Electronic data capture
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An electronic data capture (EDC) system is a computerized system designed for the collection of clinical data in electronic format for use mainly in human clinical trials. EDC replaces the traditional paper-based data collection methodology to streamline data collection and expedite the time to market for drugs and medical devices. EDC solutions are widely adopted by pharmaceutical companies and clinical research organizations (CRO).
Typically, EDC systems provide:
- a graphical user interface component for data entry
- a validation component to check user data
- a reporting tool for analysis of the collected data
EDC systems are used by life sciences organizations, broadly defined as the pharmaceutical, medical device and biotechnology industries in all aspects of clinical research, but are particularly beneficial for late-phase (phase III-IV) studies and pharmacovigilance and post-market safety surveillance.
EDC can increase the data accuracy and decrease the time to collect data for studies of drugs and medical devices. The trade-off that many drug developers encounter with deploying an EDC system to support their drug development is that there is a relatively high start-up process, followed by significant benefits over the duration of the trial. As a result, for an EDC to be economical the saving over the life of the trial must be greater than the set-up costs. This is often aggravated by two conditions:
- that initial design of the study in EDC does not facilitate the decrease in costs over the life of the study due to poor planning or inexperience with EDC deployment; and
- initial set-up costs are higher than anticipated due to initial design of the study in EDC due to poor planning or experience with EDC deployment.
The net effect is to increase both the cost and risk to the study with insignificant benefits. However, with the maturation of today's EDC solutions, much of the earlier burdens for study design and set-up have been alleviated through technologies that allow for point-and-click, and drag-and-drop design modules. With little to no programming required, and reusability from global libraries and standardized forms such as CDISC's CDASH, deploying EDC can now rival the paper processes in terms of study start-up time. As a result, even the earlier phase studies have begun to adopt EDC technology.
EDC is often cited as having its origins in another class of software — Remote Data Entry (RDE) that surfaced in the life sciences market in the late 1980s and early 1990s. However its origins actually begin in the mid-1970s with a contract research organization known then as Institute for Biological Research and Development (IBRD). Dr. Richard Nichol and Joe Bollert contracted with Abbott Pharmaceuticals for the IBRD 'network' of Clinical Investigators to each have a computer and 'directly' enter clinical study data to the IBRD mainframe. IBRD then cleaned the data and provided reports to Abbott.
Clinical research data—patient data collected during the investigation of a new drug or medical device is collected by physicians, nurses, and research study coordinators in medical settings (offices, hospitals, universities) throughout the world. Historically, this information was collected on paper forms which were then sent to the research sponsor (e.g., a pharmaceutical company) for data entry into a database and subsequent statistical analysis environment. However, this process had a number of shortcomings:
- data are copied multiple times, which produces errors
- errors that are generated are not caught until weeks later
- visibility into the medical status of patients by sponsors is delayed
To address these and other concerns, RDE systems were invented so that physicians, nurses, and study coordinators could enter the data directly at the medical setting. By moving data entry out of the sponsor site and into the clinic or other facility, a number of benefits could be derived:
- data checks could be implemented during data entry, preventing some errors altogether and immediately prompting for resolution of other errors
- data could be transmitted nightly to sponsors, thereby improving the sponsor's ability to monitor the progress and status of the research study and its patients
These early RDE systems used "thick client" software—software installed locally on a laptop computer's hardware—to collect the patient data. The system could then use a modem connection over an analog phone line to periodically transmit the data back to the sponsor, and to collect questions from the sponsor that the medical staff would need to answer.
Though effective, RDE brought with it several shortcomings as well. The most significant shortcoming was that hardware (e.g., a laptop computer) needed to be deployed, installed, and supported at every investigational (medical) site. In addition to being expensive for sponsors and complicated for medical staff, this model resulted in a proliferation of laptop computers at many investigational sites that participated in more than one research study simultaneously. Usability and space constraints led to a lot of dissatisfaction among medical practitioners. With the rise of the Internet in the mid-1990s, the obvious solution to some of these issues was the adoption of web-based software that could be accessed using existing computers at the investigational sites. EDC represents this new class of software.
The EDC landscape has continued to evolve from its evolution from RDE in the late 1990s, leveraging the latest in internet-based technologies. Today the market consists of a variety of new and established software providers. Many of these providers offer specialized solutions targeting certain customer profiles or study phases. In addition to pure software companies; some pharmaceutical, biotech and contract research organizations have developed their own EDC systems. This practice, however, was more popular back in the mid to late 1990s, With the evolution of the EDC marketplace, developing customized solutions has given way to more commercial-off-the-shelf (COTS) solutions as technology has improved over the years.
At this time point more and more vendors are offering do-it-yourself EDC solutions where users can easily build their own eCRF. That way of working has some important advantages for the user:
- Time: with an EDC vendor it can take multiple weeks or even months before your study is ready, when you build it yourself it goes much faster.
- Price: when you build it, the price will be much lower.
- More flexibility
- Easy to manage the full process
As EDC software continues to mature, vendors are including capabilities that would have previously been developed and sold as separate software solutions: clinical data management systems (CDMS), clinical trial management systems (CTMS), business intelligence and reporting, and others. Efforts are being made to integrate payment execution tied to EDC data as well. This convergence is expected to continue until electronic patient medical records become more pervasive within the broader healthcare ecosystem—at which point the ideal solution would be to extract patient data directly from the electronic medical records as opposed to collecting the data in a separate data collection instrument. Standards such as CDISC and HL7 are already enabling this type of interoperability to be explored.
- Clinical data acquisition
- Clinical data management system (CDMS)
- Case report form (CRF)
- Remote data entry (RDE)
- Remote data capture (RDC)
- Patient-reported outcome (PRO)
- Title 21 CFR Part 11
- David Handelsman. "Electronic Data Capture: When Will It Replace Paper?". SAS Institute Inc. Archived from the original on 2009-12-17. Retrieved 2010-09-03.
- Thomas Bart. "Comparison of Electronic Data Capture with Paper Data Collection – Is There Really an Advantage?" (PDF). Business Briefing, Pharmatech. Archived from the original (PDF) on 2013-09-03. Retrieved 2013-02-25.
- Brigitte Walther; Safayet Hossin; John Townend; Neil Abernethy; David Parker; David Jeffries. "Comparison of Electronic Data Capture (EDC) with the Standard Data Capture Method for Clinical Trial Data". PLOS ONE. Retrieved 2013-02-27.