Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011 Jul;80(7):533-40.
doi: 10.1016/j.ijmedinf.2011.03.014. Epub 2011 Apr 22.

Using electronic medical records to determine the diagnosis of clinical depression

Nhi-Ha T Trinh  1 , Soo Jeong Youn, Jessica Sousa, Susan Regan, C Andres Bedoya, Trina E Chang, Maurizio Fava, Albert Yeung
Affiliations

Using electronic medical records to determine the diagnosis of clinical depression

Nhi-Ha T Trinh et al. Int J Med Inform. 2011 Jul.

Abstract

Objective: To investigate the validity of using electronic medical records (EMR) database in a large health organization for identifying patients with clinical depression.

Method: The Massachusetts General Hospital EMR system was used to generate a sample of primary care patients seen in the primary care clinic in 2007. Using this sample, the validity of using certain fields in the EMR database (i.e., billing diagnosis, problem list, and medication list) to identify patients with clinical depression was compared to primary care physician (PCP) assessment by a written questionnaire. Based on this standard, the sensitivity, specificity, positive predictive value, negative predictive value, and the areas under receiver operating characteristic curve (AUC) of three specific EMR fields - individually and in combination - were calculated to identify which EMR field best predicted PCP classification.

Results: The EMR fields "billing diagnosis", "problem list" and antidepressant in "medication list", were all able to identify patients' diagnosis of depression by their PCPs reasonably well. Having one or more "billing diagnosis" of depression had the highest sensitivity and highest AUC (77% sensitivity, 76% specificity, AUC 0.77) among any of the fields used alone.

Conclusion: The AUC for "billing diagnosis" of depression performed the best of the three single fields tested, with an AUC of 0.77, corresponding to a test with moderate accuracy. This analysis demonstrates that specific EMR fields can be used as a proxy for PCP assessment of depression for this EMR system. Limitations to our analysis include the physician response rate to our survey as well as the quality of the data, which is collected primarily for administrative and clinical purposes. When using administrative and clinical data in mental health studies, researchers must first assess the accuracy of choosing specific fields within their EMR system in order to determine the level of accuracy for them to be used as proxies for clinical diagnoses.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Classification of Patients by their Primary Care Physician
See this image and copyright information in PMC

References

    1. Seyfried L, Hanauer DA, Nease D, Albeiruti R, Kavanagh J, Kales HC. Enhanced identification of eligibility for depression research using an electronic medical record search engine. Int J Med Inform. 2009 Dec;78(12):e13–e18. - PMC - PubMed
    1. Kramer TL, Owen RR, Cannon D, Sloan KL, Thrush CR, Williams DK, Austen MA. How well do automated performance measures assess guideline implementation for new-onset depression in the Veterans Health Administration? Joint Commission Journal on Quality and Safety. 2003;29(9):479–489. - PubMed
    1. Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. International Journal of Medical Informatics. 2008;77:291–304. - PubMed
    1. Gill JM, Chen YX, Lieberman MI. Management of depression in ambulatory care for patients with medical co-morbidities: A study from a national electronic health record (EHR) network. Intl J Psychiatry in Medicine. 2008;38(2):203–215. - PubMed
    1. Folsom DP, Lindamer L, Montross LP, Hawthorne W, Golshan S, Hough R, Shale J, Jeste DV. Diagnostic variability for schizophrenia and major depression in a large public mental health care system dataset. Psychiatry Res. 2006;144(2):167–175. - PubMed