Literature DB >> 21731513

Ontario protocol assessment level: clinical trial complexity rating tool for workload planning in oncology clinical trials.

Bobbi Smuck1, Phyllis Bettello, Koralee Berghout, Tracie Hanna, Brenda Kowaleski, Lynda Phippard, Diana Au, Kay Friel.   

Abstract

PURPOSE: The Ontario Institute for Cancer Research supported the creation of a working group with the objective of developing a standard rating scale to evaluate clinical trial complexity and applying the scale to facilitate workload measurement for Ontario cancer research sites.
METHODS: The lack of a mechanism to measure the workload involved in a clinical trials protocol was identified and confirmed by a literature review. To collect information on how Ontario sites were assessing workload, a survey was distributed and evaluated. As a result, the working group developed the Ontario Protocol Assessment Level (OPAL), a protocol complexity rating scale designed to capture the workload involved in a clinical trial. After a training workshop on the application, OPAL was evaluated by 17 Ontario cancer centers to demonstrate its reliability and consistency during a 3-month pilot study.
RESULTS: Twenty-seven protocols were reviewed by multiple sites, and the majority of the sites reported OPAL score differences between 0 and 1.5.
CONCLUSION: OPAL provides clinical trials departments with an objective method of quantifying clinical trials activity on the basis of study protocol complexity. With consistent application of OPAL, sites can manage staffing objectively. The working group is continuing to monitor the application of OPAL in Ontario.

Entities:  

Year:  2011        PMID: 21731513      PMCID: PMC3051866          DOI: 10.1200/JOP.2010.000051

Source DB:  PubMed          Journal:  J Oncol Pract        ISSN: 1554-7477            Impact factor:   3.840


  3 in total

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Authors:  Kenneth A Getz; Julia Wenger; Rafael A Campo; Edward S Seguine; Kenneth I Kaitin
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2.  Factors affecting workload of cancer clinical trials: results of a multicenter study of the National Cancer Institute of Canada Clinical Trials Group.

Authors:  Kathyrn Roche; Nancy Paul; Bobbi Smuck; Marlo Whitehead; Benny Zee; Joseph Pater; Mary-Anne Hiatt; Hugh Walker
Journal:  J Clin Oncol       Date:  2002-01-15       Impact factor: 44.544

3.  Invisible barriers to clinical trials: the impact of structural, infrastructural, and procedural barriers to opening oncology clinical trials.

Authors:  David M Dilts; Alan B Sandler
Journal:  J Clin Oncol       Date:  2006-10-01       Impact factor: 44.544

  3 in total
  12 in total

1.  Clinical Trial Assessment of Infrastructure Matrix Tool to Improve the Quality of Research Conduct in the Community.

Authors:  Eileen P Dimond; Robin T Zon; Bryan J Weiner; Diane St Germain; Andrea M Denicoff; Kandie Dempsey; Angela C Carrigan; Randall W Teal; Marjorie J Good; Worta McCaskill-Stevens; Stephen S Grubbs; Eileen P Dimond; Robin T Zon; Bryan J Weiner; Diane St Germain; Andrea M Denicoff; Kandie Dempsey; Angela C Carrigan; Randall W Teal; Marjorie J Good; Worta McCaskill-Stevens; Stephen S Grubbs
Journal:  J Oncol Pract       Date:  2015-12-01       Impact factor: 3.840

2.  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

3.  The clinical research team.

Authors:  Allison R Baer; Robin Zon; Susan Devine; Alan P Lyss
Journal:  J Oncol Pract       Date:  2011-05       Impact factor: 3.840

4.  Conceptual Model for Accrual to Cancer Clinical Trials.

Authors:  Simon J Craddock Lee; Caitlin C Murphy; Ann M Geiger; David E Gerber; John V Cox; Rasmi Nair; Celette Sugg Skinner
Journal:  J Clin Oncol       Date:  2019-06-05       Impact factor: 44.544

5.  Machine Learning Prediction of Clinical Trial Operational Efficiency.

Authors:  Kevin Wu; Eric Wu; Michael DAndrea; Nandini Chitale; Melody Lim; Marek Dabrowski; Klaudia Kantor; Hanoor Rangi; Ruishan Liu; Marius Garmhausen; Navdeep Pal; Chris Harbron; Shemra Rizzo; Ryan Copping; James Zou
Journal:  AAPS J       Date:  2022-04-21       Impact factor: 4.009

6.  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

7.  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

Review 8.  Optimization of protocol design: a path to efficient, lower cost clinical trial execution.

Authors:  Marina A Malikova
Journal:  Future Sci OA       Date:  2016-01-12

9.  IRB Process Improvements: A Machine Learning Analysis.

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Journal:  J Clin Transl Sci       Date:  2017-04-26

10.  Assessment and classification of protocol deviations.

Authors:  Ravindra Bhaskar Ghooi; Neelambari Bhosale; Reena Wadhwani; Pathik Divate; Uma Divate
Journal:  Perspect Clin Res       Date:  2016 Jul-Sep
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