Domain knowledge Tools – Decision- support, portals Skills – Business Intelligence 11
12. Data Visualization/Analysis Options Build (In-house technical and domain expertise and experience) Consult (Good domain knowledge but limited technical skills) Buy (Minimal technical skills and/or domain knowledge) 12
13. Types of Reports Reports Function Regulatory Operational Financial Nature Descriptive Analytical Audit Time Period Real-time Routine Ad-hoc End-User Internal (Enterprise) Internal (Focused) External 13
14. Presenting Data – Common Components • Dashboard • Summary • Event details • Drill-down • Charts • Interactive • Geo-mapping • Statistics • Modeling/Predictive Note: Not all reports need to incorporate all above features 14
15. Before building a report! Know the customer – motivation, needs, skills 1 Know the data – quality, source, exceptions 2 Know the user interface – computing power 3 Know the business (or borrow!) – functional knowledge 4 Know the barriers – infrastructure, tools, data 5 15
16. https://itcareercentral.com/it-data-analyst-job-description-all-you-need-to-know/ Skillset for Healthcare Analytics 16
17. Type of Analytics and Skillset • SQL/Visualization/Healthcare needed at descriptive level • If you want predictive and prescriptive, you need staff with the right skillsets and experience • Skillsets vary depending on type of Analytics 17
18. Have one whether you are starting new, upgrading or leveraging a software product Build the right team In house vs cloud solution Strategy Wide range in Healthcare What one person finds useful another may not Know your audience (execs vs managers vs end user) Clearly identify purpose and question/s being answered Audience Complexity vs Usefulness vs Performance Optimizing the tool capabilities Maintenance of reports Near real time vs scheduled Balance Understand your software Legacy to new implementation Vendor support – troubleshooting, training Software Capabilities Empower users Minimal assistance from IT Governance – data, tool Self Service Areas of Focus for Successful Analytics 18
19. 19 Analytics Tool Selection • Ability for non-technical user to create a report by drag/drop • Interactive/Dashboards and data exploration • Predictive analytics - integration of statistical packages • Easy integration/interfacing with other tools • Architecture/Scalability • Visionary, innovative vendor
20. Altruista Health and Analytics Why did we choose Tableau? Market leader Interactive, visual based exploration of data Innovative company Quick insights Widely used in healthcare – quick adoption How is reporting and analytics improving outcomes? Combining data sources/sets Visuals for meaningful insights Self service tool Near real time reporting as needed 20
21. How does Altruista Health support our clients? Standardized Reports Regulatory Key operational reports for wide range of audience Customizable Expertise on data Prebuilt data sets/sources for clients to use Clinical SME’s, data modeling, Tableau reporting Multiple data sources used – CM/UM/A&G/Claims Client has access to their data Training Targeted training based on user role Training on data and/or analytics tool Hands-on training Knowledge Bank Knowledge gained with wide range of organizations Dedicated staff Share best practices 21
22. Industry Insights 22
23. Key Takeaways • Have a well-defined data and analytics strategy • Bring together knowledge and technical expertise • Establish enterprise-wide policies on data use • Do your homework before investing in an analytics solution • Build strong working relationships with stakeholders and vendors • Teamwork and collaboration 23
24. 24 Q&A
25. altruistahealth.com | 703.707.8890 Thank you! For more information contact us at hello@altrusitahealth.com