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Creating systematic reviews using Real World Evidence (RWE) data presents unique challenges due to the literature volume, heterogeneity and time-consuming nature. Unlike clinical trials that follow strict protocols, RWE is derived from diverse sources, with multiple methodologies, outcomes, and publication approaches. This heterogeneity makes standardizing the data and comparing the results across studies challenging. Additionally, the time-consuming nature of RWE extraction and analysis makes conducting systematic reviews from RWE a demanding and resource-intensive process. Artificial intelligence (AI) and natural language processing (NLP) may be utilized to improve the quality and comprehensiveness of the literature review process, and deliver impactful operational and commercial benefits to stakeholders engaged in product value demonstration and assessment. It is imperative that Publication Planners and other stakeholders are current on approaches to ensure the quality and reliability of the evidence-based recommendations that systematic reviews provide in informing clinical decision-making.

Systematic literature reviews (SLRs) are critical in providing evidence-based recommendations for decision making in healthcare. Publication Planners and other stakeholders play a critical role in ensuring the quality and reliability of evidence-based recommendations that SLRs provide for clinical decision-making. However, traditional SLRs can be time-consuming, costly, and challenging to conduct. Real-world evidence (RWE) has emerged as an important source of evidence, but its use presents unique challenges, including heterogeneity, lack of standardization and time-consuming nature. In this session, we will discuss the challenges of creating systematic reviews from RWE and explore how AI/NLP can be used to create compliant and fast systematic literature reviews, given its screening automation and streamlining of the data extraction capabilities.

Description

Creating systematic reviews using Real World Evidence (RWE) data presents unique challenges due to the literature volume, heterogeneity and time-consuming nature. Unlike clinical trials that follow strict protocols, RWE is derived from diverse sources, with multiple methodologies, outcomes, and publication approaches. This heterogeneity makes standardizing the data and comparing the results across studies challenging.

Additionally, the time-consuming nature of RWE extraction and analysis makes conducting systematic reviews from RWE a demanding and resource-intensive process. Artificial intelligence (AI) and natural language processing (NLP) may be utilized to improve the quality and comprehensiveness of the literature review process, and deliver impactful operational and commercial benefits to stakeholders engaged in product value demonstration and assessment. It is imperative that Publication Planners and other stakeholders are current on approaches to ensure the quality and reliability of the evidence-based recommendations that systematic reviews provide in informing clinical decision-making.

Systematic literature reviews (SLRs) are critical in providing evidence-based recommendations for decision making in healthcare. Publication Planners and other stakeholders play a critical role in ensuring the quality and reliability of evidence-based recommendations that SLRs provide for clinical decision-making. However, traditional SLRs can be time-consuming, costly, and challenging to conduct. Real-world evidence (RWE) has emerged as an important source of evidence, but its use presents unique challenges, including heterogeneity, lack of standardization and time-consuming nature. In this session, we will discuss the challenges of creating systematic reviews from RWE and explore how AI/NLP can be used to create compliant and fast systematic literature reviews, given its screening automation and streamlining of the data extraction capabilities.

Learning Objectives: At the end of this session attendees will be able to:
●Understand the challenges of creating systematic reviews from real-world evidence (RWE) for publication planners and other stakeholders
●Learn how AI/NLP can be used to overcome the challenges of creating systematic reviews from RWE
●Explore the benefits and limitations of using AI/NLP in systematic reviews from RWE
●Understand the regulatory requirements for using AI/NLP in systematic reviews from RWE
●Learn best practices for using AI/NLP in systematic reviews to ensure compliance and quality

Approved for 1 CMPP credit.