What good is data that isn't statistically solid, legible, and dependable? Not any good at all. With clinical data management, however, you can rest assured that your data retains its strategic value. The medical research process generates a large amount of data, and in order to guarantee that it is used as effectively as possible, a robust data management strategy is required. So, what exactly is clinical data management, and how does it work in practice? Let's get to it.
What is Clinical Data management?
Clinical data management (CDM) is the process of collecting and managing research data in line with regulatory criteria. These standards ensure that information is of high quality, error-free, and comprehensive. It must follow federal, state, and municipal regulations.
Because clinical data management deals with information from clinical trials, it's critical that software systems, processes, training, procedures, databases, and protocols all work together to ensure effective trial data collection, scrubbing, and management.
Clinical data management systems arose as a result of regulatory agencies and the pharmaceutical industry's needs. While the pharmaceutical business strives to manufacture and deliver medications as quickly as possible, regulatory organizations have implemented their own regulations. They required firms to follow specific requirements while gathering data utilized in the medication assessment process.
We can't talk about CDM without addressing two standards: the Clinical Data Acquisition Standards Harmonization (CDASH) and the Human Clinical Trials Study Data Tabulation Model Implementation Guide (SDTMIG). These standards are thanks to the Clinical Data Interchange Standards Committee (CDISC). (Can you tell that they enjoy lengthy titles and acronyms?)
Why is Clinical Data Management necessary?
Clinical data management is critical because it guarantees that the data generated by a clinical trial is of high quality. When done correctly, clinical data management yields a dataset that is trustworthy, accurate, and available for analysis at the end of the trial.
Additionally, CDM provides critical assistance in the examination of regulated commodities like medical devices and medicines. Adherence to regulatory requirements is required to ensure that the items you get are safe and will function properly.
Here are some of the benefits that EDC in clinical data management offers:
- Data will not be lost.
- Development is accelerating.
- There is more security.
- The accuracy of the data is assured.
- Expenses are being decreased.
- Data collection is precise and comprehensive.
- The data is properly prepared for maximum usage.
- The database appropriately represents the trial.
- A clean dataset is available for reporting.
Main Objectives of Clinical Data Management
While there are several advantages to collecting clinical data, its basic purposes may be reduced to three key objectives. Let's go a little further into them.
1. Data Collection
EDC Clinical data management ensures that information is appropriately gathered. Additionally, properly collecting and preserving data guarantees that it will be conveniently available when needed later.
2. Data Valuation (of data and systems)
The evaluation and estimation of an item's worth is known as valuation. This comprises manual review for further supervision, as well as user acceptability testing (UAT), programming with edit checks, and quality controls. The goal is to determine how helpful the data is.
3. Data Integration
Finally, data integration allows you to consolidate all of your data into a single database. Putting all of your information in one location promotes accuracy and consistency. This, like data collection, facilitates the process of revisiting the data at a later time.
What are Clinical Data Management tools?
CDM is made feasible with the use of specific software programs. These programs assist users in conducting audits to uncover and reduce differences, especially in large scale, complex clinical studies.
It is especially important to employ appropriate clinical data management systems in studies done in medical centers, which generate huge volumes of data. These systems may be modified and are frequently deployed by pharmaceutical corporations that seek to meet their unique needs.
There are, however, a variety of clinical data management software programs available. Some of the more imperative CDM tools include -
- Clinical Data Management System (CDMS)
- Electronic Data Capture (EDC)
- Clinical Trial Management System (CTMS)
- Electronic Patient Reported Outcomes (ePRO)
- Randomization and Trial Supply Management (RTSM)
These software tools and clinical trial data management systems are intended to support different CDM operations such as data entry, data cleaning, data validation, data analysis, and data reporting, as well as to ease overall data management throughout a clinical trial.
What are The Stages of a Clinical Data Management Cycle?
Now that you have a better understanding of the tools required for good clinical data management, let's look at the cycle that is critical to keeping your data safe and secure.
1. Get ready.
Gather all of your relevant forms, whether electronic or paper. Go over your strategy again, and make sure your database is ready to receive data. The better organized you are throughout this stage of the procedure, the smoother the remainder of it will be.
2. Gather data.
You may now begin the data collection procedure through EDC data management. Throughout your research, continue to collect data. Remember that accuracy comes first. It's a good idea to check in on a regular basis to ensure that your data is correct.
3. Check for correctness.
Check that the tools you're using, your strategy, and the data you're collecting all match regulatory standards. Data quality is a primary concern; therefore, the sooner you can resolve any discrepancies, the better.
4. Keep track of and save your information.
It's time to start tracking your data. Make sure you're keeping an eye on it for any potential concerns or hazards. This is also a good moment to consider what you can do to maintain the quality of your data.
5. Combine your data.
You've kept an eye on your data for problems and ensured its integrity. It's now time to connect datasets and information. As it has been throughout the process, consistency is key.
6. Examine the results.
Because your data is clean and consistent, you can now utilize it to examine the outcome with confidence. That leads us to our second point: your clinical data management cycle does not end after you have examined the results. It is now time to ensure that your data is secure.
7. Safeguard your data.
After finishing the clinical data management cycle, make the effort to safeguard your database to ensure that no information is lost or incorrectly modified.
In Summation
With technological breakthroughs such as machine and artificial learning, the Clinical Data Management industry is eager to enter the digital age of real-time data collection, analysis, and administration.
Octalsoft's eClinical suite enables you to perform trials more efficiently and effectively, with features ranging from simplified data administration and improved collaboration to comprehensive monitoring and reporting. Octalsoft guarantees that our eClinical software suite matches the particular demands of your trial and sets you up for success by focusing on regulatory compliance, data security, adaptability, and scalability. Unlock powerful tools that enhance trial efficiency and data quality and allow for informed decision-making throughout your clinical research journey.
From seamless data migration and comprehensive user training to customization options, data security, collaboration tools, scalability, and future-proofing, Octalsoft's CDM toolkit ensures a smooth and successful transition. By choosing Octalsoft, organizations can mitigate concerns, streamline their clinical trial operations, and pave the way for improved outcomes and efficiency in their research endeavors. Want to know more about how Octalsoft can empower your CDM?
