Remote Data Entry (RDE) System
A Remote Data Entry (RDE) System is a Clinical Data Capture System that is designed for the collection of data in electronic format.
- Context:
- It has been replaced by the Electronic Data Capture System.
- Example(s):
- …
- Counter-Example(s):
- See: Graphical User Interface, Data Validation, Direct Data Capture (DDC) System, Clinical Data Acquisition System, Clinical Trial, Clinical Trial Management System (CTMS), Interactive Voice Response (IVR) System, Software as a Medical Device (SaMD), Optical Character Recognition (OCR) System, eCOA) System, ePRO) System, eConsent System.
References
2021a
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Remote_data_entry Retrieved:2021-12-12.
- A remote data entry (RDE) system is a computerized system designed for the collection of data in electronic format. The term is most commonly applied to a class of software used in the life sciences industry for collecting patient data from participants in clinical research studies—research of new drugs and/or medical devices.
Typically, RDE 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
- RDE software was started in the mid- to late-1980s as software installed locally on portable computers with modems. It has largely been replaced by a newer generation of software called electronic data capture, or EDC, that provides the same type of functionality over the Internet using web pages.
- A remote data entry (RDE) system is a computerized system designed for the collection of data in electronic format. The term is most commonly applied to a class of software used in the life sciences industry for collecting patient data from participants in clinical research studies—research of new drugs and/or medical devices.
2021b
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Electronic_data_capture#History Retrieved:2021-12-12.
- EDC is often cited as having its origins in remote data entry (RDE) software, which surfaced in the life sciences market in the late 1980s and early 1990s. However, its origins might be tracked to a contract research organization known then as Institute for Biological Research and Development (IBRD). Drs. Nichol, Pickering, and Bollert offered "a controlled system for post-marketing surveillance (PMS) of newly approved (NDA) pharmaceutical products," with surveillance data being "entered into an electronic data base on site" at least as early as 1980.
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 (real-time), 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. This became expensive for sponsors and complicated for medical staff. 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.
- EDC is often cited as having its origins in remote data entry (RDE) software, which surfaced in the life sciences market in the late 1980s and early 1990s. However, its origins might be tracked to a contract research organization known then as Institute for Biological Research and Development (IBRD). Drs. Nichol, Pickering, and Bollert offered "a controlled system for post-marketing surveillance (PMS) of newly approved (NDA) pharmaceutical products," with surveillance data being "entered into an electronic data base on site" at least as early as 1980.
2000
- (Waterfield, 2000) ⇒ Emma Waterfield. "4. Data Capture" In: Clinical Data Management. Second Edition, 75-87. Edited by R.K. Rondel, S.A. Varley and C.F. Webb.
- QUOTE: Remote data entry means data capture at the point at which it is generated. If electronic transfer of the data to the sponsor's system occurs regularly, the data can be accessed and reviewed much more quickly than has been possible using traditional data capture methods. Crucially, the data can be validated as they are entered, by on-screen prompting, thus minimising errors early on in the process. The incentives for introducing remote data entry are therefore improved data quality, speed and flexibility of access to data, automatic avoidance of simple errors and early notification of error trends, which are all factors that can expedite time to database closure.