Gaining clinical efficiency in cancer research through master data management relies on effectively utilizing master data for population research purposes. A vast amount of data in an integrated research system can be operationalized effectively by technical and human resources. It needs an interdisciplinary team that works in a novel way to establish a hypothesis, bridging technical and disciplinary gaps and interacting effectively to gain related solutions.Several strategies can be adopted to gain clinical efficiency through master data management. Some of the essential steps are the following:
Data Management Plans
Clinical data management plans are necessary to create an outline of data management work for a clinical research project. It covers timelines, milestones, strategies, and deliverables. Extensive data management plans are more effective in boosting research and should have a space for living documentation. All stakeholders must agree to the procedures and work accordingly.
The data management plans should also take care of industry data standards. It enables the research to stand out as a discrete study. CDASH (Clinical Data Acquisition Standards Harmonization) put forward 16 data collection standards to keep data consistent in different studies.
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Data Validation Plan
A critical data validation plan is necessary to resolve database queries and variability by tracking data accuracy, quality, and completeness. It consists of all computations and analyses data managers utilize to recognize variance in the dataset. The dataset is locked after making amendments to data during reporting and analysis. The resulting locked data include CSRs (clinical case study reports) and the investigator’s brochure. In this way, data integrity is maintained. In the end, the data query is sometimes needed to get preliminary results so that the protocol can be modified.
Data Management Workflow
After planning and validating the clinical data management plans, a process named data management workflow should be done appropriately. It deals with data from the data gathering to the results report and their electronic archiving. The entry procedure, batch verification, issue management, coding, restructurings, and quality assurance plans are all included in this. The workflow starts with CRF design, moves to database design, data mapping, and severe adverse events (SAE) reconciliation, and ends with locking the database.
Data Quality Management
Data quality management is another critical point for enhancing clinical research efficiency. It keeps the clinical information at a high level. Quality management in clinical trials is affected by case report forms, data conventions, guidelines for monitoring, missing data, and source data verification.
Meeting theRegulations, Guidelines, and Standards
Clinical data management should comply with guidelines, regulations, and standards to boost the research process’s efficiency. CDISC (Clinical Data Interchange Standards Consortium) is a worldwide organization that keeps clinical studies per data standards, international regulations, standard operating processes, and state laws.
Overall, we can say that cancer research can be boosted by carrying the research according to the required criteria following the clinical data management plans, clinical data validation plan, quality management in clinical trials, and meeting the regulations, guidelines, and standards. It ensures that the cancer health practitioners and researchers implement the protocol and handle the cancer patients as directed. Ultimately, this demonstrates the cancer research programs’ validity, consistency, and professional validation.
About the Author
Sasi Kumar Raju Addepalli is a Senior Master Data Management Developer at a leadingCancer Research and Diagnostics Company, Specializing in Cancer Data Analysis and in finding techniques to detect cancer in early stage. He has garnered vast experience in Master data Management especially working in Healthcare to enhance the usage and accessibility of Cancer related data. He can be reached at email@example.com