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Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates

BACKGROUND: The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity. METHODS: Tufts Center for the Study of Drug Development (Tufts...

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Autores principales: Harper, Beth, Smith, Zachary, Snowdon, Jane, DiCicco, Robert, Hekmat, Rezzan, Van Willis, Weeraratne, Dilhan, Getz, Ken
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104918/
https://www.ncbi.nlm.nih.gov/pubmed/33963525
http://dx.doi.org/10.1007/s43441-021-00301-z
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author Harper, Beth
Smith, Zachary
Snowdon, Jane
DiCicco, Robert
Hekmat, Rezzan
Van Willis
Weeraratne, Dilhan
Getz, Ken
author_facet Harper, Beth
Smith, Zachary
Snowdon, Jane
DiCicco, Robert
Hekmat, Rezzan
Van Willis
Weeraratne, Dilhan
Getz, Ken
author_sort Harper, Beth
collection PubMed
description BACKGROUND: The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity. METHODS: Tufts Center for the Study of Drug Development (Tufts CSDD)—in collaboration with IBM Watson Health—conducted an online global survey between September and October 2020. RESULTS: One hundred ninety four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial. CONCLUSION: Mid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases.
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spelling pubmed-81049182021-05-10 Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates Harper, Beth Smith, Zachary Snowdon, Jane DiCicco, Robert Hekmat, Rezzan Van Willis Weeraratne, Dilhan Getz, Ken Ther Innov Regul Sci Original Research BACKGROUND: The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity. METHODS: Tufts Center for the Study of Drug Development (Tufts CSDD)—in collaboration with IBM Watson Health—conducted an online global survey between September and October 2020. RESULTS: One hundred ninety four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial. CONCLUSION: Mid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases. Springer International Publishing 2021-05-07 2021 /pmc/articles/PMC8104918/ /pubmed/33963525 http://dx.doi.org/10.1007/s43441-021-00301-z Text en © The Drug Information Association, Inc 2021, corrected publication 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Harper, Beth
Smith, Zachary
Snowdon, Jane
DiCicco, Robert
Hekmat, Rezzan
Van Willis
Weeraratne, Dilhan
Getz, Ken
Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates
title Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates
title_full Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates
title_fullStr Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates
title_full_unstemmed Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates
title_short Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates
title_sort characterizing pain points in clinical data management and assessing the impact of mid-study updates
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104918/
https://www.ncbi.nlm.nih.gov/pubmed/33963525
http://dx.doi.org/10.1007/s43441-021-00301-z
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