Department of Veteran Affairs Hiring of Psychologists

2009 ◽  
Author(s):  
Antonette Zeiss
Keyword(s):  
Veteran Affairs ◽  
Department Of Veteran Affairs

Pressure Injury Documentation Practices in the Department of Veteran Affairs

2019 ◽  
Vol 46 (1) ◽  
pp. 18-24
Author(s):  
Margeaux A. Chavez ◽  
Allyson Duffy ◽  
Deborah Rugs ◽  
Linda Cowan ◽  
Avaretta Davis ◽  
...  
Keyword(s):  
Veteran Affairs ◽  
Department Of Veteran Affairs ◽  
Pressure Injury

scholarly journals Prevalence and features of generalized anxiety disorder in Department of Veteran Affairs primary care settings

2013 ◽  
Vol 209 (2) ◽  
pp. 173-179 ◽  
Author(s):  
Melissa E. Milanak ◽  
Daniel F. Gros ◽  
Kathryn M. Magruder ◽  
Olga Brawman-Mintzer ◽  
B. Christopher Frueh
Keyword(s):  
Primary Care ◽  
Anxiety Disorder ◽  
Generalized Anxiety Disorder ◽  
Generalized Anxiety ◽  
Veteran Affairs ◽  
Primary Care Settings ◽  
Department Of Veteran Affairs

scholarly journals 1180 The Use Of Natural Language Processing To Extract Data From Psg Sleep Study Reports Using National Vha Electronic Medical Record Data

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A450-A451
Author(s):  
S Nowakowski ◽  
J Razjouyan ◽  
A D Naik ◽  
R Agrawal ◽  
K Velamuri ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Veterans Health Administration ◽  
Free Text ◽  
Veterans Health ◽  
Health Administration ◽  
Rule Based ◽  
Veteran Affairs ◽  
Department Of Veteran Affairs ◽  
Sleep Parameters

Abstract Introduction In 2007, Congress asked the Department of Veteran Affairs to pay closer attention to the incidence of sleep disorders among veterans. We aimed to use natural language processing (NLP), a method that applies algorithms to understand the meaning and structure of sentences within Electronic Health Record (EHR) patient free-text notes, to identify the number of attended polysomnography (PSG) studies conducted in the Veterans Health Administration (VHA) and to evaluate the performance of NLP in extracting sleep data from the notes. Methods We identified 481,115 sleep studies using CPT code 95810 from 2000-19 in the national VHA. We used rule-based regular expression method (phrases: “sleep stage” and “arousal index”) to identify attended PSG reports in the patient free-text notes in the EHR, of which 69,847 records met the rule-based criteria. We randomly selected 178 notes to compare the accuracy of the algorithm in mining sleep parameters: total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL) compared to human manual chart review. Results The number of documented PSG studies increased each year from 963 in 2000 to 14,209 in 2018. System performance of NLP compared to manually annotated reference standard in detecting sleep parameters was 83% for TST, 87% for SE, and 81% for SOL (accuracy benchmark ≥ 80%). Conclusion This study showed that NLP is a useful technique to mine EHR and extract data from patients’ free-text notes. Reasons that NLP is not 100% accurate included, the note authors used different phrasing (e.g., “recording duration”) which the NLP algorithm did not detect/extract or authors omitting sleep continuity variables from the notes. Nevertheless, this automated strategy to identify and extract sleep data can serve as an effective tool in large health care systems to be used for research and evaluation to improve sleep medicine patient care and outcomes. Support This material is based upon work supported in part by the Department of Veteran Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). Dr. Nowakowski is also supported by a National Institutes of Health (NIH) Grant (R01NR018342).

scholarly journals 1156 Polysomnographic Total Sleep Time: A Novel Biomarker For Dementia

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A440-A441
Author(s):  
S Nowakowski ◽  
J Razjouyan ◽  
A D Naik ◽  
R Agrawal ◽  
K Velamuri ◽  
...  
Keyword(s):  
Time Series ◽  
Sleep Duration ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Dementia Diagnosis ◽  
Cut Point ◽  
Veteran Affairs ◽  
Department Of Veteran Affairs ◽  
The Us ◽  
Prevalence Of Dementia

Abstract Introduction Neuroprotection, early diagnosis, and behavioral intervention are national priorities for dementia research. Sleep duration is emerging as an important potential remediable risk factor. In this study, we examined whether total sleep time (TST) derived from attended overnight polysomnography (PSG) studies is associated with an increased prevalence of dementia diagnosis and determined the optimal cut-point. Methods We identified 69,847 PSG sleep studies using CPT code 95810 from 2000-19 in the US Department of Veteran Affairs (VA) national database of patient care. We used natural language processing to verify PSG reports and extract TST values from the patient free-text notes. We examined a TST of 240-420 minutes in 10-minute increments using a run chart (time series) approach to determine the optimal cut-point for determining greater odds of dementia. Results Patients had a mean age of 55.4±13.8, 91.5% were male, and 64% were Caucasian. PSG studies revealed a mean TST of 310.6±79.5 minutes. The run chart time series analysis revealing < 360 minutes being the optimal cut-point for increased odds of dementia (OR: 1.64, 95% CI: 1.36-1.99, p<.05). Conclusion Lower TST predicted higher prevalence of dementia diagnosis. TST of 360 minutes may serve as the optimal cut-point to determine greater odds of dementia. This is an important study examining PSG sleep duration and the prevalence of dementia across 19 years in the largest integrated healthcare system in the US. TST may function as a potential biomarker for developing dementia. Support This material is based upon work supported in part by the Department of Veteran Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). Dr. Nowakowski is also supported by a National Institutes of Health (NIH) Grant (R01NR018342).

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