Every clinical trial generates data across dozens of forms, sites, and time points. How that data gets collected, cleaned, and transferred determines how much of your team’s time goes to administration versus science. The right electronic data capture (EDC) system removes friction at every stage. The wrong one creates it.
This guide covers what an electronic data capture system actually does, the five operational benefits that matter most for study teams, what to look for during evaluation, and how EDC fits into the broader architecture of modern clinical trial solutions.
What is electronic data capture?
An electronic data capture (EDC) system is software used to collect, clean, transfer, process, and store data in clinical trials. EDCs are used by contract research organizations (CROs), sponsors, and sites to conduct both simple and complex clinical trials in all phases of research. By using electronic records to capture and manage data on a digital platform, trial sponsors eliminate the need for traditional paper-based data collection. They can capture data securely and expedite the research process while ensuring data reusability.
Most EDC systems today are cloud-based so users can get secure access from anywhere. These systems collect varying types of data depending on the therapeutic area related to the clinical trial. For example, the data forms used in an oncology study might include:
- Adverse events
- Biochemistry
- Coagulation tests
- Concomitant medication
- Death information
- Demographics
- ECOG performance status
- Hematology
- Medical history
- Tumor assessments
- Treatment data
- Survival follow-up
- Urinalysis
- Vital signs
The benefits of EDCs in clinical trials
Today, clinical trials are typically expensive and time-consuming. Worse yet, they often create a burden for their participants. Trial sponsors are challenged to optimize every aspect of the research and development process to accelerate time to market, reduce the risk of failure, and retain participants along the way—all while staying on budget.
With the help of EDCs, sponsors can build clinical trials that meet the needs of regulators, payers, and participants. By using a foundational data platform that is flexible and interoperable, researchers speed up their research while lowering costs. Here’s how.
1. Incorporate real-world data (RWD) collection
An EDC solution simplifies and streamlines data collection. Once the data collection phase is complete, statistical processing can happen quickly. An EDC supports real-world evidence collection by:
- Ensuring that data is accurate and appropriate through inbuilt plausibility checks.
- Supporting remote patient participation.
- Allowing researchers to add constraints to a form to prevent inaccurate or illogical values from being entered.
- Supporting source data verification and edit checks to ensure data meets requirements for ranges and formats before it’s accepted into the trial database.
- Reducing data entry time by eliminating most paperwork.
Real-life example: COVID-RED
Julius Clinical’s COVID-RED (Remote Early Detection) study used wearable sensor technology and a smartphone app to measure participant vitals and provide personalized advice to get tested for COVID-19, even before symptoms appeared. The trial sponsors knew recruiting and collecting data from their 20,000-participant target would be incredibly challenging under ordinary circumstances—it would require multiple sites, complicated administrative processes, and in-person participant visits. And with 7,000 participants from a high-risk group safety during the pandemic was an issue.
Julius Clinical partnered with Castor to build an eClinical platform to run their trial remotely from start to finish. Through Castor’s clinical trial platform, over 17,800 participants were engaged and retained remotely, while data was automatically collected from thousands of medical devices and pushed into Castor EDC. According to the team at Julius Clinical: “Castor’s infrastructure ensured we were able to effectively carry out this study while keeping administrative burden, costs, and losses in participant engagement to a minimum.”
2. Directly capture global data
Clinical trials can be a global affair, with participants and researchers spread throughout the world. An EDC supports accuracy in international clinical studies through features like legible entries and automatic calculations. EDCs support global clinical trials by:
- Offering data entry and access anywhere, anytime.
- Using medical coding so that all symptoms and names of diseases are coded according to international terminology.
- Identifying and resolving data discrepancies at remote sites through digital tools.
- Allowing data managers to easily review data and quickly issue queries to sites to solve discrepancies.
- Detecting and correcting uncertain data with numerical data validation.
Real-life example: WHO Solidarity Trial
To combat the largest pandemic in recent history and generate sufficient evidence of the efficacy and safety of potential treatments, the World Health Organization (WHO) needed to mount a massive clinical trial.
The WHO needed a user-friendly system that they could deploy globally with 24/7 support. It needed to be advanced enough to support adaptive medication allocation and real-time reporting. Finally, offline data capture was a requirement due to unreliable internet in remote locations. The WHO approached Castor to help solve these challenges and quickly selected Castor as its sole EDC provider for the trial.
Castor quickly implemented a CRF in six languages, developed an adaptive randomization algorithm fitting WHO specifications, developed an offline capture tool for areas with poor telecom infrastructure, and provided 24/7 support.
“Launching and executing this ongoing trial is a remarkable achievement. In 6 months, the trial team accumulated information on the collective experience of more than 11,000 hospitalized patients in settings with varied and evolving standards of care.” – New England Journal of Medicine
3. Automate for the next generation of trial designs
Gone are the days of tedious manual data entry. Automation within clinical trials supports faster research and can speed up the return on investment of a trial. Automation within EDCs can benefit researchers by:
- Supporting remote monitoring processes (significantly reducing costs).
- Allowing market authorization to happen as quickly as possible.
- Enhancing the efficiency and productivity of a trial, further reducing the overall cost of clinical trials.
Real-life example: AusculThing
AusculThing seeks to transform the way healthcare providers detect heart murmurs by enabling them with a far more precise tool than the human ear. To train the algorithm to work more effectively, they would need to collect an enormous amount of audio and diagnosis data across 5 different sites.
The AusculThing team turned to Castor’s eSource platform, a system that would enable them to collect, capture, and process a significant amount of patient data from several sources, and feed it directly into Castor’s EDC. By the end of the trial, researchers had collected audio data recorded by a digital stethoscope and additional diagnosis info from over 1,700 patients – all without the need for manual source data verification. With data that’s healthy, integrated, and accessible through one interface, AusculThing can now train its AI algorithm much more efficiently and make considerable strides in creating an auscultation system that’s more accurate and efficient for healthcare systems worldwide.
4. Streamline collaboration workflows
Throughout studies, trial teams, monitors, and auditors need to capture, process, and access data from anywhere, at any time. EDCs allow data to be quickly shared with relevant personnel, enabling efficient exchange of information by:
- Supporting real-time data access while limiting the time that has to be spent on query management.
- Allowing authorized personnel to access, download, and print status reports at any point.
- Supporting decision-making and adaptive trial designs with easy access to data insights.
- Offering search options so users can easily find and filter the exact information that they need.
- Storing everything in one central location that provides greater visibility.
- Linking data collected in eCRF from one form to another for analysis.
- Requesting only the data needed for a particular patient’s circumstances at a specific time.
Real-life example: RSP Systems—Using Monitoring to Track Study Progress
Over the past few years, RSP Systems has worked to bring their GlucoBeam monitor to market as a non-invasive and convenient glucose monitoring technology for diabetes patients. To do so, they’ve conducted multiple small clinical trials testing various settings on the device to improve protocols, estimate the signal-to-noise ratio, and increase the accuracy of the measurements. But the success of their studies depended on gathering highly accurate data ready for analysis. And with multiple trials running—some multicenter—they needed a bird’s eye view of all their studies with minimal manual effort.
Using Castor EDC, RSP Systems can now capture clinician, patient, device, and any other external data and get a complete overview of all the data linked to an individual patient. In particular, they’ve made great use of the monitoring feature in Castor EDC for a comprehensive overview of all the queries, data validations, and dropped verifications in each of their studies. They could find all the active validation fields in their study forms, reports, or surveys and easily filter by the type—Exclusion, Warning, and Message. From there, they could find out from which site the validation errors originated, compare the field values with the reference values defined during the validation setup, and jump to the step with the validation errors.
5. Manage mid-study changes with ease
EDC systems are flexible and adjustable, helping to meet the needs of the study as they change throughout the process. They support mid-study changes by:
- Allowing feedback from study monitors and participants right away.
- Enabling protocol amendments midway through the study.
- Providing instant eCRF updates across all sites of a multi-site (or even multi-continent) study.
- Using data traceability and access controls to maintain a full audit trail.
- Using designated permissions with most actions carried out by specific roles, further ensuring data security.
Real-life example: AKRN Scientific Consulting
AKRN Scientific Consulting joined forces with a medical device manufacturer to innovate a way to keep a donor heart oxygenated in transport to its recipient. Together, they designed a clinical study to test their transportation solution. They then partnered with Castor to help design a clinical research ecosystem that would keep up with the evolving needs of the study.
As the randomized clinical trial expanded into several sites in different countries, study managers wanted to incorporate feedback from various study monitors and participants while still accommodating the varied regulatory and clinical needs. The protocol needed to be a living document that could evolve, reflecting the advice and parameters of each site. Fortunately, Castor’s eCRF made this possible, allowing changes to be quickly programmed and automatically uploaded to each site. As a result, each site always had the most up-to-date version of the eCRF.
What features should you look for in an EDC?
While choosing an EDC is a significant investment of both budget and time, the operational savings over the course of a study make it well worth the evaluation effort. Here’s what to prioritize:
- A user-friendly interface that supports collaboration between multiple sites and researchers globally.
- Auto checks and data limits to reduce errors before they become queries.
- Guaranteed compliance with privacy and data protection regulations.
- Native integration with existing tools (for example, wearables, eCOA and ePRO solutions, or legacy systems).
- The ability to reuse data collected from the trial for future studies.
- Technical advisors to help set up and offer guidance at every stage.
- An eCRF designer with options for reusable templates and built-in edit checks.
- Both auto-generated and manual queries.
- Built-in metrics reporting to surface insights throughout the study.
How EDCs fit into the future of clinical research
Currently, clinical trials use isolated technology tools—such as eCRFs—to collect, manage, and analyze data. eCRFs are study forms built into EDCs that let researchers and clinical staff enter data directly into the system. These digital, typically web-based questionnaires ensure data compliance with privacy, security, and Good Clinical Practice regulations. Researchers can tailor eCRFs to fit each study, saving time and money.
While eCRFs represent a meaningful improvement over paper-based data entry, they remain a component within a broader, connected ecosystem rather than an end in themselves. The clinical trial industry is moving from siloed, cumbersome processes to decentralized clinical trials and fully connected, interoperable platforms for collecting and analyzing data. That shift involves pairing an EDC with the broader eClinical stack—eConsent, scheduling, televisits, and data from devices, consumer wearables, and patient apps.
An API architecture setup allows data collection from almost any source, and the goal is straightforward: a single system to collect, store, and process all clinical trial data. Therapy breakthroughs happen faster when researchers have access to affordable, user-friendly, and compliant data solutions. In just a few clicks, researchers can build a study with Castor’s clinical trials platform—without any prior technical knowledge—and grow alongside Castor as they move into fully integrated trials.
Frequently asked questions about EDC systems
What is the difference between an EDC system and a CDMS?
An EDC (electronic data capture) system is the tool used to collect and clean clinical trial data at the point of entry. A CDMS (clinical data management system) is the broader infrastructure that manages data across the full lifecycle of a study, including storage, processing, and export. In modern platforms, the two functions are typically combined: the EDC captures data from sites, devices, and patients, while the CDMS layer handles validation, query management, and data lock.
How does an EDC integrate with ePRO and eCOA tools?
Modern EDC platforms connect to ePRO and eCOA solutions via API, allowing patient-reported data to flow directly into the trial database without manual transfer. The EDC applies the same edit checks and data validation rules to patient-entered data as it does to site-entered data, maintaining a single audit trail. This is especially important in studies where patient-reported outcomes are part of the primary or secondary endpoint package.
What should I look for in an EDC for a decentralized or hybrid clinical trial?
For decentralized clinical trials, the critical EDC requirements are: remote participant access for direct data entry, device and wearable data ingestion via API, offline data capture capability for sites with unreliable connectivity, and native ePRO/eCOA integration. Additionally, look for built-in eConsent workflows and the ability to configure site-specific data collection rules without rebuilding the entire study structure.
What data standards should an EDC support for regulatory submissions?
For FDA and EMA submissions, CDISC standards are the primary requirement: CDASH for data collection forms and SDTM for data submission. Your EDC should either natively produce SDTM-compliant outputs or integrate with a data transformation layer that maps from the collection format to submission format. Systems that build CDASH compliance into the eCRF design stage reduce the transformation burden significantly at data lock.
How does an EDC reduce data errors and query burden in clinical trials?
EDC systems reduce errors at the source through built-in edit checks, plausibility constraints, and range validations that flag problems the moment data is entered—before it becomes a query. Auto-generated queries notify data managers and sites in real time, while medical coding standardizes terminology across sites and countries. The result is fewer discrepancies at database lock and less time spent on manual query resolution during the study.