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Transforming Clinical Trials: The Three Enablers of Intelligent Oversight™

By: Wendy Morahan, Senior Director, Product Management, Medidata

As science advances and clinical trials become more complex, drug development functions are expected to meet ever higher expectations for efficiency and effectiveness. Thus, staff in IT, clinical operations, and data management must be able to perform Intelligent Oversight™. In other words, they must have the tools to understand data in context, be able to collaborate across silos, and promptly remediate issues before they become problems. With the right Clinical Trial Management System (CTMS), trial oversight and management can be transformed such that stakeholders have all the information they need to quickly make decisions and determine their priorities. In this ideal state, study stakeholders no longer work for the study; the study works for them.

The Need for Transformative Technology

In recent years, clinical trials have grown demonstrably more complex. The Tufts Center for the Study of Drug development found that, in comparing the five years from 2001 to 2005 with the five years from 2011 to 2015, there’s been a dramatic increase in virtually all trial aspects that generate data. These include the number of procedures (up 70 percent), investigative sites (up 63 percent), endpoints (up 86 percent), and countries (up 100 percent).[1]

At the same time, life sciences companies are hindered by having to rely on multiple systems, disconnected processes, disparate data, and significant manual effort. This results in counterproductive reporting, inaccurate forecasting, delayed issue remediation, and stakeholder dissatisfaction that leads to high turnover and the risk of noncompliance.

Aim for Effectiveness, not Merely Efficiency

The core expectation of any CTMS has always been improved efficiency. That is, of course, the most obvious advantage over using spreadsheets to manage master data, track enrollment and other critical milestones, identify deviations and issues, and manage site performance. But working efficiently is no longer enough. To successfully plan, monitor, report, and manage increasingly complex trials, the trial team also needs to be more effective. And by that we mean being able to make smart decisions, work together collaboratively, and address issues quickly. Such effectiveness is only achievable in a seamless, data-driven, shared environment.

The Enablers of Intelligent Oversight

For companies to employ Intelligent Oversight, they must ensure that their trial management processes and systems provide them with the means to:

  • Understand Data in Context. As companies cope with more clinical trial data, they must consider the value of that data and its impact on users. Crucially, they must appreciate that data alone is meaningless; it requires structure and context to imbue it with meaning.
     

Understanding the context – what’s really happening – requires cutting through the chaos and quieting the noise in the data. When this is accomplished, companies can work smarter by making informed decisions, identifying problems sooner, and intervening more effectively. For example, EDC subject enrollment information is populated into CTMS and compared to plan, providing clinical trial managers with a complete picture of the status of sites and patients in the trial process.

  • Collaborate. True collaboration is only possible when information flows freely across functions. When all team members of a matrixed, global team have access to the same information, they can accurately manage the entire process, eliminate redundancies, and prioritize patient safety and study quality. For example, if a screen failure rate is higher than expected, several team members, including the Study Monitor and Medical Monitor, must collaborate to assess and correct the situation. 
     
  • Remediate. By detecting issues before they become big problems, companies can optimize outcomes. This involves centralizing analysis, communicating issues, eliminating data reconciliation errors, and mitigating risk (which helps ensure confidence and audit readiness).
     

For example, if the Medical Monitor notices evidence of a site’s misunderstanding of the study inclusion/exclusion criteria, the Site Monitor will need to retrain the staff, document the corrective action and then close the issue. Information on the action will flow into the Monitor Visit Report (MVR) and associated Letters. Resolving the issue effectively and efficiently leads to better data quality and improved compliance.

System Requirements

For a CTMS to support Intelligent Oversight, it must provide:

  • One source of the truth. This means that data records are unified so that data is entered once and an update in one part of the system is reflected throughout the system.
  • Access to multiple data sources.  The system must be able to ingest data not only from Electronic Data Capture (EDC) systems, but also from imaging, patient sensors, laboratory systems, operational software, and more.  
  • A unified platform. Seamless clinical and operational data will drive the flow of work and decision making.
  • Readily available insights. Clearly, oversight is easier when the right information, such as real-time metrics and progress against milestones, is available at the right time. Users should not have to wait for a study log, spreadsheet, or complicated integration to be completed in order to get the information they need.

When the CTMS properly supports Intelligent Oversight, study teams work smarter by having information in context, breakdown silos to work collaboratively, and optimize outcomes with prompt remediation of issues. 

 

[1] Tufts Center for the Study of Drug Development Impact Report, July/August 2018.

This article was created in collaboration with the sponsoring company and our sales and marketing team. The editorial team does not contribute.
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