REAL-WORLD DATA (RWD) MARKET ANALYSIS

Market Challenges And Opportunities

Global Real World Data (RWD) Market Drivers

• Increasing Adoption of Electronic Health Records (EHRs): The widespread adoption of electronic health records by healthcare providers has led to the accumulation of vast amounts of patient data. This data, when aggregated and anonymized, can provide valuable insights into real-world patient outcomes, treatment patterns, and disease progression. For instance, on January 16, 2023, Fujitsu, the information and communications technology equipment and services corporation, and Sapporo Medical University announced the launch of a joint project to realize data portability for patients’ healthcare data, including electronic health records (EHRs) (2) and personal health records (PHRs).
• Growing Demand for Evidence-Based Medicine: There is a growing emphasis on evidence-based medicine, which relies on real-world evidence (RWE) derived from RWD. Stakeholders in healthcare, including regulators, payers, and healthcare providers, are recognizing the importance of using real-world data to make informed decisions about treatment effectiveness, comparative effectiveness, safety, and cost-effectiveness. For instance, on January 16, 2023, according to an article published in the Nature Medicine journal, advances in wearable technologies, data science, and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation ‘deep’ medicine. Moreover, bringing a drug successfully through all phases of drug development into the clinic costs more than 5–2.5 billion dollars. Therefore, innovative strategies are needed to engage patients and generate the necessary evidence to propel new advances.
• Regulatory Support and Guidance: Regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have recognized the value of real-world evidence in regulatory decision-making. They have provided guidelines and frameworks for the use of RWD and real world evidence (RWE) in regulatory submissions, drug approvals, and post-market surveillance, further driving the demand for real-world data.
• Advancements in Data Analytics and Technologies: The advancement of data analytics tools and technologies, such as artificial intelligence (AI), machine learning (ML), and big data analytics, has facilitated the extraction of meaningful insights from large and complex real-world datasets. These technologies enable the analysis of RWD to identify treatment patterns, patient subgroups, adverse events, and other clinically relevant information.

Global Real World Data (RWD) Market Opportunities

• Drug Development and Clinical Trials: Real-world data can be leveraged to optimize the drug development process and enhance clinical trial design. RWD can be used to identify target populations, understand disease progression, assess treatment effectiveness, and identify potential safety concerns. By integrating real-world evidence into clinical trials, researchers can enhance trial efficiency, recruit appropriate patients, and generate robust outcomes. For instance, in June 2022, AbbVie Inc., a pharmaceutical company, announced the launch of AbbVie Research Collaborative, a health data platform that enables adults anywhere in the U.S. to partner with AbbVie’s world-class scientific researchers. Collaborative participants contribute their real-world health data, such as genetic, lifestyle, and exercise information, medical records, and more, to provide a more comprehensive picture of their health. Real-world health data empowers researchers to find health and disease patterns they may not see within a clinical trials.
• Comparative Effectiveness Research: Real-world data enables comparative effectiveness research, which focuses on comparing the benefits and risks of different treatment options in real-world settings. RWD allows for the assessment of treatment effectiveness, safety, and patient outcomes across diverse patient populations, thereby providing valuable insights for healthcare decision-making. For instance, in August 2020, the Medical Device Innovation Consortium (MDIC) announced the launch of its real world evidence (RWE) Framework for regulatory decision-making for in vitro diagnostics.
• Market Access and Reimbursement: Real-world evidence derived from RWD can be used to demonstrate the value and cost-effectiveness of drugs and medical devices to payers and health technology assessment agencies. This evidence can support market access and reimbursement negotiations, facilitating the inclusion of innovative therapies in healthcare coverage.
• Pharmacovigilance and Drug Safety Monitoring: Real-world data plays a crucial role in post-marketing surveillance and ongoing drug safety monitoring. RWD can help identify and monitor adverse events, drug interactions, and the long-term safety profiles of medications. This data can aid regulatory agencies, pharmaceutical companies, and healthcare providers in making informed decisions regarding drug safety and risk mitigation.

Global Real World Data (RWD) Market Restraints

• Data Quality and Completeness: Ensuring the quality, completeness, and reliability of real-world data remains a challenge. RWD is often collected from disparate sources with varying levels of data quality, standardization, and completeness. Inconsistent data collection practices, missing data elements, and data entry errors can impact the reliability and validity of the insights derived from RWD.
• Data Privacy and Regulatory Compliance: The use of real-world data raises concerns about patient privacy and data protection. Regulatory requirements, such as GDPR and HIPAA, impose strict rules on the collection, storage, and use of patient data. Compliance with these regulations adds complexity and cost to RWD initiatives, requiring robust data de-identification and security measures to protect patient privacy.
• Data Fragmentation and Interoperability: Real-world data is often fragmented across various healthcare systems, electronic health record platforms, and research databases. Lack of interoperability and data standardization hinder the seamless integration and analysis of RWD. The need to overcome technical and semantic barriers to data sharing and aggregation poses challenges to deriving meaningful insights from disparate data sources
• Selection Bias and Generalizability: RWD may suffer from selection bias, as it is typically derived from specific patient populations, healthcare settings, or regions. This may limit the generalizability of the findings to broader populations. It is crucial to consider the representativeness of the data and account for potential biases when interpreting and applying insights from RWD.