AI-powered real-world evidence: Strategically enhancing value and access
- Real-world evidence (RWE) complements traditional randomized controlled trials by providing insights from diverse data sources, helping healthcare decision-makers with coverage, reimbursement, and treatment guidelines.
- Artificial intelligence (AI) and natural language processing (NLP) are pivotal in extracting insights from RWE, helping to address challenges like data quality and bias, and enabling more effective value and access (V&A) strategies.
- AI can improve the structuring and quality of real-world data, identifying biases and ensuring privacy, which enhances the efficacy of health technology assessments and regulatory approvals.
- AI-driven RWE supports targeted evidence generation and value propositions by identifying specific patient cohorts, addressing unmet medical needs, and informing clinical practice with comprehensive insights.
- Strategic partnerships with AI and RWE experts can help drug developers navigate the complexities of the AI landscape, ultimately enhancing value demonstration and patient access through innovative V&A strategies.
Real-world evidence (RWE) is a critical tool for understanding how medicines perform in diverse patient populations and real-world settings. However, the sheer volume and complexity of RWE data pose challenges for traditional analysis methods.
Artificial intelligence (AI) is revolutionizing the pharmaceutical industry and having a major impact on value and access (V&A). By harnessing AI to extract RWE insights, developers can efficiently demonstrate the value of medicines, broaden patient access and strategically navigate the complexities of market access.
In this blog, Envision Pharma Group leaders Suki Kandola, Global Head of Commercial Strategy, Value and Access and Yahya Anvar, General Manager and Head of Okra.ai explore the current challenges in leveraging RWE in V&A and how AI can extract insights to generate a comprehensive and robust evidence base that feeds into V&A strategies.
Informing healthcare decisions with real-world insights
Unlike traditional randomized controlled trials (RCTs), RWE captures data from diverse sources, such as electronic health records, insurance claims, patient registries and wearable devices. This wealth of information provides a more comprehensive picture of how treatments perform in real-world settings, taking into account a wider range of