Real-world data (RWD) is an umbrella term for different types of data that are not collected in conventional randomised controlled trials. RWD in the healthcare sector comes from various sources and includes patient data, data from clinicians, hospital data, data from payers and social data. There are already examples of ways in which research has contributed to the provision, construction and capture of RWD to improve health outcomes. However, to maximise the potential of these new pools of data in the healthcare sector, stakeholders need to identify pathways and processes which will allow them to efficiently access and use RWD in order to achieve better research outcomes and improved healthcare delivery. Current efforts to improve access to RWD and facilitate its use take place in a context of resource scarcity.Based on a literature review, case studies, a small set of interviews of experts from public and private organisations and a scenario based workshop, the study outlined possible strategies to illustrate how RWD standards development could facilitate RWD-based research. By investigating the current forms and uses of RWD in Europe, this study
has highlighted their significant potential for assessing the (short- or long-term) impact of different drugs or medical treatments and for informing and improving healthcare service delivery. Although the potential of RWD use seems quite clear, this research reveals barriers that restrict further development towards its full exploitation:
- the absence of common standards for defining the content and quality of RWD
- methodological barriers that may limit the potential benefits of RWD analysis
- governance issues underlying the absence of standards for collaboration between stakeholders
- privacy concerns and binding data protection legislation which can be seen to restrict access and use of data.
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Table 15 Improving access to and use of RWD: from barriers to enablers
Barriers
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Strategies to improve access and use
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Content and quality issues –
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Terminology issues
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Standardisation of codes
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Incomplete data
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Longitudinal data collection
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Data quality issues
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Processes for data quality assurance
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Methodological barriers
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Limited analytics capabilities
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National eHealth strategies
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Lack of analytical standards
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Transnational and multisectoral coalition of experts
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Linkage challenges European
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projects and best practices
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Fragmentation
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Interoperable systems
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Governance structures
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Lack of clear pathway
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Buying the data
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Access granted to academics only
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Hiring/partnering with academics
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Lack of data controller engagement
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Incentives for clinicians
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Privacy practices –
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Ethical concerns among professionals
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Trusted third party, depersonalisation tools
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Ethical concerns among the public
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Communication campaigns
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Consent management
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Liberal national strategies and innovative consent management tools
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