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Big Data Trends in Pharma for Utilization Across Medical Affairs

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Despite the growing awareness about the importance of embracing Big Data, the bio-pharmaceutical sector has been slower than other industries to utilize Big Data due to the inherent costs and challenges that come with utilization. On the medical side, organizations have begun looking to Big Data as a way to improve everything from clinical trials to adherence. Recognizing the huge potential that Big Data holds for key medical decisions, organizations are taking steps to develop greater Big Data capabilities.

Best Practices, LLC undertook this study to probe current & future trends and best practices for Big Data utilization across Medical Affairs functions. The study will help leaders to better understand the growing influence of Big Data in the bio-pharmaceutical sector and how it impacts medical, HEOR, and commercial operations in the U.S.

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Big Data Trends in Pharma for Utilization Across Medical Affairs

  1. 1. Big Data in Pharma: Current & Future Trends for Big Data Utilization Across Medical Affairs Functions Best Practices, LLC Strategic Benchmarking Research Study with Segmented Responses by Medical Affairs Functions
  2. 2. 2 Table of Contents I. Executive Summary pp. 3-8  Research Overview pp. 4  Universe of Learning pp. 5-6  Big Data Team Overview and Key Study Insights pp. 7-8  Quantitative Key Findings pp. 9-12 II. Defining Big Data pp. 13-20 III. Data Types and Sources pp. 21-26 IV. Data Producers, Dissemination & Requestors pp. 27-31 V. Centralization pp. 32-34 VI. Governance and Leadership pp. 35-51 VII. About Best Practices, LLC pp. 52
  3. 3. Best Practices, LLC, conducted a customized study – with responses segmented by medical, commercial and HEOR functions - to better understand the growing influence of Big Data in the biopharmaceutical sector and how it impacts medical, HEOR, and commercial operations in the U.S.  Best Practices, LLC engaged 12 leaders from 11 pharmaceutical companies through a benchmarking survey in regards to Big Data usage in Medical Affairs.  Research analysts also conducted seven deep-dive executive interviews with selected benchmark participants. Research Goal Research Methodology  Produce reliable industry metrics on current and future trends for Big Data utilization across medical, commercial and HEOR groups. Topics Covered  Types of Big Data Projects Used to Support Medical, Commercial and HEOR Decisions  Big Data Capabilities and Governance  Types and Value of Data Used for Big Data Projects  Big Data Staffing and Budget Levels  Value Rating of Partnerships on Big Data Projects  Policies and Procedures Governing Big Data Activities  Investigate data types, data partnerships, and staffing/budget levels companies are using as they move to a more analytically based approach to commercial, HEOR & medical decisions. Research Overview Research Project Objectives & Methodology
  4. 4. Benchmark Class: Eleven Companies Participated in the Benchmark Study Best Practices, LLC engaged 12 leaders from 11 pharmaceutical companies through a benchmarking survey in regards to Big Data usage in Medical Affairs.
  5. 5. Copyright © Best Practices, LLC 5 Quantitative Findings: Big Data Types and Utilization Across the medical segment, the following key Big Data types, sources and utilization were observed in this study. Medical segment participants said post-launch studies around product use and health outcomes were the most common types of Big Data projects they conducted. Post-Launch and Customer Segmentation Studies Most Common Big Data Projects Payers & Data Aggregators Cited as Best Partners by Majority of participants A majority of participants said that partnerships with payers and data aggregators were highly impactful. Health systems were also seen as valuable partners by each of the three segments. Only 5 of 36 Types of Data Rated as Highly Valuable Across The Study Only 5 of 36 types of Transactional, Reported, Online, Scientific and Machine- Generated data were rated highly valuable by a majority of study participants in Medical Affairs groups. They were: Claims; EHR (Electronic Health Records); Health Outcomes (provider/payer reported); Real World Studies; and Registries. No types of Online or Machine-Generated data were rated highly valuable by a majority of participants
  6. 6. 6 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Point of Sale (POS) Claims Electronic Health Records (EHR) / Electronic Medical Records (EMR) / HIE (Health Information Exchanges) ePrescription / pharmacy fulfillment Wholesalers / Group Purchasing Organizations (GPOs) Government (e.g., cost data) Credit card Impact of Transactional Data (Medical) Highly impactful Somewhat impactful Not impactful Not used Transactional Data: Medical Segment Rates Claims, EMR Most Valuable A majority of participants responding from a Medical perspective rated claims and Electronic Medical Records (EMR) as highly impactful or valuable types of transactional data for Big Data studies. N=12 Q: How impactful (or valuable) has each of the following types of transactional data sources proven to be?
  7. 7. 7 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Impact of Data Producers (Medical) Highly impactful Somewhat impactful Not impactful Not used Medical: Many Find Range of Data Producers Useful Medical teams broadly find more value across data sources compared to analytics in other functions. Government agencies and commercial payers are clear leaders, likely due to their growing influence on formularies and the need to understand their value calculus. N=12 Q: How impactful (valuable) is each of the following types of data producers?
  8. 8. 8 Medical Leaders Describe Analytics As Centralized Medical leaders were the most likely to describe their analytics efforts as having centralized or dedicated personnel. N=12 Q: Do you have a centralized/ dedicated group of individuals to support Big Data projects? Yes 58% No 42% Dedicated Big Data Team (Medical)
  9. 9. 9 0% 10% 20% 30% 40% 50% 60% 70% North America EU Europe (nonEU) Asia Big Data Capabilities and Governance by Region (Medical) Regions with Big Data capabilities Region where governance resides Medical Leaders See Smallest Governance Gaps Our benchmark class reported that their Big Data capabilities and governance are generally based in North America and the EU. This finding varies little between functions, although medical leaders were more likely to report capabilities and governance residing in the EU than commercial leaders. N=12 Q: Please indicate the regions below where your organization has Big Data capabilities, and where Big Data governance resides.
  10. 10. 10 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Partnership Value (Medical) Highly impactful Somewhat impactful Not impactful Not used Medical: Health Plans/Payers, Aggregators and Health Systems Seen as Highly Impactful Partners Medical leaders were most likely to rate partnerships as highly valuable across all varieties of collaborations. Most popular were collaborations with payers and data aggregators. N=12 Q: Which of the following partners are most impactful/ valuable on Big Data programs and projects?
  11. 11. Copyright © Best Practices, LLC 11 Best Practices, LLC is a research and consulting firm that conducts work based on the simple yet profound principle that organizations can chart a course to superior economic performance by studying the best business practices, operating tactics, and winning strategies of world-class companies. Best Practices, LLC 6350 Quadrangle Drive, Suite 200 Chapel Hill, NC 27517 www.best-in-class.com About Best Practices, LLC

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