An electronic medical records system for clinical research and the EMR–EDC interface
This is a review for Elizabeth C. Murphy, Frederick L. Ferris, III, and William R. O’Donnell's An Electronic Medical Records System for Clinical Research and the EMR–EDC Interface 
As the use of medical records continues to expand among clinics and hospitals, there is growing research into the use of this collected data for clinical research. This article seeks to explore the practicality of implementing EMR systems for clinical research.
The authors worked in partnership with an ophthalmology clinic in order to develop a new system to work in conjunction with their current EMR.
The resulting system functioned properly and in many instances improved on the current EMR of the clinic in correlation with EDC and used successfully in practice research protocols.
EMR usage in clinical setting is ever increasing and the demands for practical research along with it. This article has provided an example of a successful method of using such systems for research protocols as well as clinical trials.
My only critique to this article is that is structured in a way that it is difficult to attain important concepts. However, the article is successful in its intentions of providing a detailed process of establishing an interface useful for both clinical and research purposes.
A similar article regarding the use of EMR in research aspects is Electronic medical records for clinical research: application to the identification of heart failure
Based upon the report by Institute of Medicine and safety initiatives promoted by Leapfrog group, the Han et al wrote that, Department Critical care Medicine, University of Pittsburg School Of Medicine, Pittsburg, PA, implemented a commercially sold computerized physician order entry (CPOE) systems. Accordingly, the attempt was an effort to reduce medical errors and mortality rate. The idea behind the implementation was to test the hypothesis that CPOE results in the reduction of mortality rate among children who are transported for specialized care. 
During the planning phases, we evaluated companies that provided software specifically for ophthalmology. After a complete review, the NextGen product was selected for its flexibility in template construction and design as well as its database capabilities. To enable speed of use, information can be compartmentalized, with details hidden and only pertinent information displayed.2 Although generally each screen corresponds to a database table, NG EMR provides the ability to display elements from one screen (table) on another screen. This allows the details to be hidden and enables the researcher to link to the information pertaining to a particular study, without navigating the entire system. By collecting the data using the Early Treatment of Diabetic Retinopathy Study (ETDRS) visual acuity protocol and chart in a subform (or pop-up as the system calls them) and displaying only the total letters and maximum line reached on the main form, both goals can be accomplished (Fig. 1) without complicating the main screen. Data elements are grouped by part of the eye, and those specific elements are hidden in pop-ups, while the top-level screen only displays the systems that were normal and those that had remarkable findings (Fig. 2). In this manner, the physician can quickly and easily enter the part of the eye where there are pertinent findings to record, without having to page through hundreds of elements in parts of the eye that were free of disease. The ability to jump quickly to a subset of elements and to record the pertinent findings enables us to add substantially more data items without increasing the time it would take to collect the data. We create specialized templates for prospective studies, which allow us to add the study-specific elements as well as display the standard elements, which are outcomes for the study. A single tool, Cognos Impromptu, is used to extract data for research analysis. This tool can be used to extract data in a planned format for prospective studies, and the resulting datasets can be transmitted to our data coordinating center for quality control purposes or data analysis. A second data-extraction tool, Crystal Reports, is used to generate an encounter note that can be filed in the patient’s medical record and sent to referring physicians. Elements that are recorded as positive in the universal system can be translated to positive on the study-specific screen. Outcomes that are negative will have to be specifically marked as such, rather than assuming that they are negative from inaction. It is the data from the study-specific screen that is then submitted to the study’s data-coordinating center or statistician for analysis, whereas the data on the standard screens remain a part of the universal dataset and are available as part of that collective for retrospective studies.
They presented an example of a functional integration of EMR and EDC that eliminates transcription errors, facilitates retrospective study analysis, and serves as the primary resource for clinical care. Although their scenario may not work for all practices or centers, it is an example of a successful method of implementing an EMR designed for clinical research and of integrating the EMR with an EDC system for research protocols and clinical trials.
I found it interesting that they accomplished their goal, however the system was highly specific to their centers. This just goes to show the fundamental challenges facing interoperability.
- Murphy, E. C., Ferris, F. L., & O’Donnell, W. R. (2007). An Electronic Medical Records System for Clinical Research and the EMR–EDC Interface. Investigative Ophthalmology & Visual Science, 48(10), 4383–4389. http://doi.org/10.1167/iovs.07-0345 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2361387/
<ref>tag; no text was provided for refs named
Cite error: Invalid