Clinical data management is a critical process that generates reliable, high-quality, statistically sound data that can be stored in the EMR systems. OSP’s clinical trial data management software can be designed to handle and store such gigantic amounts of information securely. We can build custom clinical data management solutions to manage data as per regulatory standards. Our CDM process can provide a user-friendly interface to empower healthcare professionals with routine tasks. It can ensure high data security and integrity for clinical research endeavors, including data management in clinical trials. Medical records management is daunting, but with OSP’s custom-built solution, you get the confidence of timely clinical trials and data collection.
OSP’s customized clinical data management software can offer an organized, structured repository for both clinical data systems and hospital management systems. We can offer tailored clinical data services where health data can be stored and consolidated from multiple clinical sources such as lab or EMR systems. It can provide the complete scenario of care a patient has received and helps health professionals to get an organized way to examine the data and make informed decisions.
We can create clinical database management system solutions to centralize your data. It can simplify the management and analysis procedure during the clinical trial. OSP’s efficient developers can tailor our clinical data management service to centralize data into one database or aggregate data from different systems into one reporting function. It can ensure improved data integrity, provide valuable insights, reduce data redundancy, and more. Our customized service can benefit sponsors in many ways, like accessing data in a consistent format.
Clinical trials are an expensive method of data collection. Our custom clinical data management solution can ensure that the collected data adhere to standards and guidelines defined by bodies like CDISC. It can boost the regulatory submission process. You get accurate and consistent data by using our custom software solutions for clinical data management. OSP’s custom clinical decision support system can ensure that the submitted data complies with the latest standards, thus increasing the possibility of successful approvals.
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We reached out to OSP to provide an estimate on a technology solution we were interested in developing. From the initial conversation, the team was professional, courteous, and thorough. We were able to make a quick decision to move forward with OSP because we were confident that our requirements were accurately captured and the development deliverables and associated costs were clear.
The OSP development team stayed on schedule and within budget throughout the build phase and provided weekly communications to keep our team informed along the way. If we require application development in the future, OSP will be the first call we make.-- Selita Jansen
We have worked closely with OSP for two years, meeting twice a week to work through development requirements, strategy, design, progress, and support. OSP has become an integral part of our business, and our mutual teams work together as one team. OSP tackles problems that arise with integrity and operate with respect for budgeting.-- Charlie Langdon
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Clinical data management (CDM) is a crucial process of clinical trials as it generates enormous high-quality data. It ensures data quality, reduces expenses, protects from data loss, provides excellent security, and more. It is essential for clinical research and healthcare settings because it directly affects the decisions related to treatment development and patients’ health. Hence, regulatory bodies maintain strict standards and guidelines to protect clinical data.
Edit checks in CDM are automated validations implemented within the clinical data management system (CDMS). It ensures that every data meets the defined rules. It helps in validating the reliability and quality of the collected data. Edit checks play a pivotal role in detecting inconsistencies in the data for the most accurate data collection and management.
The CDM process can be divided into 5 steps. The process starts when clinical data management solution providers generate a case report form (CRF) and ends when the database is locked. The five steps are CRF design, database design, data mapping, study conduct, and study closeout.
In medical abbreviation, EDC expands to mean Electronic Data Capture. It plays a vital role in the clinical trials process. It can collect data in electronic form, opposing the traditional paper form. EDC is a computerized system programmed for edit checks and source data verification. It ensures that data meets certain required quality, formats, ranges, etc., before it is accepted into the trial database.
Lab reconciliation is a crucial part of clinical data analytics. Usually, clinical data managers perform this process. Lab reconciliation compares the test results of laboratory data in the clinical database to the laboratory data in the sponsor’s laboratory database. Simply put, it is used to compare and correct any discrepancies between the two databases.
Clinical Data Management (CDM) and Electronic Health Record (EHR) systems are related but distinct concepts within the healthcare sector. CDM focuses on collecting, organizing, and validating data received from different clinical trials. It ensures accuracy and deals with both structured and unstructured data. On the other hand, Electronic Health Record (EHR) systems store comprehensive patient health information digitally. It is designed to offer a more comprehensive and interoperable approach, facilitating better data exchange, coordinated care, and patient engagement. They enhance data quality, support evidence-based medicine, and improve healthcare outcomes.
The key components of an effective clinical data management system (CDMS) can have 4 main stages: data collection, cleaning, healthcare analytics, and reporting. In the data collection stage, the clinical data manager collects accurate and complete data, which may involve an electronic database. In the data cleaning stage, the clinical data manager cleans data to ensure accuracy and completeness. It may include checking for discrepancies, errors, etc. Under the data analysis process, the manager analyzes data to assess the safety and efficacy of the investigational therapy. For the data reporting stage, reports are prepared and submitted to regulatory agencies.
The steps involved in the data capture and data entry process within CDM can be:
CDM ensures data security and compliance through stringent measures. It gives restricted access to authorized personnel only. Encrypted methods are used to safeguard data during storage and transmission. Regular auditing and monitoring is done to check for any anomalies. Compliance with regulations, like HIPAA and GDPR, is prioritized. Also, continuous staff training is provided to protect security protocols. One can even use a healthcare master data management solution for better results.
Clinical data integration combines different types of patient health data from various sources into a single, organized format. It can include EHR, EMR, health insurance claims, lab test results, and more. Data harmonization unifies disparate data formats, fields, columns, and dimensions into a combined dataset. It is a new approach to analyzing and visualizing data. In this way, they can facilitate the CDM process and ensure improved healthcare services.
CDM plays a vital role in regulatory approvals and clinical trial reporting. It ensures data accuracy, integrity, and compliance. It creates well-organized datasets, supporting statistical analysis and regulatory submissions. It follows a rigorous process of data cleaning to identify and rectify errors. Data reconciliation and validation checks are also done to identify consistency across various sources. A proper clinical trial management system also generates audit trails to track data modifications to ensure transparency. By following the above processes, it produces high-quality datasets that are essential for regulatory submissions. Hence, it results in successful approvals and meets reporting requirements for clinical trials.
Data archiving is moving older data that is not used actively to separate storage for long-term rendition. It is done to ensure that important old records remain available for years for future reference. The data archiving process may have 5 steps. First is inventorying and deciding where you decide which data should be archived. Retention policy is second, where you assign a retention schedule based on compliance regulations. The third step is to develop an all-inclusive archive policy where you can generate a manageable and enforceable document. Data protection is fourth, where you must be very particular in choosing which company will handle your client and sensitive information. This is because data archiving does not secure information at regulation requirement levels. The last step will be choosing the best data archive product or service. But before making a choice, have a checklist to ensure they can provide every feature you seek.
eSource refers to electronic source data from a variety of software systems. It is used to cut down the time spent collecting, uploading, scanning, and printing to initially recording in the electronic format. To efficiently manage electronic source data (eSource) in a CDMS, you must consider standardization, integration, and validation.
Query management in clinical management systems is identifying and resolving queries. Such queries can include questions or concerns about data accuracy or completeness. The process might include these 4 pointers: Identification of queries, Creation of queries to assign to the appropriate team to resolve them, resolution of queries which might be resolved within 48-72 hours of turnaround time, and documentation of query management activities to ensure regulatory compliance.
The strategies involved in CDM to ensure data accuracy and consistency can be standardizing data collection, training staff members, monitoring data quality, using Electronic Data Capture (EDC) systems, and conducting data cleaning procedures. These can ensure that your data is clean, accurate, and consistent.
To manage data from decentralized or remote clinical trial sites, the very first thing one should consider is to have clear communication channels. It can help in resolving miscommunication issues. Then, you must establish standard operating procedures (SOPs). This ensures consistency across the trial. Now, implement robust data encryption and transfer protocols for safeguarding sensitive information. A secure and compliant clinical trial management system is also essential to protect patients’ sensitive information. Regular virtual audits are also essential to maintain data quality and consistency.