2025-06-23-艾昆纬-以自信和专注进行创新_通过利用新型工具和方法_提升设计_规划和试交付中的可预测性和质量_8页_893kb

Whitepaper Summary: Confidence and Focus in Innovation for Clinical Research

Introduction

This whitepaper explores how sponsors and CRO partners can enhance innovation in clinical research by leveraging advanced tools and methods to improve predictability, quality, and decision-making. It highlights the use of AI, machine learning, and prescriptive analytics to mitigate risks and optimize clinical trial processes, authored by RAJA SHANKAR and STEPHEN HAMPTON from IQVIA.

Key Objectives

• Utilize novel tools and methods for better decision-making in risk-prone environments.
• Enhance confidence in clinical development through advanced analytics and AI integration.

Core Content

Evolution of Analytics

• Analytics progress from descriptive ( summarizing past data) to predictive (forecasting future events) and now prescriptive (advising specific actions).
• Advanced AI and automation improve risk management, data quality, and efficiency in trial design and execution.

Innovation Areas in Planning and Design

Clinical Development Planning

• Robust methods incorporate historical data, AI, and domain expertise to evaluate factors like scientific feasibility, regulatory approval, and commercial potential.
• IQVIA's healthcare-grade AI enhances planning by providing precise insights, predicting drug responses, identifying new targets, and assessing unmet medical needs.

Protocol Optimization

• AI-driven digital connection of protocols enables efficient scenario planning and evidence-based decisions on design elements.
• Tools score protocols for operational complexity and patient/site burden, reducing trial challenges and improving recruitment.

Patient Recruitment Strategies

• AI accelerates the creation of data-driven recruitment scenarios, improving accuracy in predicting enrollment rates and tailoring strategies for diverse patient populations.

Benefits

• Increased confidence and predictability through automated, AI-enhanced tools.
• Faster end-to-end processes, better risk mitigation, and higher trial success rates.

Conclusion

Advanced AI and analytics are crucial for transforming clinical research, fostering innovation with greater precision and reliability, setting the stage for autonomous AI applications.