June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Best Practices for Querying Electronic Ophthalmology Health Records for Potential Clinical Trial Participants
Author Affiliations & Notes
  • Katarzyna Brodowska
    Ophthalmology, Case Western University, Seven Hills, Ohio, United States
  • Mahdi Rostamizadeh
    Ophthalmology, Case Western University, Seven Hills, Ohio, United States
  • diane weiss
    Retina Associates of Cleveland, Cleveland, Ohio, United States
  • Lawrence J Singerman
    Retina Associates of Cleveland, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Katarzyna Brodowska, None; Mahdi Rostamizadeh, None; diane weiss, None; Lawrence Singerman, Alcon (F), Ophthea (F), Tyrogenex (F)
  • Footnotes
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Investigative Ophthalmology & Visual Science June 2017, Vol.58, 1958. doi:
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      Katarzyna Brodowska, Mahdi Rostamizadeh, diane weiss, Lawrence J Singerman; Best Practices for Querying Electronic Ophthalmology Health Records for Potential Clinical Trial Participants. Invest. Ophthalmol. Vis. Sci. 2017;58(8):1958.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To explore best practices for querying MDI (MDIntelleSys, Nexttech Systems LLC)
Electronic Medical Record (EMR) Software for Ophthalmology Practice for identifying lists of potential clinical trial participants with exudative age-related macular degeneration (AMD) who are likely to be eligible to participate.

Methods : Inclusion and exclusion criteria for two clinical were assembled in a crosswalk table and compared. Next, each criterion was evaluated for its ability to be operationalized within the MDI software. For those that could be operationalized, specifications of how to program the query were developed. Finally, a decision-tree chart was constructed to guide the programmer as to applying the criteria from the studies to the data in the MDI EMR in a hierarchical fashion so as to develop lists of participants with a high likelihood of qualifying for one or more of the trials.

Results : Assembling the screen shots from the MDI EMR was necessary to assist in operationalization of the criteria and to build the crosswalk. Approximately 40 hours was devoted to this effort over several weeks. Careful consideration of each criterion revealed that many criteria would need to be evaluated manually, as they were not available with sufficient accuracy in the MDI EMR. However, many criteria, such as injections received, diagnosis, interval of injections, visual acuity, could be coded in multiple different ways in the MDI EMR; a complex query algorithm was required for these. Other criteria, such as disqualifying non-ophthalmologic co-morbidities, were not always recorded in the MDI EMR, and would need to be evaluated manually. Finally, a decision-tree was formulated to guide the programmer as to the execution of the MDI EMR query in steps so as to produce two lists of potential participants likely to qualify for each study, respectively.

Conclusions : Comparing trials for eligibility criteria and developing a query plan for an MDI EMR system in ophthalmology to produce a list of potential clinical trial participants who are likely to qualify can be cost- and time-effective if the trials have very similar criteria, and if most of them can be evaluated by querying the MDI EMR. Developing clear query specifications to provide the MDI EMR programmer will also likely increase the accuracy of and the efficiency in producing the potential participant lists.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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