Clinical Data Management (CDM) holds the entire life cycle of clinical data from its collection to exchange for statistical analysis in support of performing regulatory activities. It primarily focuses on data integrity and dataflow. Clinical Data Science (CDS) has expanded the scope of CDM by ensuring the data is reliable and credible. Risk-based data strategies are essential to consider as the most important component in the automation of clinical data management. Other solutions include identifying sites for clinical trials, targeting the right audience, recruiting the right patients, collecting reported outcomes, obtaining digital consent, remotely screening patients, and conducting decentralized trials.
Not all data collected is useful for statistical or other analysis. There has been a steady increase in data volume; CDM can ensure which data needs to be collected to support further analysis. CDM is responsible for generating structured and unstructured data from various sources and transforming that data into useful information. Generating, integrating, and interpreting different data type new data technology strategies. Take Clinical Research Course from the Best.
Sponsors have incredibly increased the use of healthcare apps and digital health technologies to collect other real-world data (RWD) and reported outcomes. Over 200 new health apps are added every day to app stores. Phase IV is most likely of all clinical trial phases to witness experiments with digital health. However, this is unfortunate since it can improve the efficacy of clinical research trials in various ways.
Automation of clinical data management presents myriad possibilities for clinical research trials. Streamline clinical trial management, enhance data collection, analysis, and sharing, better matching of eligible patients with trials, and an overall improvement in experience for all stakeholders are some ways suggested and tested strategies. Still, a lot still needs to be done to enhance and maximize the benefits of automation. Take the Best Training in Clinical Research.
Currently, electronic health records (EHRs) and electronic data capture (EDC) can rarely be integrated. The problems of exchange and the non-standardization of data should be solved for the clinical research industry to achieve the full potential of automated processes.