Title |
Measuring Use of Evidence Based Psychotherapy for Posttraumatic Stress Disorder in a Large National Healthcare System
|
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Published in |
Administration and Policy in Mental Health and Mental Health Services Research, February 2018
|
DOI | 10.1007/s10488-018-0850-5 |
Pubmed ID | |
Authors |
Shira Maguen, Erin Madden, Olga V. Patterson, Scott L. DuVall, Lizabeth A. Goldstein, Kristine Burkman, Brian Shiner |
Abstract |
To derive a method of identifying use of evidence-based psychotherapy (EBP) for post-traumatic stress disorder (PTSD), we used clinical note text from national Veterans Health Administration (VHA) medical records. Using natural language processing, we developed machine-learning algorithms to classify note text on a large scale in an observational study of Iraq and Afghanistan veterans with PTSD and one post-deployment psychotherapy visit by 8/5/15 (N = 255,968). PTSD visits were linked to 8.1 million psychotherapy notes. Annotators labeled 3467 randomly-selected psychotherapy notes (kappa = 0.88) to indicate receipt of EBP. We met our performance targets of overall classification accuracy (0.92); 20.2% of veterans received ≥ one session of EBP over the study period. Our method can assist with identifying EBP use and studying EBP-associated outcomes in routine clinical practice. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 67% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 103 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 15 | 15% |
Student > Master | 14 | 14% |
Researcher | 10 | 10% |
Student > Ph. D. Student | 10 | 10% |
Student > Doctoral Student | 8 | 8% |
Other | 11 | 11% |
Unknown | 35 | 34% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 25 | 24% |
Medicine and Dentistry | 11 | 11% |
Computer Science | 6 | 6% |
Nursing and Health Professions | 6 | 6% |
Engineering | 3 | 3% |
Other | 10 | 10% |
Unknown | 42 | 41% |