Pharmaceutical and Life Sciences Real World Evidence Market Trends

Market Driver - Increasing Adoption of Real-World Evidence in Regulatory Decisions

The use of real-world evidence in regulatory decision-making is being increasingly adopted. Regulators around the world are showing more openness to using real world data generated outside of traditional clinical trials. They recognize that real world evidence can help address some key limitations of randomized controlled trials like limited sample sizes, restricted patient populations and controlled environments. It provides a more pragmatic view of how medical products and interventions perform during routine clinical use.

In the United States, the FDA has published several guidance in the past few years clarifying their view on using real world evidence for regulatory purposes. This includes guidance on using real world data to support labeling changes and drug approval. The FDA sees real world evidence as complementing other sources of evidence like randomized trials. It believes real world data when generated using robust methodologies can help support various decisions throughout the product lifecycle including new target identification, safety surveillance and clinical use. In the EU, regulators have also acknowledged real world evidence can potentially support marketing authorization under exceptional circumstances when clinical trial data is difficult to obtain.

The growing adoption by regulators stems from recognition that real world evidence studies can address certain gaps and limitations of traditional clinical research methods. It provides insights into treatment patterns, adverse events, effectiveness and other outcomes during routine medical practice.

Market Driver - Rising Healthcare Expenditure on Real World Data Analysis

Healthcare costs continue to rise significantly across both developed and developing countries. This exerts tremendous financial pressure on governments and private insurers to curb spending and optimize available resources. At the same time, there is a growing push for more evidence-based healthcare practices and performance benchmarking of various treatment options. This has led to greater focus on health technology assessment and analyzing real world performance or medical interventions, drugs and devices.

Healthcare payers and insurers are showing increased interest in real world evidence studies to evaluate the value and economic outcomes of various treatments. Real world data generated during routine clinical practice provides insights into effectiveness, safety, quality of life outcomes and economic impacts like costs of associated hospitalizations, Lost work productivity etc. during naturalistic use. Such data helps payers and insurers make more informed decisions about formulary inclusion, reimbursement rates and covered benefits for different treatment options. It allows them to negotiate effectively with life sciences companies and ensure value for money for funded healthcare services.

Given rising cost of healthcare, governments and private insurers also want improved cost-effectiveness and performance benchmarking across healthcare providers. Real world evidence analysis allows monitoring of cost and quality metrics during routine operations. It helps identify unwarranted variations, evaluate different care delivery models, and helps scale up more efficient practices. This in turn supports performance-driven reimbursement and more outcomes-based healthcare funding. Overall, to curb rising costs and optimize resource allocation, real world data analysis is expected to see increasing expenditure support from governments, private insurers and healthcare payers.

Market Challenge - High cost associated with data collection and analysis

One of the major challenges faced by the pharmaceutical and life sciences sector regarding real world evidence is the high cost associated with data collection and analysis. Gathering real world data from electronic health records, claims databases, registries and other sources is an expensive process as it requires building the necessary infrastructure and partnerships to access these datasets. It also involves overcoming various regulatory and privacy hurdles regarding the usage of patient health information. Additionally, analyzing the vast amounts of real-world data gathered from multiple sources in an effective manner requires heavy investments in data management and analytics tools, as well as hiring skilled data scientists and researchers to derive meaningful insights. Linking disparate data sources across different organizations and geographies further adds to the complexity and expense of real-world data collection and analysis for the life sciences industry. The costs incurred do not always guarantee successful outcomes from real world evidence studies, making return on investment difficult to ascertain for pharmaceutical companies. Overall, the expenditure required to generate real world evidence from real world patient data poses significant budget constraints on life sciences organizations, especially smaller to mid-sized firms.

Market Opportunity: Growing Use of Artificial Intelligence and Machine Learning in Data Analysis

One major opportunity for the pharmaceutical and life sciences real world evidence market lies in the growing application of artificial intelligence and machine learning techniques for data analysis. As real-world datasets continue expanding in size and complexity, traditional statistical methods are reaching their limitations in effectively studying these massive real-world databases. Advanced technologies like deep learning, natural language processing and predictive analytics offer novel ways to extract valuable insights from the sea of unstructured, multi-dimensional patient information. AI capabilities like automated pattern recognition, segmentation and outcome prediction can help analyze real world data at faster speeds and larger scales than traditional human-driven approaches. This will enable deriving clinically relevant findings in more efficient, cost-effective ways to support drug development and outcomes research. As life sciences firms increasingly invest in AI to optimize their R&D pipelines, they are also exploring ways to leverage these technologies for real world evidence generation. The integration of AI stands to transform how real-world patient data is studied to accelerate medical progress.