Electronic Data Capture
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.
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, and today the market consists of a variety of new and established software providers, such as Phase Forward and Medidata Solutions.
In addition to pure software companies; pharmaceutical, biotech and contract research organizations have developed their own EDC systems.
The future of EDC
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. 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.