Literature DB >> 22056655

Creating an effort tracking tool to improve therapeutic cancer clinical trials workload management and budgeting.

Pam James1, Patty Bebee, Linda Beekman, David Browning, Mathew Innes, Jeannie Kain, Theresa Royce-Westcott, Marcy Waldinger.   

Abstract

Quantifying data management and regulatory workload for clinical research is a difficult task that would benefit from a robust tool to assess and allocate effort. As in most clinical research environments, The University of Michigan Comprehensive Cancer Center (UMCCC) Clinical Trials Office (CTO) struggled to effectively allocate data management and regulatory time with frequently inaccurate estimates of how much time was required to complete the specific tasks performed by each role. In a dynamic clinical research environment in which volume and intensity of work ebbs and flows, determining requisite effort to meet study objectives was challenging. In addition, a data-driven understanding of how much staff time was required to complete a clinical trial was desired to ensure accurate trial budget development and effective cost recovery. Accordingly, the UMCCC CTO developed and implemented a Web-based effort-tracking application with the goal of determining the true costs of data management and regulatory staff effort in clinical trials. This tool was developed, implemented, and refined over a 3-year period. This article describes the process improvement and subsequent leveling of workload within data management and regulatory that enhanced the efficiency of UMCCC's clinical trials operation.

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Year:  2011        PMID: 22056655     DOI: 10.6004/jnccn.2011.0103

Source DB:  PubMed          Journal:  J Natl Compr Canc Netw        ISSN: 1540-1405            Impact factor:   11.908


  5 in total

1.  Measuring clinical trial-associated workload in a community clinical oncology program.

Authors:  Marjorie J Good; Barbara Lubejko; Keisha Humphries; Andrea Medders
Journal:  J Oncol Pract       Date:  2013-02-19       Impact factor: 3.840

2.  Assessing Clinical Trial-Associated Workload in Community-Based Research Programs Using the ASCO Clinical Trial Workload Assessment Tool.

Authors:  Marjorie J Good; Patricia Hurley; Kaitlin M Woo; Connie Szczepanek; Teresa Stewart; Nicholas Robert; Alan Lyss; Mithat Gönen; Rogerio Lilenbaum
Journal:  J Oncol Pract       Date:  2016-03-22       Impact factor: 3.840

3.  The National Cancer Institute-American Society of Clinical Oncology Cancer Trial Accrual Symposium: summary and recommendations.

Authors:  Andrea M Denicoff; Worta McCaskill-Stevens; Stephen S Grubbs; Suanna S Bruinooge; Robert L Comis; Peggy Devine; David M Dilts; Michelle E Duff; Jean G Ford; Steven Joffe; Lidia Schapira; Kevin P Weinfurt; Margo Michaels; Derek Raghavan; Ellen S Richmond; Robin Zon; Terrance L Albrecht; Michael A Bookman; Afshin Dowlati; Rebecca A Enos; Mona N Fouad; Marjorie Good; William J Hicks; Patrick J Loehrer; Alan P Lyss; Steven N Wolff; Debra M Wujcik; Neal J Meropol
Journal:  J Oncol Pract       Date:  2013-10-15       Impact factor: 3.840

4.  Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center.

Authors:  Praveena Iruku; Martin Goros; Jonathan Gelfond; Jenny Chang; Susan Padalecki; Ruben Mesa; Virginia G Kaklamani
Journal:  Contemp Clin Trials Commun       Date:  2019-07-19

5.  Design, planning and implementation lessons learnt from a surgical multi-centre randomised controlled trial.

Authors:  Katie Biggs; Daniel Hind; Mike Bradburn; Lizzie Swaby; Steve Brown
Journal:  Trials       Date:  2019-11-01       Impact factor: 2.279

  5 in total

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