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Using information from the electronic health record to improve measurement of unemployment in service members and veterans with mTBI and post-deployment stress.

Dillahunt-Aspillaga C, Finch D, Massengale J, Kretzmer T, Luther SL, McCart JA. Using information from the electronic health record to improve measurement of unemployment in service members and veterans with mTBI and post-deployment stress. PLoS ONE. 2014 Dec 26; 9(12):e115873.

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Abstract:

OBJECTIVE: The purpose of this pilot study is 1) to develop an annotation schema and a training set of annotated notes to support the future development of a natural language processing (NLP) system to automatically extract employment information, and 2) to determine if information about employment status, goals and work-related challenges reported by service members and Veterans with mild traumatic brain injury (mTBI) and post-deployment stress can be identified in the Electronic Health Record (EHR). DESIGN: Retrospective cohort study using data from selected progress notes stored in the EHR. SETTING: Post-deployment Rehabilitation and Evaluation Program (PREP), an in-patient rehabilitation program for Veterans with TBI at the James A. Haley Veterans' Hospital in Tampa, Florida. PARTICIPANTS: Service members and Veterans with TBI who participated in the PREP program (N = 60). MAIN OUTCOME MEASURES: Documentation of employment status, goals, and work-related challenges reported by service members and recorded in the EHR. RESULTS: Two hundred notes were examined and unique vocational information was found indicating a variety of self-reported employment challenges. Current employment status and future vocational goals along with information about cognitive, physical, and behavioral symptoms that may affect return-to-work were extracted from the EHR. The annotation schema developed for this study provides an excellent tool upon which NLP studies can be developed. CONCLUSIONS: Information related to employment status and vocational history is stored in text notes in the EHR system. Information stored in text does not lend itself to easy extraction or summarization for research and rehabilitation planning purposes. Development of NLP systems to automatically extract text-based employment information provides data that may improve the understanding and measurement of employment in this important cohort.





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