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A huge amount of data is generated through various phases of clinical research. The data has to be proficiently managed in order to draw relevant conclusions, file regulatory submissions and publish results and findings in research journals. Clinical Data Management (CDM) is the mainstay of clinical/medical research process needed for collection, interpretation and analysis of clinical data.
CDM is a specialized job which demands expertise in working with tools and knowledge of standard procedures and guidelines practiced by organizations dedicated to this field. Competency in medical writing with good statistical and medical knowledge provides an extra edge in collection, management and interpretation of data. Various modern and sophisticated practices involve the use of software designed exclusively for CDM and remote data capture (RDC), e.g. ORACLE CLINICAL, SAS clinical software and open source software like open CDMS(clinical data management services), Trial DB etc. Various available tools provide the basic help of on-site/remote data capture, managing, maintaining, retrieving data and even report generation. However, quality mainly relies upon how well the medical knowledge has supported the IT team at all stages of application development, data validation, analysis and interpretation reports. Therefore the quality of data and its applicability rest on the guidance of medical team.
After development of data management (DM) plan, first step that medical team performs is case record form (CRF) preparation. A concise and clear CRF is necessary for accurate data collection. Traditionally the paper version of CRF is used though electronic version also exists where investigator could directly enter the responses by accessing electronically. Once filled, CRFs are received and reviewed by data associates for any error or missing pages. Then data entry is done which could be made by two individual operators in order to negate the errors and omit any scribbled data. It is observed that data entry by two operators on paper CRF is more effective. Data once entered is tested for its synchronization with specifications of protocol and for inconsistency. Data discrepancy is checked by edit check programs, those might arise due to irregular data or any conflict with protocol.
Once identified, discrepancies are resolved by identifying their cause of origin, few of those which cannot be resolved are termed unworkable. This is most vital part of CDM (Clinical Data Management) process with the help of medical knowledge as it produces the clean data. Data is thus cleaned and discrepancy is stored in discrepancy database. Periodic reviews are made by CDM team to ensure that all the discrepancies are resolved. Coding is performed using electronic dictionaries to classify the reported terms on CRF into medical terminology. Two dictionaries are most widely used, World Health Organization–Drug Dictionary Enhanced (WHO-DDE) is used to code the medications while Medical Dictionary for Regulatory Activities (MedDRA) is used to code adverse events and pathological terms. After completion of all these tasks and ensuring resolution of data discrepancies, database is locked. Once locked, any modification in database is not possible hence an approval for locking should be seeked only after confirming that the data is completely clean.
At WorkSure™ we offer a specialized package for managing clinical data from development of data management plan to its extraction for analysis and final reporting, with supportive statistical and medical writing services. Thus WorkSure CDM team designs CRF, develops application, validates data by programming edit checks, codes adverse events and medical terminology, checks accuracy of data, resolves discrepancy, cleans and produces error free data. In the modern research oriented pharmaceutical industry, an efficient CDM is essential for skillful maintenance of data, enabling efficient regulatory submissions and interpreting clinical outcomes. Though advent of new tools and software provided new horizons to this field, medical knowledge lies in the core of CDM and data applicability. Implementation of new and updated guidelines has standardized the process and helped to regulate the practices as demanded by regulatory authorities.