Patient’s Journey using Real World Data and its Advanced Analytics
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The document discusses the significance of real-world data (RWD) in understanding patient journeys and improving drug development and commercial strategies. It outlines the differences between RWD and clinical trial data, examples of insights gained from RWD, and the technological requirements for managing and analyzing this data. Challenges such as data quality and integration are also highlighted, emphasizing the need for investments in technology like cloud computing and machine learning to harness RWD's potential.
Patient’s Journey using Real World Data and its Advanced Analytics
- 1. Patient’s Journey using RealWorld Data and itsAdvancedAnalytics Kevin Lee
- 2. Disclaimer The views and opinions presented here represent those of the speaker and should not be considered to represent any companies or organizations.
- 3. • What is RWD? • Why is it important? • Patient’s Journey using RWD • Examples of Insights from RWD • Challenges in RWD • Technology requirements of RWD • Conclusion
- 4. 4 What is Real Word Data (RWD)? Data collected outside of Clinical Trial Study Real World Data Clinical Trial Data
- 5. 5 The Examples of Real Word Data (RWD) RWD Electronic Health Records (EHR) (e.g., Optum Market Clarity Data) Claim Activities (e.g., IQVIA LAAD, NPA, Xponent, LRx) Billing Activities (e.g., Insurance Payer data) Patient Registry
- 6. 6 Real World Data vs Clinical Trial Data Real World Data Clinical Trial Data Population All the population Population of Eligible Criteria Patients Number ~100 million 300 to 3,000 volunteers in Phase 3 Bias Various bias Minimized bias Environment Real World Controlled Visit Any Visit Scheduled Visit Purpose Effectiveness Efficacy Treatment Schedule As needed Fixed Comparison All the drugs Placebo / Control Patient Monitor As needed Continuous during Clinical Trial Data Quality As they come Cleaned Data Collection Various sources (e.g., EHR, Claims) EDC per protocol Data Format Any format SAS dataset format Data Structure Any data structure Tabular data structure
- 7. 7 Why is RWD important? • More comprehensive, holistic patient journey history • More Personalized medicine • Safety and Efficacy findings in bigger population • More complete pictures on how medicines are used in the real world rather than controlled environment in Clinical Trial • Leading to better clinical trial design (e.g., Patient recruitment, other indication ) • Helping drug companies to manage product life cycle (pre-launch, post- launch, during patent, after patent) • Helping drug companies for marketing and sales strategies
- 8. 8 Patient 1001 got AC1 LAB test on 2020- 01-15. Patient 1001 started “Metformin ” on 2020- 01-25. Patient 1001 was diagnosed with ‘prediabetes’ on 2020-01-22. Patient 1001 got AC1 LAB test on 2021- 01-10. Patient 1001 was diagnosed with ‘Diabetes, Type 2’ on 2021-01-20. Patient 1001 switched to “Jardiance 10 mg” on 2021-01-23. Patient 1001 got AC1 LAB test on 2021- 06-14. Patient 1001 switched to “Jardiance 25 mg” on 2021-06-20. Patient 1001 visited his family doctor. Patient 1001 used “Aetna” insurance Patient 1001 complained about “Dizziness”. Patient 1001 got AC1 lab result at 6.2% on 2020-01-21 Patient 1001 got AC1 lab result at 7.1% on 2021-01-18 Patient 1001 got AC1 lab result at 8.0% on 2021-06-18 Patient 1001 got AC1 LAB test on 2022- 01-14. Patient 1001 got AC1 lab result at 7.0% on 2022-01-18 Patient’s Journey in RWD
- 9. 9 Data in RWD Patient’s Journey Real World Data (RWD) Patient Demograp hics Diagnosis Procedures Claims Healthcare Provider Insurance Payers AE LAB Vital • EHR • Patient’s Medical Activity based • Demo, Vitals, Lab, AE, provider’s notes • Claims • Patient’s Claim Activity based • Prescriptions, Diagnosis, Procedures • Source • Providers • Payers
- 10. 10 Examples of insights from RWD (1) Clinical Trial Optimization • Originally, Jardiance is approved for Type 2 Diabetes treatment, but in RWD, it has been prescribed for pre-diabetes patients. • Using RWD, Statisticians / programmers has found Jardiance show positive effectiveness with low safety issues. • Using RWD, Statisticians / programmers could compare effectiveness compared to competitors. • Using RWD, Statisticians / programmers could find safety information of medicines. • Using RWD, Patients recruitments are more effective.
- 11. 11 Sales Performance Metrics • Geographic Sales Performance • Sales Performance vs Forecasts • Sales Comparison with Competitors ( e.g., 50% market share for Jardiance) • Sales Performance by quarters or month • Source of Business • New • Switch • Continued Examples of insights from RWD (2)
- 12. 12 Sales Performance Metrics GeographicSales Performance Sales Performance vs Forecasts Sales Comparison with Competitors Sales Performance by quarters Source of Business New Switch Continued Examples of insights from RWD (3) Treatment Effectiveness / Personalized Medicine • 1st Line vs 2nd Line • Combo and Mono Therapy • Patient Profile – Age, Gender, Races, Diagnosis
- 13. 13 Challenge in RWD • Quality of data • As they come, not cleaned / validated • High possibility of Bias • Various data sources (e.g., Veeva CRM, IQVIA Claim, Hospital Data) • Unstructured / Unstandardized Data • Big Data • Data integration / ETL • System Infrastructure for RWD – Data Storage, Data Processing, Analysis • Return of Investment • Usage in Clinical Trials • Regulatory restriction
- 15. RWD is Big Data • It contains up to 5 years of ~100 million patient’s records per TA. • ~1 trillion claims for Diagnosis, Procedure & Prescription. • Typical Analysis for RWD contain up to ~1 trillion records. • Tools : Cloud Computing
- 16. RWD in Cloud Computing • Big Data in RWD requires much bigger computing power, which fits well with Cloud Computing. • Flexible and scalable Cloud Computing environment will serve RWD Advanced Analytics (e.g. AI, prediction, Data Visualization) well. • An easy access for the reports and analysis thru internet connection. • A great collaboration environment / platform for research and analysis. • Tools : AWS, Azure, Databricks
- 17. RWD in Central Database / Data Warehouse 17 • Tool to store, manage and govern RWD Data • Security and Data Quality for Sensitive health care data • Data Share and Access to different stakeholders • Centralized data storage • Tool : AWS Redshift, Snowflake, Oracle, Hive
- 18. RWD in Open-Source Programming • RWD is not a regulatory required data. • Open-source programming for advanced analytics (e.g. Machine Learning, Data Visualization) • More and more organizations are expanding to open- source programming for RWD analysis for operating cost and advanced analysis. • Tool : R, Python, SQL, R-Shiny
- 19. RWD in Data Visualization • Monthly and weekly performance metrics of the products in market • Interactive dashboard implementation by filters and graphs • Patients Journey from Diagnosis, EHR, to Prescriptions • Tool : Tableau, Power BI 19
- 20. RWD in Machine Learning/ Prediction 20 • Predicting the treatment effectiveness • Clinical Trial Optimization on Study Design, Patients recruitments • Drug Discovery on identifying potential drug targets • Predicting personalized Medicine for patients • Structured data from unstructured data ( e.g., Insights from doctors’ notes) • Tools : Jupyter, R Studio, Databricks, Machine Learning, Deep Learning, Natural Language Procession, Convolutional Neural Network
- 21. 21 Conclusion • Real World Data could provide valuable medical and business(commercial) insight and understanding in Real World Situation. • RWD could bring comprehensive, holistic real world patient’s journey history (e.g., up to 5 years or more ), leading to Personalized Medicine. • RWD could support drug pipelines (e.g., clinical trials) and commercial drug management (e.g., marketing and sales). • More and more RWD will be integrated with Biometric function. • RWD need technology investment (e.g., Big Data, Cloud Computing, Open- Source, Machine Learning) to fully utilize its potential and benefits.
- 22. Any Questions?