Is the Future of Clinical Data Management Finally Here? Indeed, It Is!
Clinical Data management has reached its pinnacle. It is currently being modernised by combining clinical research and clinical data management on cloud-based platforms to improve data management systems & clinical data management services scalability, flexibility, intelligence, and interoperability with various third-party systems. Explore this blog to learn where your company stands in the clinical data management landscape & the changes that need to be made to reduce data management risk and optimise cleaning strategies to support novel trial design paradigms like decentralised clinical trials, which are also discussed in this post. When it comes to assuring the future viability of your drug development initiatives, current clinical data management systems can make all the difference.
Clinical data management (CDM) is a method of collecting, managing and validating clinical data in clinical trials. Before generating analysis datasets, clinical data managers guarantee accurate coding, validation, and data review.
Clinical research methods have changed dramatically in the previous ten years. Globalisation of clinical trials, complicated trial designs, and a trend to patient-centric studies have positioned modern technology as a viable alternative to traditional data management. CDM activities have evolved as a result of new technology.
What Is Clinical Data Management (CDM) and How Does It Operate?
To ensure data integrity, the CDM process begins even before the study protocol is established. 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 to be 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 tasks.
Before employing actual clinical trial data, the plan is tested. The following steps in the procedure include CFR tracking, data entry, validation, discrepancy management, medical coding, and database locking.
Case report forms can be used to collect data on paper or electronically, but as technology has advanced, so has the trend toward electronic data collection. Furthermore, as a time-saving technique, many pharmaceutical businesses have implemented remote data entry or e-CRF.
With the introduction of new technology and clinical procedures, we are seeing an increase in data obtained through digital means. As a result, the previous methods (first paper, then EDC) are no longer viable solutions. With EDC becoming yet another data source, clinical data management teams are being asked to reduce database build times, data handling durations and boost productivity while adhering to a quality-by-design strategy to assure data integrity.
Make use of the potential presented by these technologies to automate data evaluation, increase quality, and decrease human labor and resource-intensive stages.
Evolve from gathering, cleansing, and delivering data to internal and external clients to being data stewards and leaders in this fast-changing, digitally empowered environment.
More than ever, they are critical in delivering more efficient procedures for real-time data collection/processing, TPV (Third-Party Validation) integration, and technology optimization to assure timely, high-quality data delivery to internal/external clients.
- Improve TPV selection and management processes to ensure important criteria are known, agreed upon, and followed.
- Ensure TPV data alignment, data delivery on schedule, smooth integration and reconciliation with other clinical data, and increased delivery oversight.
It is necessary to synchronize the mechanisms and procedures for gathering, reviewing, and cleansing data. Because current EDC platforms cannot handle such large volumes of data, repositories (such as data lakes) are being established. Data managers and the clinical team will be unable to use the same data cleaning practices, so they are turning to AI/ML to scour the data first, greatly improving the preliminary quality of the clinical research and clinical data management and highlighting potential errors for teams to investigate further to determine if the data is indeed incorrect.
Teams can discover trends and enhance data quality and integrity more quickly using powerful dynamic visualizations to examine clinical data collected on a clinical trial than they would use “data dumps.” As a result, real-time data assessments will be replaced by a risk-based, prospective validation strategy, lowering the overall cleaning time.
Applying KRI (key risk indicator) levels to the data review process helps focus the reviewer on areas of concern in the data or the flow/processing of the data. Programmatically adding them to the dashboards, study-specific KRI thresholds would be developed and integrated into the review process. Moving the data cleansing process away from a “examine everything” perspective and into a more focused risk-based strategy.
Improvements in utilizing KRI thresholds, technical advances, and continued enhancements and optimization of AI/”skills,” ML will lessen the strain on teams even more. This will also enable teams to focus more on critical thinking activities and make better-informed decisions faster to provide life-changing treatments.
Using Deep Data & Clinical Data Management ServicesTo Its Full Potential
Element Technologies’s Clinical Data Management team is ready to help you make the most of the data you’ve worked so hard to acquire by improving efficiency, lowering development costs, and predicting hazards.
Our global staff of clinical data managers, statisticians, and statistical programmers combines deep data with clinical data management services to provide high-quality information at every process stage. In addition, we’re here to help you with fact-based counsel on everything from systematic analysis and reporting through the regulatory process.
The following services are available:
- Clinical Services
- Clinical Data Services
- Biostatistical Services
- CDISC Standardisation Services
- Quality Assurance, Quality Engineering, Digital Assurance, and IV&V Services
We are altering the way we collect patient data for the better. However, compiling everything against the same standards can be difficult, including sensors, wearables, reported outcomes, and more. This is especially true in cross-border trials. With a comprehensive set of tools in one place, our clinical research and clinical data management links the dots between descriptive Statistics, data governance, and interpretation.