The Current Landscape and Emerging Applications for Real‐World Data in Diagnostics and Clinical Decision Support and its Impact on Regulatory Decision Making

Abstract

Real-world data (RWD) and real-world evidence (RWE) are becoming essential tools for informing regulatory decision making in health care and offer an opportunity for all stakeholders in the healthcare ecosystem to evaluate medical products throughout their lifecycle. Although considerable interest has been given to regulatory decisions supported by RWE for treatment authorization, especially in rare diseases, less attention has been given to RWD/RWE related to in vitro diagnostic (IVD) products and clinical decision support systems (CDSS). This review examines current regulatory practices in relation to IVD product development and discusses the use of CDSS in assisting clinicians to retrieve, filter, and analyze patient data in support of complex decisions regarding diagnosis and treatment. The review then explores how utilizing RWD could augment regulatory body understanding of test performance, clinical outcomes, and benefit-risk profiles, and how RWD could be leveraged to augment CDSS and improve safety, quality, and efficiency of healthcare practices. Whereas we present examples of RWD assisting in the regulation of IVDs and CDSS, we also highlight key challenges within the current healthcare system which are impeding the potential of RWE to be fully realized. These challenges include issues such as data availability, reliability, accessibility, harmonization, and interoperability, often for reasons specific to diagnostics. Finally, we review ways that these challenges are actively being addressed and discuss how private-public collaborations and the implementation of standardized language and protocols are working toward producing more robust RWD and RWE to support regulatory decision making.

Exploring the discrepancies between clinical trials and real-world data by accounting for Selection criteria, Operations, and Measurements of Outcome

“…The SOMO approach aims to contribute to the research direction on establishing an analytical process to estimate the challenging trade-off between internal validity and generalizability 6,11,13,33 . In this work we proposed how such a framework could be beneficial for both translational directions: clinical trials can be improved by understanding the discrepancy with the real-world, and real-world therapies can be leveraged by comparing the discrepancies of trials from the real-world (e.g., the comparison of Amgen and Eli Lilly phase III trials in Figure 4).…”

“…Translation between clinical trials and real-world populations is therefore still not completely understood 1,[8][9][10]13,14,16,17 . Further refinement of proposed methodologies are required to realise the potential of real-world data to inform clinical trial design 6,9,10,13,33 . In the past, the added value of a mechanistic systems view of translation in drug development has been beneficial for other areas of model-informed drug development, such as quantitative in vitro-in vivo extrapolation and physiologically-based pharmacokinetics of metabolic drug-drug interactions 34 .…”

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

“…Although major challenges related to data governance/ management, 135,[175][176][177][178][179][180][181][182][183] ethical/legal, [184][185][186][187] and environmental 188,189 considerations would need to be addressed, these "dynamic multimodal ML models" may become cost-effective in different populations 169,[190][191][192][193] if conceived as the integrative tools needed to provide precision health 22,164,165,168,[194][195][196] and support some of the iterative cycles of knowledge generation and continuous improvement of "learning health systems". 11,[197][198][199][200]…”

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine

“…14 Advancements in data privacy preservation approaches have instilled confidence and trust in the utilization of AI by both clinicians and patients and are alleviating privacy concerns that have traditionally prevented data owners from providing access to their data. [15][16][17] However, none of these advancements would have been sufficient with the massive increase in computing power that has been the driving force behind this acceleration. 18 Notably, the application of AI methods using causal inference approaches has been instrumental in advancing the use of AI in clinical research, specifically, and particularly in areas where understanding the causal relationship between a drug and health outcomes is imperative.…”

“…Another contributing factor is related to the continuous improvements in data standards, interoperability, and healthcare data exchange, which has enabled efficient data sharing 14 . Advancements in data privacy preservation approaches have instilled confidence and trust in the utilization of AI by both clinicians and patients and are alleviating privacy concerns that have traditionally prevented data owners from providing access to their data 15–17 . However, none of these advancements would have been sufficient with the massive increase in computing power that has been the driving force behind this acceleration 18 .…”