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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Detalles Bibliográficos
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:
Original Research
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
Descripción
Sumario: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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