Lead with (1) sentence restating the peer’s main point and express agreement/disagreement with a specific part of their point
- Start with a formal salutation; use your peer’s proper name
- Do not start with something like “Hey I really loved your post!” or “I agree completely!”
- Lead with (1) sentence restating the peer’s main point and express agreement/disagreement with a specific part of their point
- Do Not: fill your post with compliments and neglect adding your thoughts/opinions and evidence to back up your critical thoughts
- Do Not: simply repeat the points you made in your initial posts – peer responses need to include fresh content and references
- References: a minimum of (1) (with APA 7th Edition formatting)
- Length: approximately (2) full paragraphs
The National Cancer Institute’s initiative to modernize and standardize a clinical data management system for the US national is no small task. Given their thought on creating this infrastructure to improve clinical trial efficiency, they set up the groups for success. They determine how to adhere and adapt to local applications and a local knowledge base. Instead of implementing a global system and asking all sites and groups to adapt, they take a softer approach, which will assist with the system’s success. The local insight will provide valuable support and input to the system’s infrastructure for the vision to be successful, with the ultimate goal of efficiency to reduce cost and timelines to have metadata for evaluation in a faster timeline.
Local knowledge will assist in strengthening the overall database and reporting. Instead of working around local parameters and requirements, they leverage the local knowledge base. They have outlined manners to working during protocol development to tailor the database to the local needs and integration. As Data365 states, the system must be rational for any individual group or institution (2018). The data system can maintain standardization while also adapting to meet local reasoning needs without compromising the overall data collection, processing, or resulting. NCI establishing this need and building the course for this creation with this in mind shows the adequate planning and proper foresight of the overall goal.
The NCI CDMS team must determine parameters for ongoing review and upkeep of data standards. This should be done at a local and a task force level. Especially in the clinical research industry, the landscape is ever-changing. The data collected in trials supports new products to market to treat medical problems and reflects upon determining necessary modifications to best practices and processes for future trials (Quirk, 2021). The team should be asked what structures will be put in place to remain at the forefront of these changes and how they will work to assess and implement the ones identified as necessary to integrate into the system.
Acting in the role of an NCI staff member, data sharing is part of routine activity. Sharing is not just collecting data but also a top exercise in the collection and sharing itself. The team’s approach to creating governance for reviewing and updating practices is to prioritize the matter. Ensuring that key members of the different teams keep this prerogative in mind with their respective roles will provide success. A subject matter expert will be identified and appointed to do just this. Appointed to consistently learn not only global but also local shifts in data management initiatives and bringing this to the attention of the larger group is primary. From there, the NCI will discuss as a larger group and determine the best routes for incorporation. Given your research on Clinical Data Management Systems:
Participation of local laboratories in clinical trials and their impact on the data management process. DM365. (2018, February 19). Retrieved February 7, 2022, from https://datamanagement365.com/blog/articles/uchastie-lokalnykh-laboratoriy-v-klinicheskikh-issledovaniyakh-i-ikh-vliyanie-na-rabotu-menedzhera-p/uchastie-lokalnykh-laboratoriy-v-klinicheskikh-issledovaniyakh-i-ikh-vliyanie-na-rabotu-menedzhera-p.html (Links to an external site.)
Quirk, D. (2021, July 27). Keeping Up With the Trends: How Digital Demands Affect Data Centers. Keeping up with the trends: How digital demands affect data centers. Retrieved February 7, 2022, from https://www.ashrae.org/news/ashraejournal/keeping-up-with-the-trends-how-digital-demands-affect-data-centers
What I feel is the most interesting aspect of the NCI’s initiative to modernize and standardize a CDMS for the US national cooperative groups are the partnerships and collaborations that were created. NCI partnered with the Center for Biomedical Informatics and Information Technology (CBIIT) to create a system to help modernize and standardize research. CBIIT partners with NCI programs and cancer researchers to meet today’s community’s informatics, data, and information technology needs (NCI,2022). The collaboration between NCI and CBIIT to develop such a system as a cloud-based data share data to further cancer research is impressive. The semantic infrastructure provides standard terminologies, common data elements, clinical case report forms, and data models (NCI,2022). The Cancer Trial Support Unit (Registry) creation made enrollment easier for cancer studies to find participants. The CTSU collaborates with the NCI and its funded organizations to develop and support operational processes and informatics solutions, leading to cost-effective solutions that reduce the administrative burden on the clinical site (CTSU,2022). Registry like the CTSU is created for other disease states that burden enrollment, such as Improve Care Now for pediatric Crohn’s and Ulcerative Colitis. Progress cannot be made without the cohesiveness of teams working to build something great.
Throughout the development of the Medidata system, they speak to working groups for data elements that fall under the integration prioritization as a priority one, necessary for implementation. As a data manager, I would want to know what caDSR was before establishing a CRF model. I would like to know precisely what caDSR was to develop a model for the eCRFs. As an NCI staff member, I would inform the data manager that caDSR stood for Cancer Data Standards Registry and Repository. Then I would continue to tell him is a comprehensive set of standardized metadata descriptors for cancer research data for both information collection and analysis (NIH, 2022). Then I would follow up by saying it is designed to integrate Cancer Common Ontologic Representation Environment infrastructure and supports the development and deployment of data elements that are used as metadata descriptors, primarily for NCI-sponsored research (NIH, 2022).
Center for Biomedical Informatics and Information Technology. National Cancer Institute Center for Biomedical Informatics and Information Technology. (n.d.). Retrieved February 8, 2022, from https://datascience.cancer.gov/about
Connecting data to accelerate cancer research. National Cancer Institute Center for Biomedical Informatics and Information Technology. (n.d.). Retrieved February 8, 2022, from https://datacommons.cancer.gov/
CTSU members. Welcome to the CTSU Website. (n.d.). Retrieved February 8, 2022, from https://www.ctsu.org/
National Institutes of Health. (n.d.). Umls Metathesaurus – NCI_CADSR (cancer data standards registry and repository) – synopsis. U.S. National Library of Medicine. Retrieved February 8, 2022, from https://www.nlm.nih.gov/research/umls/sourcereleas…
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Subject: Health & Medical
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