How to Create a Clinical Data Management Plan

How to Create a Clinical Data Management Plan

In Phase III clinical data management, a staggering 3.6 million data sets are collected, a threefold increase from a decade ago. The Phase III process has remained largely unchanged over the years. It commences before the approval of a medical treatment or therapeutic approach, extends over several years, and involves a workforce of potentially up to three thousand individuals.

Clinical data management quality control encompasses extensive drug development programs that focus on rare diseases and genetic data, all of which contribute to the realm of clinical data management. This guide aims to elucidate the concepts of user acceptance testing in clinical data management, edit checks in clinical data management, and the definition of clinical data management. Let’s initiate our exploration by clarifying what clinical data management entails.

What is Clinical Data Management?

Clinical data management (CDM) comprises a set of procedures that govern the handling of information collected during medical research. Its primary objective is to ensure the accuracy and reliability of data, as well as its alignment with organizational policies and government regulations. The CDM process also facilitates consistency among key stakeholders in clinical trials:

Sponsors:

These entities, such as pharmaceutical companies or agencies, initiate, oversee, and fund the trials.

Contract Research Organizations (CROs):
These organizations, hired by sponsors, are responsible for designing and executing the study.

Sites:

These locations are tasked with managing the data collection from trial participants.

Clinical data management plays a pivotal role in assessing the efficacy and safety of therapies, diagnoses, or disease prevention techniques. When executed correctly, it can significantly expedite the introduction of new medical products.

Steps and Processes in Clinical Data Management

The initial activities in clinical data management commence during the design phase of a clinical trial when a protocol outlining the study’s objectives and methods is created. Data-related tasks are typically divided among various roles, including:

Clinical Data Manager (CDM): Responsible for overseeing the entire CDM process and managing the involved parties.
Database Programmer or Designer: Works on database-related tasks.
Data Entry Experts: Handle the entry of data.
Medical Coders: Utilize industry-standard codes to record medical information, such as patient diagnoses, procedures, and complications.
Quality Control Professionals: Ensure data quality.

In the following section, we delve into the data management process.

1. Data Management Plan Design

Data management plans serve as a blueprint for the processes, activities, milestones, and outcomes of the CDM lifecycle. These documents guide data management and mitigate risks while keeping stakeholders informed about the trial’s progress. The Data Management Plan (DMP) must be completed as part of the trial’s design before enrolling the first participant. It ensures data is collected in a standardized format and cataloged appropriately. The plan remains adaptable and may require revisions as the trial progresses to accommodate new information or changing circumstances affecting healthcare data management.

2. Electronic Case Report Form (eCRF) Design

A case report form (CRF) is a questionnaire, either in paper or digital form, used to collect data from trial participants and transmit it to trial sponsors. Each study project has its unique CRF designed in compliance with:

The trial protocol.
Recommendations from the Clinical Data Acquisitions Standards Harmonization (CDASH), created by the Clinical Data Interchange Standards Consortium to facilitate communication across different sectors. For instance, CDASH may mandate a specific date format, like dd/mm/yyyy, for recording dates.

Data managers collaborate with database developers to design data entry screens and eCRF layouts. Multiple iterations of reviews typically occur before finalizing the design.

3. Clinical Trial Database Structure

Data collected during a clinical trial is organized into a database, formatted as rows and columns to align with the CRF structure. Some information from questionnaires can be categorized to reduce data storage requirements. Database design experts establish processes for translating or mapping codes into CRF components. Databases undergo testing using dummy data in a secure, non-research environment before being deployed in the production (study) environment.

4. Conducting Clinical Trials with Electronic Data Capture

CRFs serve as the primary means of gathering data in clinical trials. Typically, clinicians or data entry specialists collect participant information during office visits. In recent years, medical sites have transitioned away from being the primary data capture centers. Data from paper-based questionnaires requires manual entry into printed forms before being uploaded to the database. However, with electronic data capture (eCRF) systems, data is entered directly into computers, and some areas can be completed automatically, thanks to robotic process automation (RPA) systems that receive data from medical devices or Electronic Health Records (EHRs). Regardless of the method, data validation is imperative for all information entered into the forms and subsequently into the clinical database.

5. Data Validation: Source Verification, Data Anonymization, and Editing

Clinical data management validation encompasses a series of checks designed to detect issues related to data credibility, consistency, readability, or integrity. The validation procedures include:

Edit Check Verification: Database designers incorporate edit checks into eCRFs to compare entered data against predefined numerical and logical standards, reducing the likelihood of improbable values. For example, a system set to deal with temperatures in Fahrenheit may reject data falling outside the range of 95 to 105 degrees.

Source Data Verification (SDV): The SDV process verifies data entered into a CRF against the corresponding source documents, such as medical records. Its aim is to ensure that an eCRF accurately reflects a participant’s profile and includes all relevant data.

Data Anonymization: De-identifying clinical data is essential for Health Insurance Portability and Accountability Act (HIPAA) compliance before transmitting it to sponsors. This involves removing all protected health information (PHI) from the data.

6. Database Lock and Data Archiving

Upon completion of the study, the database is locked to prevent data tampering. Subsequently, the data, after thorough cleaning, is handed over to relevant parties for statistical analysis, reporting, and publication. Although not technically part of clinical data management, these processes are crucial. Evidence and trial documents must be retained for a minimum of three years, allowing for trial reconstruction and analysis to advance knowledge.

The Importance of Clinical Data Management

Spreadsheets and other office software are inadequate for handling the complexities of healthcare data management. Clinical data management systems (CDMS), also known as electronic data capture (EDC) systems, are indispensable due to their capacity to handle large volumes of data and their tailored design for medical research.

CDMSs can be customized for various applications, including psychiatric research, medical device clinical trials, and drug development. They share fundamental data management functionality.

1. Compliance with 21 CFR Part 11

In the United States, the regulation of food and medications falls under Title 21 of the Code of Federal Regulations (CFR). Part 11 of this act outlines guidelines for the use of information systems by FDA-regulated entities (the Food and Drug Administration). By adhering to the guidelines outlined in this document, you can ensure the security and traceability of your data. These guidelines include:

Controls to restrict access to the system and its contents to individuals with valid credentials (electronic signatures).
Mandatory periodic password changes.
Audit trails, which provide chronological records of all database activities and modifications.

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These measures are implemented to assure the system’s reliability.

2. eCRF Professional

This component offers a library of pre-built eCRF templates, data entry fields, and a user-friendly drag-and-drop interface for easy customization. Completed forms can be stored and reused in subsequent projects. The editor also supports programmable edit checks within the designer.

3. Query Management in Clinical Data Management

During clinical studies, sponsors may issue queries to researchers, seeking additional information. These queries are generated during data review, before database locking, and aim to rectify discrepancies or errors that are identified. Query management facilitates communication among data managers, sponsors, and other stakeholders.

4. Monitoring

Monitoring systems offer features such as setting alerts at predetermined intervals, recording adverse events, and directly transmitting alerts to sponsors.

5. Medical Coding Support

This module streamlines code searches, cross-coding system mapping, and error-checking processes.

6. Data Import and Export

Some systems facilitate the amalgamation of data from multiple sites. The data export function converts trial database information into formats preferred by analysts and mandated by research sponsors and regulatory bodies.

Tools Used in Clinical Data Management

1. IBM Clinical Advancement Tool

IBM Clinical Development (ICD) is a comprehensive cloud-based system tailored for large-scale decentralized studies capable of collecting data from diverse sources. It not only initiates the trial data management cycle but also generates analytical reports. ICD’s strengths lie in its reliability, extensive library of pre-built forms, user-friendly interface, and modular design, allowing users to select and pay for the features they require. IBM Watson’s AI capabilities assist in medical coding. It’s worth noting that initiating a trial may require a programmer, and some users have reported high ownership costs, limiting its accessibility to new ventures. Additionally, users have mentioned an outdated user interface and slow customer support.

2. Oracle’s Clinical Research Suite

Oracle’s Clinical Research Suite has a long-standing presence in the CDMS market, comprising three distinct yet complementary systems:

Oracle Clinical: Provides automated trial protocol creation, data validation, and report generation.
Oracle Remote Data Capture: An Electronic Data Capture system equipped with a layout editor for creating data collection forms.
Centralized Thesaurus Management System: Facilitates the integration of medical terminologies.

Built atop the Oracle database, this suite is known for its stability and effectiveness in manual data collection. However, it may not be suitable for startups with limited resources.

3. Castor EDC

Castor EDC is a cost-effective solution that accelerates trial setup. Its user-friendly interface allows for quick creation of electronic case report forms (eCRF), assignment of study roles, and addition of new users. Data from electronic health records (EHRs), medical devices, and wearables can seamlessly integrate with the system. Prompt assistance is available from qualified professionals during emergencies. The platform’s affordability makes it accessible to startups, although it may offer limited functionality and export options compared to more comprehensive and costly alternatives.

4. TrialKit Tool

TrialKit is a web and mobile application designed for virtual, decentralized research. It enables companies to create eCRFs without requiring coding expertise, expediting trial initiation. The software offers a collection of pre-made templates and a drag-and-drop CRF builder, allowing trials to commence within days. TrialKit seamlessly incorporates data from wearables, such as Fitbit and Apple Watch, as well as electronic health records. However, it has a steep learning curve and limited functionality.

Guidelines and Regulatory Requirements for Clinical Data Management

To ensure the success of your trial, consider the following tips for selecting and implementing a CDMS:

Evaluate Compatibility and Integration Options: Confirm that your chosen CDMS can seamlessly integrate with other platforms you plan to use throughout the trial, such as an EHR system or Laboratory Information Management System (LIMS). Engage a technology partner with expertise in system integration to address compatibility challenges.

Assess Vendor Support: Factors such as response time for issue resolution and the quality of user training fall under this category. Ensure that the service provider can assist with system implementation, integrations, and the development of new features as needed.

Solicit Stakeholder Feedback: Obtain input from Contract Research Organizations (CROs), sponsors, staff, and technical support at research sites before implementing a platform. Leverage free demos or trial versions to allow everyone to experience the user interface and provide feedback.

Prioritize Core Functionalities: Avoid unnecessary features that can inflate costs and complicate user adoption. You can expand capabilities as your project evolves by adding new modules from the same vendor, integrating third-party technologies, or employing custom programming.

Compare the CDMS with the eCRF: Verify the accuracy of the electronic forms through user acceptance testing after their development. End users, such as data entry experts, clinicians, and researchers, should assess the eCRF structure to ensure it includes all relevant variables.

Carefully Plan Database Setup: Balancing efficiency and data safety is crucial. Invest time in designing and testing the database before the trial to avoid costly and potentially invalidating software updates during the study.

Closing Remarks

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