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実環境データとその医療アプリケーションに関する調査

Real World Data and its Healthcare Applications Report

発行 EyeforPharma 商品コード 273972
出版日 ページ情報 英文 57 Pages; 17 Tables & Figures
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実環境データとその医療アプリケーションに関する調査 Real World Data and its Healthcare Applications Report
出版日: 2013年03月04日 ページ情報: 英文 57 Pages; 17 Tables & Figures
概要

当レポートでは、実環境データ(RWD)関連の主な課題、機会、および破壊的な影響とそれらが製薬ビジネスモデルに及ぼす影響を調査しており、欧州および北米を拠点とするディシジョンメーカーへの詳細なインタビューを含め、概略以下の構成でお届けいたします。

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調査手法

第1章 実環境データ(RWD)のソース

  • データベース
  • 調査データ
  • 患者報告結果
    • ケーススタディ:PatientsLikeMe
  • 登録

第2章 提携−競争の協調性

  • 潜在的パートナー
  • 理想的な提携

第3章 データ規制・品質管理

  • データ所有関連の政策・規制
  • データ収集の品質保証方法に関する業界の見解

第4章 RWDはどのように利用されているか?

  • コストメリットを特定するため、直接比較試験の実行にRWDを使う
  • 薬剤有効性・薬効評価の決定にRWDを使う
  • 医療費支払い者のRWD研究の利用
  • RWD利用の背後にある手法

第5章 医療費支払い者のニーズとそのRWD利用

第6章 RWDの利用増加から業界が得たもの

業界の教訓

略語

参考資料

付録A

図表

目次

The best sources of Real World Data are discussed in detail with examples of how they are currently being used.

Industry Insights

37 detailed interviews across all stakeholders have been collated to produce the exclusive content contained in this report.

Partnerships

Who is best to partner with is discussed with case study examples for you to learn from.

Industry overview

Developing business in the pharmaceutical industry has rarely been more difficult than in recent years. High drug attrition rates and the stagnation of developed economies have caused chronic growth issues to pharmaceutical companies; these, coupled with pressure from payors who are becoming increasingly focused on price, have made for a difficult environment.

Satisfying payor needs and ensuring treatments are suitably reimbursed is a key challenge - what Real World Data stand to do is allow payors to better understand the outcomes of various treatments and only pay for those which are most beneficial to society through conditional reimbursement. Whilst conditional reimbursement is risky for pharmaceutical companies, it will help improve openness and reward innovation, rather than iterative developments - which have become all too common.

The benefits of Real World Data are that they will help integrate stakeholders allowing each to better utilising these data; whilst certain professions will argue they have been using data from the real world for many years, it is only now that the industry is beginning to harmonise and appreciate how individuals in different roles can work together and make use of this additional information - without question developments in technology have played a huge role in this.

It is clear from the research conducted that industry professionals (of all levels) are becoming focussed on Real World Data, with over 70% of those surveyed expecting their time spent working with RWD to increase over the next 2 years. This anticipation of change is what has really driven the publication of this report which aims to raise industry knowledge across areas which have been identified by the most challenging by those within the industry.

The report provides a comprehensive assessment of the key challenges, opportunities, and disruptive influences associated with real world data and how these are affecting the pharmaceutical business model. Key opinion leaders from the US and Europe have been interviewed in depth to provide these exclusive insights.

Highlights and Key questions answered

  • What are the best sources of Real World Data and why?
  • Examples of partnerships and how these were formed (what did each stakeholder gain from this?)
  • Understand the different types of trial which have used real world data and why?
  • Turning quantitative data into qualitative data to predict a realistic measure of true health outcomes.
  • Understanding how to use real world data and how these compliment RCTs.
  • Case studies and insights into how companies are using Real World Data.
  • Exclusive details and insights into GSKs Salford Lung Study
  • How to use Real World Data to satisfy payor needs?
  • Regulations affecting Real World Data usage in the EU and US

Industry Reviews

Additional to those who were interviewed to build content for this report, we would also like to extend our sincere thanks to the following for reviewing and offering their opinion on the report. This has been invaluable, as it has helped shape the direction allowing us to expand on certain areas.

Methodology

The eyeforpharma Real World Data (RWD) Report, 2013, was based on 4 months' research involving 34 in-depth interviews with decision makers based in Europe and North America, an exclusive survey on RWD and detailed secondary research on papers published within the past 2 years, as well as those which were referred to by interviewees.

Figure 3: Distribution of job function in initial interviews

Table 3: Distribution of job function in initial interviews

JobDistrbution
Pharma11
Consultant3
Vendor2
Payer1
Academic3
HCP1

Source: eyeforpharma survey (n=68), 2012

In order to get an accurate feel for RWD within the pharmaceutical industry we initially conducted 21 interviews with industry leaders. The interviews were used to understand current market trends and helped capture qualitative information by using open-ended questions. Given how new this topic is to many people, we focused on the key challenges and needs going forward to understand what topics the report needed to focus on in order to add value.

Figure 4: Most valuable data analytics services and partnerships

Table 4: Most valuable data analytics services and partnerships

Data analytic service/partnership%
Solution providers / Consultants59.1
Academics18.2
Pharmaceutical Companies7.6
Regulators3
Payors / Providers12.1

Source: eyeforpharma survey (n=65), 2012

Initial interviewees were selected based on their previous involvement with eyeforpharma - all interviewees were based in Europe and North America: 11 were from pharmaceutical companies, 3 were consultants, 2 vendors, 1 payor, 3 academics and 1 was a healthcare professional.

Figure 5: Primary job function

Table 5: Primary job function

Job%
Clinical/R&D12.5%
Data management6.3
Health Economics6.3
Health Outcomes10.9
Marketing/Sales20.3
Market Access17.2
Top-level Management26.6

Source: eyeforpharma survey (n=75), 2012

Having established there was a need for business intelligence relating to RWD, various stakeholders were identified as holding valuable insights (figure 3):

  • Pharma
  • Payors
  • Academics
  • Regulators
  • Consultants/solution providers

Figure 6: ‘Businesses’ primary function'

Table 6: ‘Businesses’ primary function'

Organisation's primary business%
Pharma / Biotech Company36.2
Consultant / Vendor / Supplier50.7
Academic / Researcher4.3
HTA / Regulator / Insurer2.9
Health Professional5.8

Source: eyeforpharma survey (n=75), 2012

A survey was sent out to a targeted section of our database (pharmacists, clinical trials managers, payors, marketing directors, R&D, and medical directors from across the globe) (figure 5). Of these, there was a fairly even distribution of primary job functions, with the majority of those completing the survey coming from top level management (figure 6).

Surveys were distributed electronically in December 2012 and the questions asked were based on:

  • The 21 preliminary interviews with pharmaceutical professionals regarding their challenges and needs surrounding RWD
  • Published material on RWD
  • Participant requests from eyeforpharma conferences

Having conducted the initial interviews, a survey, a review of additional datasets, literature and conference proceedings pertaining to RWD usage, a decision was made to conduct a further 13 interviews with key figures who have been involved in the development of RWD in the pharmaceutical industry (from the main stakeholders), most of whom are acknowledged in this report.

The additional interviews were very focused and structured around questions which had been identified as business-critical in both the initial interviews and surveys. Interviewees were asked to openly share their thoughts, ideas and personal experiences surrounding the questions asked (see appendix A), and, where possible, were asked to provide case studies to help contextualize a particular subject.

Once the information had been collected it was integrated to uncover the industry trends which are discussed and presented in this report. Key findings have been presented in the form of chapters and case studies and, where relevant, contributors are quoted with examples given in their particular case.

Finally, the report was sent out to 3 peer reviewers who were independent of the project to receive their unbiased opinion on what had been produced.

Table of Contents

  • Welcome
  • Industry reviews
  • About eyeforpharma
  • Acknowledgments
  • Index of figures and tables
  • Executive summary
  • Introduction
    • Real World Data defined
    • Pros and cons of RWD
    • Using RWD in like-for-like trials
    • How RWD use is changing in the healthcare industry
    • Factors driving rise in use of RWD
    • Gaps in knowledge of RWD
  • Methodology
    • 1. Sources of RWD
      • 1.1. Databases
      • 1.2. Observational data
      • 1.3. Patient reported outcomes
        • Case study: PatientsLikeMe
      • 1.4. Registries
    • 2. Partnerships-Competitive cooperativeness
      • 2.1. Potential partners
      • 2.2. Ideal partnerships
    • 3. Data regulations and quality control
      • 3.1. Policies and regulations surrounding data ownership
      • 3.2. Industries thoughts on how to ensure quality of data collection
    • 4. How are RWD being used?
      • 4.1. Using RWD to perform head-to-head trials to identify cost benefits
        • Case study: Cost-benefit of RWD
      • 4.2. Using RWD to determine drug effectiveness and efficacy
        • Case study: Using RWD to assess risk
        • 4.2.1. Information relating to Salford Lung Study
      • 4.3. Payors' use of RWD studies
      • 4.4. Methodologies behind RWD usage
    • 5. Payors' needs and their use of RWD
    • 6. What the industry has to gain from increased use of RWD
    • Industry learning
    • Abbreviations
    • References
    • Appendix A

List of Figures and Tables

  • Figure 1: Time spent working with RWD
  • Figure 2: Expected increase in time spent working with RWD over the next 2 years
  • Figure 3: Distribution of job function in initial interviews
  • Figure 4: Most valuable data analytics services and partnerships
  • Figure 5: Primary job function
  • Figure 6: Businesses' primary function
  • Figure 7: Best sources of RWD
  • Figure 8: Gaps in knowledge
  • Table 1: Impact of RWD on the healthcare system
  • Table 2: Time spent working with RWD
  • Table 3: Distribution of job functions in initial interviews
  • Table 4: Most valuable data analytics services and partnerships
  • Table 5: Primary job function
  • Table 6: Businesses' primary function'
  • Table 7: Best sources of RWD
  • Table 8: Gaps in knowledge
  • Table 9: Most valuable data analytics services and partnerships
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