goBalto White Papers

  1. Don’t Settle For Less. Redefining The Core And Scope Of Study Startup

    This white paper focuses on defining and expanding the definition of study startup, an element of clinical trials that is gaining attention because it offers the greatest opportunity to improve quality while compressing clinical trial timelines.

  2. The Evolution Of Risk Management In Clinical Trials

    The cloud has made anywhere/anytime, centralized monitoring (adaptive-on-site and off-site) of all risk-related trial factors a seamless reality. Looking ahead, the cloud can integrate centralized monitoring, risk management measures, and predictive analytics. It can also create risk repositories, to keep trial costs in check and optimize better quality results for life saving therapies.

  3. Addressing The Data Challenges Of Pharmacovigilance

    As pharmacovigilance adopts next-generation technology by leveraging artificial intelligence (AI) and the cloud, new possibilities are opening up for knowledge generation – and thus value – from the data collected and processed. This paper looks at three important developments around drug safety data and their analysis and how industry is prepared for them.

  4. Building Risk Assessment and Mitigation Into Study Startup

    Risk management efforts in drug development have mostly emphasized post-marketing drug safety, but the clinical trials process has its own set of potential risks that can easily derail a company’s costly development programs.

  5. Spotlight On Quality In Study Startup

    This white paper addresses the growing interest in quality in clinical trial execution and how workflows play an essential role by building in the steps needed to comply with TMF guidelines, reducing downstream problems. This proactive strategy limits issues caused by siloes, yielding process improvements measurable by performance metrics.

  6. Analytics And Metrics Help Pinpoint Costs Of Study Startup

    Learn how by embracing a systematic, data-driven approach, it is possible for metrics to identify more accurately the best sites, steps causing delays, the associated costs, and why this is happening.

  7. Study Startup: The Last Major Frontier In Automating Clinical Operations

    The industry has stepped up with various cloud-based solutions such as clinical trial management systems (CTMS), electronic data capture (EDC), and the electronic trial master file (eTMF)—all quantum leaps—yet lengthy cycle times, lasting nearly seven years,1 are still commonplace. A key reason is that they do not address the one part of a study’s lifecycle that strongly impacts the overall timeline of clinical trial conduct—study startup (SSU). As more stakeholders acknowledge that better SSU processes are essential for shorter clinical trial timelines, SSU has become the last major frontier in clinical trial automation, the final holdout where spreadsheet methodology still looms large, and where innovation is making a resounding difference.

  8. Breathing Life Into SOPs To Streamline Clinical Study Startup

    This white paper describes how automating SOPs for study startup - a notorious bottleneck2 - can guide sponsors and CROs to compliance using workflows consistent with organizational standards and country-specific regulations.

  9. Study Startup: New Battleground In CRO Differentiation Strategy

    The drug development process is long, arduous, and costly, driving many sponsors to expand their use of contract research organizations (CROs). This move reflects sponsors’ sharper focus on core competencies as they shift the management and conducting of clinical trials to CROs.

  10. Study Startup Solutions Improve CRO Oversight Through Collaboration

    The relationship between sponsors and contract research organizations (CROs) is strengthening as outsourcing becomes a clinical trial mainstay. Making this connection as productive as possible means continuing the transition away from tactical projects and toward strategic partnerships with both stakeholders have a vested interest in greater operational efficiency.