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Clinical data management (CDM) is a critical process in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. [1] Clinical data management ensures collection, integration and availability of data at appropriate quality and cost.
In such CDMSs, the investigators directly upload the data on CDMS, and the data can then be viewed by the data validation staff. Once the data are uploaded by site, the data validation team can send the electronic alerts to sites if there are any problems. Such systems eliminate paper usage in clinical trial validation of data.
The Fast Healthcare Interoperability Resources (FHIR, / f aɪər /, like fire) standard is a set of rules and specifications for the secure exchange of electronic health care data. It is designed to be flexible and adaptable, so that it can be used in a wide range of settings and with different health care information systems.
Good clinical data management practice (GCDMP) is the current industry standards for clinical data management that consist of best business practice and acceptable regulatory standards. In all phases of clinical trials , clinical and laboratory information must be collected and converted to digital form for analysis and reporting purposes.
An electronic data capture (EDC) system is a computerized system designed for the collection of clinical data in electronic format for use mainly in human clinical trials. [1] EDC replaces the traditional paper-based data collection methodology to streamline data collection and expedite the time to market for drugs and medical devices.
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
Data verification helps to determine whether data was accurately translated when data is transferred from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss .