Leveraging of Open EMR Architecture for Clinical Trial Accrual

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This is a review of Afrin, Oates, Boyd, and Daniels (2003) article, Leveraging of Open EMR Architecture for Clinical Trial Accrual. [1]

Research Question

The purpose of this study is to leverage an open architecture EMR system to remove barriers for mass-screening of potential research participants. The authors worked with the institutional review board to design a prototype solution.


Identifying potential research subjects for clinical trials creates a bottleneck in the scientific process. The authors developed a prototype for a local investigator-initial trial using an open architecture system to search the clinical data repository for possible subjects. The study was conducted over 11 months. The system automatically screened 7,296,708 lab results from 69,288 patients. The system detected 1,768 lab tests of interest, which lead to the identification of 70 potential candidates for the research trial. This lead to the accrual of 3 research participants. The authors were disappointed with the accrual rate. The results were partially due to clinicians’ lack of response to alerts.


The trial was conducted at MUSC for lupus nephritis patients with diagnostic tests signifying proteinuria and a positive anti-nuclear antigen antibody or a positive anti-double-stranded-DNA antibody. MUSC uses the Oacis system. The system captured demographic, encounter data, diagnostic test results, provider notes, and other clinical data for half a million patients over 10 years. The system has an open architecture design allowing for the development of a code to search test results using a Perl script and the Sybase stored procedure. Email and pager notifications were sent to attending physicians when a patient met criteria. The attending physicians received no education, training, or other advanced notice about the experiment.


Cerner’s lab information was accessed to screen 7,296,708. The screening system identified 1,768 tests for relevance. These test results triggered the identification of 70 patients. This should have triggered emails to all 70 patient’s attending physicians. However, the email addresses of several attending physicians were missing. Because of this issues, the notifications were sent to the principal investigators in addition to the ordering physicians. A total of 70 notification were sent to investigators and 64 notifications were sent attending physicians. These notifications lead to enrolling 3 patients.


Using an open source EMR system to screen potential subject can prove useful in the initial screening, if the system for screening complies with current clinical research ethics and regulations. There is the potential to use Natural Language processing of free-test clinical reports to identify trial candidates in future studies. However, there is limited research in the literature demonstrating the use of automated mass-screening of electronic clinical data for research purposes. In the present study, many possible participants were ineligible upon review. The authors were disappointed with the low accrual rate, but the system demonstrated its ability to significantly increase the number of patients screened for the trail. One issue presented in the study is the poor follow-through by ordering physicians. This contributed to the disappointing results. One potential reason for poor follow-through was the time required by the attending physician to interact with patients regarding clinical trial. A possible solution is to provide patients with a website for trail information or to directly notify the patients of possible study eligibility. Another issues identified was the study is limited amount of patient information that could be included in the alerts due to IRB mandates. In summary, the study demonstrated that an open architecture clinical data repository can be leveraged to screen more potential research participants than could be achieved without the system. Future studies should focus on refining the screening method to make it more generally available to the clinical research community.


There is possibility of resolving the bottleneck created by the need to screen a very large number of patients for possible trial eligibility using an open-source EMR system. This bottleneck is growing as the number of ongoing clinical trials increases year by year. Furthermore, costs and time associated with screening research participants is not insignificant. Thus, an open-source EMR system that is capable of automatically screen patient’s electronic data can provide a less expensive and quick solution. Further refinement and research is necessary to resolve issues related to low follow-through rates by message recipient.

Related Links

  1. EMR
  2. Open_source_EMR_software:_Profiling,_insights,_and_hands-on_analysis
  3. Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS
  4. Open-source health information technology: A case study of electronic medical records


  1. Afrin, L. B., Oates, J. C., Boyd, C. K., and Daniels, M. S. (2003). Leveraging of Open EMR Architecture for Clincial Trial Accrual. AMIA Annual Symp Proc. 2003, 16-20. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1480210/