Enhanced identification of eligibility for depression research using an electronic medical record search engine

Seyfried, Lisa; Hanauer, David A.; Nease, Donald; Albeiruti, Rashad; Kavanagh, Janet; Kales, Helen C.

doi:10.1016/j.ijmedinf.2009.05.002

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Volume 78, Issue 12 , Pages e13-e18, December 2009

Enhanced identification of eligibility for depression research using an electronic medical record search engine

Lisa Seyfried
- Department of Psychiatry, University of Michigan Medical School, United States
David A. Hanauer
- Department of Pediatrics, University of Michigan Medical School, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, United States
- Comprehensive Cancer Center, University of Michigan Medical School, United States
Donald Nease
- Department of Family Medicine, University of Michigan Medical School, United States
Rashad Albeiruti
- Department of Psychiatry, University of Michigan Medical School, United States
Janet Kavanagh
- Department of Psychiatry, University of Michigan Medical School, United States
- Serious Mental Illness Treatment Research Education and Clinical Center, Health Services Research and Development, United States
Helen C. Kales
- Department of Psychiatry, University of Michigan Medical School, United States
- Serious Mental Illness Treatment Research Education and Clinical Center, Health Services Research and Development, United States
- Geriatric Research Education and Clinical Center, VA Ann Arbor Healthcare System, United States
- Corresponding author at: Department of Psychiatry, University of Michigan, Box 5765, 4250 Plymouth Rd., Ann Arbor, MI 48109, United States. Tel.: +1 734 232 0388; fax: +1 734 615 8739.

Received 30 October 2008; received in revised form 28 April 2009; accepted 22 May 2009. published online 29 June 2009.

Abstract

Purpose

Electronic medical records (EMRs) have become part of daily practice for many physicians. Attempts have been made to apply electronic search engine technology to speed EMR review. This was a prospective, observational study to compare the speed and clinical accuracy of a medical record search engine vs. manual review of the EMR.

Methods

Three raters reviewed 49 cases in the EMR to screen for eligibility in a depression study using the electronic medical record search engine (EMERSE). One week later raters received a scrambled set of the same patients including 9 distractor cases, and used manual EMR review to determine eligibility. For both methods, accuracy was assessed for the original 49 cases by comparison with a gold standard rater.

Results

Use of EMERSE resulted in considerable time savings; chart reviews using EMERSE were significantly faster than traditional manual review (p=0.03). The percent agreement of raters with the gold standard (e.g. concurrent validity) using either EMERSE or manual review was not significantly different.

Conclusions

Using a search engine optimized for finding clinical information in the free-text sections of the EMR can provide significant time savings while preserving clinical accuracy. The major power of this search engine is not from a more advanced and sophisticated search algorithm, but rather from a user interface designed explicitly to help users search the entire medical record in a way that protects health information.

Keywords: Information storage and retrieval, Medical record systems, Computerized databases, Clinical research, Medical informatics