Pharmaceuticals and Life Sciences

IBM Cognos Clinical Trial Enrollment Forecasting Performance Blueprint

Patient enrollment is an ongoing challenge for life sciences organizations. Failure to enroll adequate numbers of patients is a primary reason why some clinical trials fail. Sponsors and CROs need robust forecasting and enrollment analytics that involve collaboration among managers, analysts, and the individual investigator sites. The ability to collect and model enrollment trends and make decisions to shut down or set up additional sites is key to managing a global development portfolio as study designs become more complex and resources become more scarce.

Beyond spreadsheets and existing CTMS technologies

Despite the importance of clinical trial enrollment, many life sciences organizations rely on inadequate technologies and processes to manage it. Custom-designed or commercial Clinical Trials Management Systems (CTMS) often lack the flexibility and forecasting capability to model the entire enrollment picture across a worldwide development portfolio. Consequently, spreadsheets are used for modeling and forecasting, creating these issues:

  • Manual processes introduce substantial control risks
  • The combination of CTMS for collecting actual enrollment and spreadsheets for forecasting or scenario modeling leads to problems of data integrity and information silos
  • Spreadsheets lack enterprise-class functionality such as managed workflow and the ability to push template changes out to hundreds of users simultaneously
  • Keeping multiple systems up to date and synchronized is frustrating, time-consuming, and error-prone
  • Spreadsheets create islands of data, making enterprise-wide reporting a challenge.

Align clinical trials with corporate objectives

Based on industry best practices, Cognos, an IBM company, offers an IBM Cognos Performance Blueprint on Clinical Trial Enrollment Forecasting (CTEF). It provides out-of-the box functionality including dashboards, analytical reports, and a pre-configured data model to facilitate rapid time-to-value. Proven practices in model design greatly reduce investment in implementation time and resources.

The Blueprint helps answer these key questions:

  • Am I forecasting sufficient patient numbers to meet study goals?
  • Are patients being enrolled quickly enough to meet end-points?
  • Which sites are underperforming?
  • What are the leading indicators that would provoke a decision to shut down a site?
  • Do I have the right balance between cost and performance?
  • How much will it cost to get enrollment back on target?

Learn more about the Clinical Trial Enrollment Performance Blueprint.

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