Clinic Data Management
Accelerate your clinical trials with advanced data management
Structured and sorted data is the key to efficient research outcomes.
Achieve the best possible returns for your efforts invested in clinical trials using MarveLIS Clinical Data Management.
Clinical Data Surge, a new IT trend of the 21st century
Recent years have seen large volumes of data generated during Phase III clinical trials, approximately 3.6 million data points, a figure that is three times greater than it was 15 years ago.
This rapid increase is largely due to several factors:
- Extensive drug development efforts for rare diseases,
- Integration of biomarkers and genetic data,
- Proliferation of patient data sources, such as online questionnaires and wearable devices,
- Health-related R&D,
- E-data repository (electronic health records),
- Increasing levels of awareness in the population, etc.
Discover the power of Clinical Data Management (CDM) for profitable transformation.
MarveLIS Clinical data management (CDM) encompasses a range of practices designed to handle and oversee the data produced during medical research. Its primary goals are to maintain data quality, ensure integrity, and comply with both internal protocols and regulatory requirements.
CDM encapsulates efforts and coordination of different clinical trial stakeholders:
- Sponsors are the entities, such as pharmaceutical companies or institutions, that initiate, supervise, and fund the trials.
- CROs (Contract Research Organizations) are hired by sponsors to design and execute the study as per plan.
- Sites are the centers that are responsible for collecting and managing data from trial participants (samples).
Effective CDM is essential for assessing the safety and efficacy of various treatments (TB, Cancer, etc.), drugs, medical devices, digital therapeutics, and all preventive measures.
Mapping the effective strategy for managing your clinical data
MarveLIS Clinical Data Management (CDM) is a systematic and sequential multi process workflow across several interconnected stages. We strategize your data as per our predefined model and give our clients the most accurate and refined readings.
It focuses on creating the Case Report Form (CRF), which outlines the structure and content for data collection. It involves concomitant therapies, eligibility screenings, follow-ups, lab tests, medical histories, physical exams, randomization, etc.
It ensures accuracy, precision, and proper format and ordering of the collected data needed for the study.
The Database is structured to accommodate the study data. It involves setting up the database architecture, data entry fields, backend tables, etc., and ensuring the Database can handle study-specific data.
Mapping involves integrating data from various sources and formats into the Database to organize and present data in a manner that facilitates continuous reporting and analysis. It involves data entry assessment, automated edit checks, and testing to ensure data integrity is maintained during transfer and integration.
It covers the management of data collection during the study, including handling Adverse Events (AEs) and Severe Adverse Events (SAEs). It encompasses data entry, discrepancy management, data coding, ongoing quality control, and SAE reconciliation.
Post study, the Database is locked to prevent any further changes. It includes final quality control checks, final data review, database lock, archiving, sponsor submissions, database maintenance, and archiving.
A strong team is the backbone of your organization
Accurate Clinical Data Management is the direct responsibility of the whole team, which is achieved by proper coordination and cooperation among team members.
- Supervises the CDM team throughout the entire process and helps in correcting errors.
- Preside discussions related to data collection strategies.
- Cross examines the accuracy and integrity of the study data.
- Coordinates all data management activities and ensures smooth and optimized workflow.
- Handles, verifies, and validates data; checks compliance status with quality standards.
- Manages data validation processes and checks the implementation of quality control measures.
- Responsible for locking the Database after the study's conclusion.
- Annotates Case Report Forms (CRFs) for clarity and consistency.
- Designs and creates the study database to ensure it meets study requirements.
- Develops data entry screens to facilitate accurate data input.
- Programs and validates edit checks using dummy datasets to ensure data quality.
- Codes medical data, including adverse events and medical histories, to ensure standardized reporting.
- Designs the CRF and provides detailed CRF completion guidelines.
- Develops protocols for managing discrepancies to ensure data consistency.
- Conducts accuracy checks on data entry and performs comprehensive data audits to maintain data quality.
MarveLIS Data Management Plan (DMP)
Our Data Management Plan (DMP) is the blueprint for navigating the Clinical Data Management (CDM) lifecycle. It outlines all essential procedures, milestones, and deliverables, helping manage data efficiently and mitigate risks while keeping all stakeholders in the loop.
- Data Collection: The type of clinical data that will be gathered from trial participants is planned.
- Integration: A well-organized integration strategy for incorporating existing data.
- Formats: Standardized format structure for data mapping and other data operations.
- Metadata: Metadata standards and data practices.
- Storage & Backup: Regular alerts, notifications, and supervision for storing and backing up data.
- Security: Robust protocols to protect confidential clinical information.
- Quality Control: QC checks and orders for data quality management.
- Roles & Responsibilities: Who handles what within the team? The whole team structure is explained above.
- Access & Sharing: Rules for accessing and sharing of data internally and externally.
- Archiving: Long-term data preservation strategies.
- Costs: Budget maintenance for data management and archiving.
- Compliance: Adherence to domestic and international regulations and guidelines.
Remember, a DMP is a living document—hence, we keep it updated to reflect any changes throughout the trial.
MarveLIS Clinical Data Management System/Clinical Trial Management System
Clinical Data Management System (CDMS) is a specialized software solution that is designed to collect, manage, and analyze clinical trial data efficiently.
- Streamline data entry,
- Maintain data accuracy through validation checks,
- Facilitate real-time access for research teams.
By integrating features like electronic case report forms (eCRFs), automated data validation, and robust security measures, MarveLIS CDMS enhances data quality and productive outcomes.
It supports data integration from various sources (LIMS, EHR, etc.), improves data sharing and reporting, and offers tools for long-term data storage and analysis.
Clinical Data Management System equipments in working
Clinical Data Management System (CDMS) incorporates several crucial tools to optimize trial data processes:
- Electronic Data Capture (EDC): Platforms like Medidata Rave, Oracle Clinical, etc. streamline data collection by eliminating paper forms.
- Electronic Patient Reported Outcomes (ePRO): Allows patients to submit health related information in real time through devices like smartphones and tablets.
- Interactive Response Technology (IRT): Handles patient enrolment, randomization, and drug allocation during trials.
- Randomization and Trial Supply Management (RTSM): Ensures efficient management of patient randomization and investigational product distribution.
- Safety Gateway: Facilitates adverse event reporting, ensuring timely regulatory submission.
- Coding Application: Standardizes medical terminology for consistency in trials.
- Laboratory Data Integration: Integrates lab data seamlessly into the clinical trial system, maintaining data accuracy.
Contact Us
Request a Demo !
Unlock the full potential of MarveLIS with a personalized demo – where proficiency in lab management meets expertise in precision solutions
Address
Informics Digital Inc. East Windsor, NJ 08520 USA
contact@informicsdigital.com
FAQs
Clinical Data Management (CDM) involves some key tasks listed below:
- Study Protocol Review, understanding study requirements and designing data collection processes.
- CRF Design, developing Case Report Forms (CRFs) for collecting trial data.
- Data Collection, gathering data from participants via Electronic Data Capture (EDC) or paper forms.
- Data Entry, ensuring accurate input into databases.
- Data Cleaning, identifying and resolving data inconsistencies or errors.
- Validation Checks, running automated and manual checks to ensure data quality.
- Data Coding, standardizing terms like adverse events and medications.
- Database Locking, freezing the Database once data collection is complete for analysis.
Data Managers must have the following specialized skills:
- Strong understanding of clinical trial processes.
- Proficiency in Electronic Data Capture (EDC) systems.
- Knowledge of important regulatory guidelines (e.g., FDA, EMA, ICH-GCP).
- Attention to detail and data quality management.
- Expertise in data cleaning and validation.
- Familiarity with some basic coding standards (e.g., MedDRA, WHO-DD).
- Database management and SQL knowledge, Data Analytics.
- Project management and organizational skills.
- Problem-solving and critical thinking abilities.
- Effective communication and teamwork.
- Knowledge of data security regulations (e.g., GDPR, HIPAA).
- Adaptability to advanced technologies and practices.
The future of CDM is rapidly evolving with the integration of advanced and emerging technological interventions.
Artificial intelligence (AI) and Machine Learning (ML) will enhance data quality checks, automate data cleaning, and predict patterns and trends.
Cloud-based platforms will improve data accessibility, storage of large volumes of data, and cross-collaboration across global teams.
Blockchain can ensure data security and keep data encrypted to prevent data leakage.
Real-time data capture from wearable devices (IOT) and remote monitoring tools will expand data collection capabilities and will integrate new sets of data.
Regulatory requirements will push for more transparent and compliant clinical data handling.
As precision medicine advances, CDM will focus more on personalized data, requiring flexible systems to manage increasingly complex datasets.
Mention some CDM best practices.
Some CDM best practices are listed below:
- Leverage EDC systems, i.e., maximize efficiency and accuracy with advanced electronic data capture tools.
- Ensure data integrity through audits and checks.
- Follow regulatory compliance like- global standards like FDA, EMA, and ICH-GCP.
- Optimize data cleaning.
- Standardize coding and apply coding standards for consistency.
- Use encryption and access controls to protect sensitive data.
- Foster cross-functional communication for seamless operations.
- Ensure teams are skilled in the latest CDM technologies and methodologies.
