EDC systems in clinical trials are used to collect and track patient data fast and efficiently. Nowadays, the demand for advanced EDC systems in clinical trials is growing.
EDC systems for clinical trials provide healthcare organizations with comprehensive data processing tools. The main reason for EDC demand is that clinical data volumes increase exponentially, and traditional approaches to process data can’t provide enough efficiency and accuracy. That’s why designing one could bring significant business outcomes. In this article, we outlined vital approaches to consider when building an electronic data capture system.
Electronic data capture system in clinical trials
An electronic data capture system is software which streamlines data processing in clinical trials. During clinical trials, researchers usually collect data on paper and then duplicate it into the system. Modern clinical trials have begun to use electronic records instead of conventional paper records. EDC facilitates data management in clinical trials and accelerates time to market for medications and medical devices. According to the systematic review of 53 studies,EDC is superior for pain-related data collection methods than conventional methods in efficiency and usability. It’s worth mentioning that 83% of interviewed patients state that the electronic data collection method is preferred to the conventional one. Scientists value technology for supporting data collection and result computation for clinical trials. Among the benefits of EDC there are:
Key EDC system features
EDC developers constantly work on their product enhancement to satisfy the market demand.
While organizations, who conduct clinical trials, may require specific functionality, core features stay the same. Electronic data capture systems typically consist of three key components:
- a graphic user interface,
- validation component,
- reporting and analysis tools.
These components usually are represented by a specific structure and provide exceptional functionality required by industry. Let’sLet’s explore EDC key features.
Case report form (CRF) is a questionnaire used in clinical trial studies. CRFs are specially designed for each clinical trial, and their structure is documented in the clinical trial’s protocol. eCRF designer allows researchers access legit form samples, which promotes data standards. Case report forms should have validation and edit check features to prevent users from entering invalid data. It’s also essential to provide a convenient user interface to facilitate data entry. For example, short and consistent forms are better than scrolled ones.
CEDC systems for clinical trials can validate entered data and record data entries that have issues, which are called data queries. Query management tool provides researchers with features to resolve data queries, eliminating the risk of invalid data affecting the study results. When there’s a data query caused by a user or computer mistake, the query management tool tracks it over time and provides several options to resolve the issue by:
- providing the correct value (handling errors),
- marking conflicted data as valid.
The user interface should be easy-to-understand and provide information about the type of error that occurred to facilitate error handling. For example, you can provide error tags that indicate empty fields, warnings, or invalid data. Query list should give the information about the error that occurred, such as:
- data query origin,
- reason of the query (invalid input, etc.),
- query status: need attention or resolved.
Exporting data from an EDC system is as important as entering it. The exported data should be readable and accessible on demand by authorized users. The EDC systems for clinical trials usually provide customized data exports in standardized formats such as CSV, SAS data sets, CDISC ODM. Besides, advanced EDC systems for clinical trials often provide visualization for reporting clinical data to facilitate data interpretation.
How to build EDC for clinical trials?
The electronic data capture system in clinical trials allows sites to collect data from the patients, track their visits and monitor data quality. It’sIt’s crucial for the EDC systems to be intuitive and straightforward in use to provide data quality and integrity and minimize data errors. Developers face several challenges concerning data collection quality and consistency, error handling, and system usability when building EDC systems for clinical trials. Poorly designed EDC systems can lead to data entry errors, slowing down clinical trials processes, and messing up results. That’sThat’s why developers should carefully plan the EDC systems design process, provide convenient features and data security.
Case report forms
The most fundamental unit in the EDC system is a case report form (CRF). CRFs define how the clinical data is arranged within the system and aim to collect complete and accurate research data, reducing the number of incomplete and excessive data. Perfect CRFs would collect only relevant data, which corresponds with the leading research goal. When designing electronic CRFs, it’s best to provide:
- CRF duplication reduction,
- built-in edit checks,
- instant query resolution,
- user-friendly interface.
Make sure most mundane tasks are handled automatically to reduce time and effort for data obtaining and clarification, thus enhancing a clinical trial regulatory submission and approval.
For an EDC system to be effective, a user interface that provides easy navigation through the system functionality and data is critical. The end-users of an EDC system are researchers, patients, and sponsor investigators. The EDC system interface should be easy and fast to learn so that each part of a clinical trial can quickly adapt to using the system. Here are some ideas you may consider for building an excellent user interface:
- Keep the layout simple with three or four main components.
- Add tips and notifications to help users learn EDC functionality faster.
- Don’t overcomplicate pages with bright colors and unnecessary UI elements.
- Provide routes to ease the navigation.
- Make data issue indicators as clear as possible.
The EDC system design inconsistencies can cause obstacles that slow down the workflow. For example, CRFs inputs should be automatically validated and show the information about incorrect data entry. On the other hand, overcomplicating the data entry process could lead to mistakes and deliberately entering inaccurate data. When the user faces too many requirements and rules, they can become frustrated and enter any data to proceed. That’sThat’s why an EDC system should have tools to provide flexible data management, boosted by field checks, automatic calculations, clarification tools, etc.
Reports and notifications
Сonvenient EDC systems in clinical trials should have reporting tools to provide clinical staff with essential data about the trial participants, research progress, etc. Usually, clinical trials require customized reports, so an efficient EDC system should have configurable reporting tools. For example, in clinical trials of drugs keeping track of adverse medication effects is crucial for pharmaceutical corporations, so keeping adverse events reports close at hand could prevent unwanted issues. Additionally, an EDC system should provide notifications to help researchers address issues in time while the trial is still on the go.
EDC system security
EDC systems in clinical trials work with Protected Health Information (PHI), including patients’ names, contact information, medical records, SSN, etc. Patients’ sensitive information and internal research insights could be corrupted by malefactors and used to manipulate sponsor investigators. Keeping EDC system records safe is a top priority for clinical trial organizations. So, each EDC system should implement data protection mechanisms such as:
- multi-factor authentication,
- access control,
- activity tracking,
- data encryption,
- data backup,
- IP-blocker, etc.
EDC system leading technologies
Many industries use artificial intelligence (AI) and machine learning (ML) to enhance the expected outcomes. Electronic data capture systems started to leverage AI and ML to improve data interpretation models and discover new information patterns. These technological advances make data cleaner and speed up the cleaning process.
It’s expected artificial intelligence and machine learning to become key EDC system market drivers, so using AI can be beneficial for your future EDC system.
Creating an advanced EDC system requires accurate preparations, planning, and resources. First and foremost, an EDC system should provide precise and efficient data collection tools embodied in CRFs. Besides clinical data accumulation, an EDC system allows researchers to manage and interpret data faster than traditional data capture approaches. To enhance the user experience within the EDC system, it’s best to add reporting tools and implement modern technologies such as artificial intelligence.
Regulatory requirements for EDC solutions
As we mentioned before, EDC systems for clinical trials work with patients’ sensitive data. It means the EDC software should comply with several legal regulations to provide data integrity and security. Besides, there are regulatory requirements for managing data in clinical trials. Here are key standards each EDC system should comply with:
- 21 Code Federal Regulations Part 11. These are regulations on electronic records and electronic signatures (ERES), established by the Food and Drug Administration (FDA). According to these regulations, an EDC system should provide data security by implementing access control, data encryption and backup, user action tracker, and more.
- GCDMP, or Good clinical data management practice, was established by The Society for Clinical Data Management. GCDMP recommendations concern database design for EDC systems.
- FDA Guidance for Industry: Computerized systems used in clinical investigations. This regulation instructs EDC systems for clinical trials to be as accurate as paper systems, comply with research protocol requirements and preclude errors in data management.
- GDPR or General data protection regulation establishes how to manage and protect personal data.
Besides regulatory requirements, each clinical trial has a Clinical Study Protocol, which EDC system developers should also consider. If an EDC system doesn’t comply with the protocol, there’s a risk of collecting inconsistent data and compromising the research results. To configure the EDC system properly, pay attention to
- clinical study design,
- visit schedule,
- inclusion/exclusion criteria,
- randomization type,
- laboratory data,
- LAE\SAE reporting.
Last but not least is the format of collected data. FDA requires data to be easily retrieved for investigations, so EDC system providers must ensure flawless data transfer by adhering to formatting requirements. Besides, clinical data should be prepared for statistical analysis and regulatory submission in the future to provide an efficient workflow. Key documents that provide format guidelines are established by Clinical Data Interchange Standards Consortium (CDISC).
Electronic data capture systems displace traditional paper-based data capture approaches. The survey shows that 73.9% of 194 organizations use two or more solutions, while 26.1% use only one. Although EDC systems for clinical trials decrease the time to market drugs and medical devices, there are still challenges associated with the EDC system’s usability. A thorough approach to developing and implementing modern technologies can provide a competitive advantage for EDC system providers. So if you are interested in developing an electronic data capture system in clinical trials, you know where to start.
What are EDC systems for clinical trials?
An electronic data capture system is software which streamlines data processing in clinical trials. EDC facilitates data management in clinical trials and accelerates time to market for medications and medical devices.
How is clinical trial data captured?
The most fundamental unit in the EDC systems for clinical trials is a case report form (CRF). CRFs define how the clinical data is arranged within the system and aim to collect complete and accurate research data, reducing the number of incomplete and excessive data.
What are the regulatory requirements for building an EDC system?
Here are key standards each EDC system should comply with:
- 21 Code Federal Regulations Part 11. These are regulations on electronic records and electronic signatures (ERES), established by the Food and Drug Administration (FDA).
- GCDMP, or Good clinical data management practice, was established by The Society for Clinical Data Management.
- FDA Guidance for Industry: Computerized systems used in clinical investigations.
- GDPR or General data protection regulation.