What is Clinical Data Management?
Clinical Data Management is important and plays a vital role in the data collection part of clinical research. Data collection allows the researcher to obtain accurate data while abiding by the standards of regulatory authorities in the industry. The primary goal of Clinical Data Management is to have the maximum amount of error-free data for extensive research. There is a huge demand for clinical data in the pharmaceutical industry. With such a demand for clinical data and the demands of regulatory authorities in the industry, the management of the same evolves. There is a constant urge of the pharmaceutical companies to develop new products. This needs to be controlled through the process of quality assurance. Clinical Data Management is demanded by the government authorities for assuring the fact that data collected by the pharmaceutical companies for the development of drugs are free from error.
Clinical Data Interchange Standards Consortium (CDISC) is particularly responsible for the development of standards that need to be followed by the companies for research purpose. Two important standards that worth mention is the Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG) and Clinical Data Acquisition Standards Harmonization (CDASH). U.S. Food and Drug Administration (FDA) has made it mandatory to abide by the standards when it comes to the management of clinical data. The process of such data management involves the creation of dedicated tools and software that allows audit trials and mitigate conflicts in large and detailed clinical trials. For conducting trial audits in the various medical centers across the world, Clinical data management systems (CDMS) are extensively used.
How Does Clinical Data Management (CDM) Work?
An organized plan and approach is needed to complete a successful Clinical Trials and Data Management. The process of data management and clinical trial are similar however there are certain differences. The process is initiated much before the protocol of the study is finalized. It is necessary that documents concerned with the field of clinical studies are being finalized. This will provide a guideline for the designing of the database.
A CDM professional is assigned for designing the database. He is also responsible for reviewing the protocol that is associated with the management. Based on the mentioned protocol and plans a proper data management plan is developed. This is one of the most important parts of the management system that helps in resolving conflicts and being aligned with the process. Once the plan is done properly a database is developed. A database can be considered as a software that helps the user to perform various tasks in the process of Clinical Data Processing.
The data collection can be done in two different ways. It can be collected in electronic formats or papers. The information is entered by the front end users and is stored in the database in an organized way. A clinical research associate is assigned to track the activities in the CRF. He is responsible for the accuracy and finding discrepancies in the data. Once everything is done, the final step is database locking. Once the database is locked there are no possibilities to make further changes.
Evolution of Clinical Data Management
The evolution of data is a continuous process. Since the inception of Clinical Data Processing, there are several ways the clinical data and its management has changed with the changing environment of the pharmaceutical industries and the regulatory authorities. Both internal factor and external factor triggers the need for evolution for such data management from time to time. While the strategies and approaches of a Clinical Data Management Company differ from the models which are to be outsourced the company needs to look into the ways they would fit in the market. This is one reason why there is a continuous evolution of data management. On the other hand, factors like technological development in the clinical studies, market demand shift and change in the guidelines of regulatory authorities are some of the external factors that trigger the need for the evolution of Clinical Trials and Data Management from time to time. Over time, Clinical Data Processing has evolved extensively. For example, in today’s time, clinical data are not just collected in case report forms but other resources like IVRS and electronic patient-reported outcomes (ePRO) systems are also included in the process of data collection and management. The data managers need to learn new skills that would help them adapt to the new trends of data management. Study and analysis of data is a tedious and time taking process. However, the manager needs to find ways to minimize the time and maintain accuracy as per the demands of today’s market.
What are the steps in the process of Clinical Data Management?
Steps involved in Clinical Data management are as follows:
- A clinical trial is the first step that is being taken for the management of data. This helps in maintaining the integrity of data. The clinical trial is initiated much before finalizing the study protocol.
- Once the first step is completed, the Clinical Research and Data Management team provides a case report form. The form mentions the data filed where such data is to be utilized. The type of data and the measurement units are also being mentioned by the Clinical Research and Data Management team.
- The form provided by the team also has certain guidelines that need to be followed when data collection is done. The process of data collection needs to abide by the guidelines mentioned in the form. The form also has certain terms which are coded for annotation of variables.
- Once the process is completed a data management plan (DMP) is formed. The plan includes the guidelines that are to be followed while performing the Clinical Research and Data Management activities.
- Specific tools are involved in forming databases that would support data management activities. A process of testing is initiated much before using the plans for real clinical trials.
- The process is followed by further steps of tracking CRF, entering data, validation, management of conflicts and discrepancies, medical coding, and locking of the database.
Clinical data management services
A professional Clinical Data Management Company will attempt to mitigate the risk associated with the clinical trial. This is the primary goal of the company. Such companies provide a holistic solution to pharmaceutical companies with advanced analysis and risk management initiatives. Advanced Clinical Data Management Company integrated various business intelligence tools that enhance Clinical Trials and Data Management for successful research. Such a company would help in monitoring and controlling clinical trials effectively and efficiently. A range of services provided by these companies such as data management tools and risk mitigation support is helpful for the pharmaceutical companies to research in successful ways. With the growing demand in pharmaceutical research, a trusted data management company is an essentiality to be considered. The data provided by such companies are well adhered to the regulatory authorities and ensures the safety of the patient.
ClinACT is the only RBM solution that offers two complementary modules. The Analytics module can be used as a standalone tool for Study oversight, or for better insight, the integrated Risk module makes ClinACT the most complete RBM system available.
Better analytics: The detailed, cross study integrated analytics delivered by ClinACT are easily understood through our visual dashboards, including: site performance, study performance, subject-specific and region-specific views. This allows multi-vendor (CRO’s) oversight at the study and portfolio level.
ClinHUB is a revolutionary data aggregation platform that is flexible, source system agnostic and provides analytics that enable users to visualize outliers and trends.
Data integration: Leveraging big data architecture to aggregate both structured and unstructured data with relative ease, ClinHUB is highly configurable to allow you to load data from multiple sources and formats, including databases, line listings, files, reports, etc.
Site Payment and Contracting Environment provides end-to-end automation with integration of contracts and payments modules to help accelerate payment cycles and mitigate potential manual error.
CTA manager: Highly configurable template-based engine to author and generate CTAs and define contract to payment terms
Put the power of the data in the hands of the data managers with our cutting edge new Data Workbench tool. This source agnostic tool allows users to perform complex relationship checks for increased accuracy of clean, quality data. Our Data Workbench is fully integrated with our existing line of products to provide enhanced speed and quality of data throughout the clinical research process.
Business Intelligence Tool
Experience the newest revolution in data visualization and clinical trial analytics with our new Business Intelligence tool. Users have the ability to slice and dice data in real-time and create reports, graphs and dashboards to provide clearer, more accurate insights. Our cloud based platform is fully integrated with ThoughtSphere’s full line of solutions and can also be offered as a standalone solution.