Understanding The Clinical Data Management Process
Table of Contents
From a financial standpoint, drug developers want to make sure the data they provide to regulatory agencies is accurate; from an ethical standpoint, clinical data drive treatment decisions and, ultimately, patient health. Clinical data quality and integrity are critical for both of these reasons.
Although data management may begin after the data has been gathered, it begins before the research protocol has been established. Because CDM is best considered a process that runs throughout a clinical trial, it’s critical to include the CDM team from the outset, beginning with protocol creation.
What Is Clinical Data Management and How Does It Work?
clinical data management services (CDM) is the process of processing data generated by clinical studies. Clinical data management encompasses all areas of research data processing. This involves creating and maintaining software systems, databases, processes, procedures, training, and protocols to help with data collection, cleansing, and management.
Clinical data management’s major goal is to develop and maintain high-quality data from data input through analysis. As a result, clinical data management produces an accurate, secure, dependable dataset, and is available for analysis at the end of a trial if done appropriately.
What Is the Importance of Clinical Data Management?
Clinical data management is a vital component of the regulated product review process (e.g., pharmaceuticals, medical devices, cosmetics, food). Clinical data management provides the following benefits in addition to assuring that these products are safe, function as predicted, and comply with regulatory requirements:
- Quality control for data
- development in a hurry
- Data backup and recovery
The administration of clinical data also guarantees that:
- Statistical analysis and reporting requirements a clean study dataset.
- Data collection must be thorough and precise.
- On-time data is prepared for maximum use.
- Data quality and integrity transmitted from trial subjects to the study database
- In the database of the study, there is an accurate picture of the trial.
Clinical Trial Data Management System Has Three Objectives
- Compiling data
Using paper and electronic mediums to collect data
- Integration of data
All of the data is integrated into a single database to maintain consistency and accuracy.
- Validation of the system and data
UAT (User Acceptance Testing), QC (Quality Control), programming using edit check programs, and manual review
How Does the CDM Process Operate?
The CDM process begins before the study protocol is established to ensure data integrity. First, the CDM team creates a case report form (CRF) and the data fields used. The type of data to be gathered, the units of measurement utilized, and the CRF completion standards are all specified in CRFs (i.e., instructions for filling in data). Then, using coded phrases, variables are annotated.
The trial’s CDM operations are then described in a data management plan (DMP). Databases with accompanying compliance tools are designed to assist CDM duties. Before employing real clinical trial data, the plan is tested. The following phases in the procedure include CFR tracking, data entry, validation, discrepancy management, medical coding, and database locking.
Case report forms can gather data on paper or electronically, but as technology has advanced, so tends electronic data collection. Furthermore, as a time-saving technique, several pharmaceutical businesses have used remote data input.
Tools for Clinical Data Management
System for the Management of Clinical Trials
A clinical trial management system is one of the most often utilized clinical data management technologies (CTMS). This is a form of project management software tailored to the needs of clinical research and data management.
A clinical trial management system (CTMS) allows a single organization or group to organize, report, and track all elements of clinical trials, making them more efficient, compliant, and effective.
- Management of documents
- Data collection using electronic means (EDC)
- Randomization, among other things, is part of the enrollment management process.
- Installation options include cloud-based and on-premises solutions.
- The bare minimum of users
- Support for iOS and Android on mobile devices
- database of patients
- Capabilities for payment
- Management of the recruiting process
- Capabilities for scheduling
- Communication on the job
- Workflows and study planning
A clinical trial data management system can provide the following advantages:
- Data repository that is both controlled and standardized
- Managing and analyzing data from a single location
- Costs of IT and company operations are both reduced.
- Process efficacy has improved
- Better quality submissions
- adherence to established criteria
- Having access to a system that provides a single source of truth
- Data collecting that is consistently automated
- Regulatory compliance is guaranteed.
Recommendations to improve clinical trial data management system;
Consistency is crucial, especially when working with different sites and team members. From one visit to the next, every research staff member must perform the tests or procedures exactly as described in the protocol. If a query is made because a cataract was overlooked at baseline and subsequently recorded at the second visit, it adds to the study’s workload and raises the risk of erroneous reporting of an adverse event.
The training staff will have to go back and resolve the inquiry after it is given, which takes far longer than simply being comprehensive and consistent in what is evaluated from a visit to visit. So, again, the fewer questions you ask, the better.
Another issue to examine is what “relevant” medical history means. Medical history takes up much time in retina studies, and it’s generally a low-hanging objective because it doesn’t affect the primary and secondary endpoints. Moreover, given the sick condition, much effort is spent cleaning medical records that are irrelevant to the research instead of current or more applicable medical histories.
Medical histories are unquestionably important, but a tonsillectomy performed 15 years ago may not be as significant as LASIK surgery performed the year before.
Finally, bad events in the eye are categorized differently than those occurring elsewhere in the body. Ensure that the terminology used in documenting adverse events or medical history is full and precise. For example, if hyperemia is found, think about whether it should be labeled as a vascular condition or ocular hyperemia. When recording a discovery, make sure it’s anatomically specific, leading to fewer clarification requests.
From the very beginning of a clinical trial until the results are preserved, clinical data management provides advice to maintain data integrity. Taking the effort to follow the clinical data management practices that run throughout clinical trials results in high-integrity data that can be used for research and reporting.
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