FEATURED CONTENT

• R&D Life Science Predictions: Exciting Trends To Watch For In 2024

  From AI's broad operational impact to the adoption of innovative franchise models with SaaS providers, take a look at a few trends that could define life sciences in the coming year.

• Dear Clinical Data Reviewer, Listing Reviews Don't Have to Be Painful.

  Struggling with cumbersome listing reviews? Discover how modern SaaS tech and collaborative workflows streamline data consistency for Medical Monitors and Data Managers.

• Leveraging Large Language Models For Business Automation

  Unlocking the full potential of Large Language Models (LLMs) for operational workflows requires strategic selection and meaningful prompts. Let’s explore their application in business automation.

• Clinical Technology: Were You Sold A Ferrari When You Actually Need A Crossover?

  Just as SUV crossovers have become popular for their practicality, the life sciences industry is adopting integrated clinical data platforms to enhance efficiency, collaboration, and cost-effectiveness.

• Efficient Data Aggregation And Oversight: A CRO Success Story

  Explore how a full-service CRO and tech-driven life sciences provider streamlined data aggregation and clinical trial oversight, yielding significant time and cost savings.

• Breaking The Mold: How R Is Revolutionizing CSR Generation

  The open-source statistical programming language R is becoming more popular in the life sciences sector for clinical study report submissions due to its cost-effectiveness, inclusivity, and collaboration.

• Emerging Biotechs: Clinical Data Platform Considerations To Lessen CRO Reliance

  Explore the crucial factors necessary to evaluate clinical data platforms, leading to more streamlined and effective management of clinical trials.

• DCTs: Balancing Patient Accessibility With Data Reliability

  Discover why ongoing dialogue and scrutiny are essential to strike a balance between patient-centered initiatives and the methodological rigor necessary for credible and reliable results.

• Implementing ICH E6 (R3) In A Risk Averse Culture

  Get several pointers on how to facilitate a transition to an agile, multi-faceted, and continuous risk and issue management approach aligned with the Statistical Analysis Plan.

• Deciphering Fiction From Reality In The Technology B2B Sell Cycle

  Gain insight from someone who has worked on both sides of B2B sales about choosing a technology partner and what you should look for in a vendor.

• Applied AI For Enhanced Clinical Data Operations

  Explore AI use cases and practical applications to accelerate and advance clinical trial operations through data interoperability, automation, and data surveillance.

• Maximizing Clinical Data Interoperability And Accelerating Trials

  Observe how meta-data-driven systems yield fresh data insights and empower seamless synchronization and automation of data review workflows, redefining the landscape of clinical research.

• FDA Covariate Adjustment Guide: What Non-Statisticians Should Know

  Non-statistical team members are encouraged to read this summary regarding the FDA’s guidance to better understand and support covariate adjustments applied in clinical trials.

• The Evolution From Data Integrity To Data Quality

  Consider these three factors when transitioning to a more robust and quality-driven data cleaning and central monitoring strategy.

• Are You Aiming For The Bullseye Or Just The Target?

  Learn what this author has to say about focusing on a clinical trial's outcome when developing Risk-Based Quality Management strategies.

• Closing the Gap Between C-Suite Vision And Operational Reality

  Unearth how biopharma companies are bridging the gap between C-suite members and clinical operations with these scalable and practical solutions.

• Implementing And Scaling Data Platforms For Streamlined Delivery

  Dive into this three-stage execution plan established by a CRO that allowed organizational and process changes to occur in lockstep with a risk-based quality management platform rollout.

• Creating A Successful ML Data Governance Strategy

  Thanks to discoveries and tech advancements, the industry has witnessed a surge in data. Consider these key principles when developing and implementing machine learning governance policies.

• Repositioning Your CDM Team To Meet Digital Trial Demands

  Consider these steps when repositioning your clinical data management team to unify cross-functional reviews and align processes and technology to meet the demands of digital trials.

• Small Team, Big Results: Maximizing Your Data Footprint

  Explore several options that leaders should consider when determining how best to leverage and position data science expertise to support clinical data operations.