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Best Practices: The Electronic Medical Record Is an Invaluable Clinical Tool: Let’s Start Using It
Scott I. Vrieze, Ph.D.; Anna Docherty, Ph.D.; Paul Thuras, Ph.D.; Paul Arbisi, Ph.D.; William G. Iacono, Ph.D.; Scott Sponheim, Ph.D.; Christopher R. Erbes, Ph.D.; Wayne Siegel, Ph.D.; Jennie Leskela, Ph.D.
Psychiatric Services 2013; doi: 10.1176/appi.ps.201300272
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Except for Dr. Iacono and Dr. Vrieze, the authors are currently with the Minneapolis Department of Veterans Affairs (VA) Health Care System, Minneapolis, Minnesota, where Dr. Vrieze was affiliated when this work was done. Dr. Vrieze is now with the Department of Biostatistics, University of Michigan, Ann Arbor. In addition, Dr. Docherty is with the Department of Psychology, University of Missouri, Columbia. Dr. Thuras, Dr. Arbisi, Dr. Sponheim, Dr. Erbes, and Dr. Leskela are with the Department of Psychiatry, and Dr. Iacono and Dr. Siegel are with the Department of Psychology, all at the University of Minnesota, Minneapolis. Send correspondence to Dr. Leskela at Minneapolis VA Health Care System, One Veteran’s Dr., 116A, Minneapolis, MN 55417 (e-mail: jennie.leskela@va.gov). William M. Glazer, M.D., is editor of this column.

Copyright © 2013 by the American Psychiatric Association

Abstract

This column describes the potential of an enhanced electronic medical record (EMR) to advance best practices by displaying patient history, measuring progress, and facilitating clinical research. To create a graphical, single-page display of patient history, the authors examined data in the Minneapolis Department of Veterans Affairs EMR system, including 1.8 million encounters for 50,000 mental health patients. The prototype dashboard presents information on a patient’s current and past providers, diagnoses, therapeutic interventions, prescriptions, dosages, and outcomes. To provide needed outcome data to monitor patient progress, the authors tested two questions with 212 patients. Patient and clinician responses to the questions provide reliable and clinically useful data that can be used in the EMR to track patient change over time. Use of EMRs can bridge gaps between science and practice to inform diagnosis and treatment decisions and permit more accurate prognoses.

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Figure 1 Prototype dashboard for an enhanced electronic medical recorda

aThe dashboard is organized by month. In November 2012 this fictional patient was assessed (A) by Clinician B, given a medication evaluation (M), prescribed sertraline, diagnosed as having depression (d) and PTSD (p), and administered the Beck Depression Inventory (BDI-II) and Minnesota Multiphasic Personality Inventory (MMPI-2RF). The patient was then seen for depression by Clinician A. He did not attend his first appointment (n, no show) and engaged in therapy (T) around a week later. From November to March, he had diagnoses of depression and PTSD and experienced improvement (the running averages in the Outcome Data section increase over time for the two outcome measures). His BDI-II score decreased from 45 to 30. During that time, the medication was switched from sertraline to escitalopram. Despite ending treatment successfully in March, he was readmitted in June with prominent PTSD. His care was transferred to Clinician C, who provided psychotherapy, and Clinician D, who represcribed sertraline, eventually augmented with aripiprazole. The BDI-II was readministered, along with the PTSD Checklist (PCL). In August he was given a diagnosis of alcohol dependence (a) and given a naltrexone injection.

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References

Hamburg  MA;  Collins  FS:  The path to personalized medicine.  New England Journal of Medicine 363:301–304, 2010
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