Predictive modeling of initiation and delayed mental health contact for depression.

Background: Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment.

Methods: We obtained data from the VA Corporate Data Warehouse (CDW).

Mental health treatment utilization patterns among 108,457 Afghanistan and Iraq veterans with depression.

People with depression often underutilize mental health care. This study was conceived as a first step toward a clinical decision support tool that helps identify patients who are at higher risk of underutilizing care. The primary goals were to (a) describe treatment utilization patterns, early termination, and return to care; (b) identify factors associated with early termination of treatment; and (c) evaluate the accuracy of regression models to predict early termination.

Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury.

Study Design: A 5-year longitudinal, retrospective, cohort study.

Objectives: Develop a prediction model based on electronic health record (EHR) data to identify veterans with spinal cord injury/diseases (SCI/D) at highest risk for new pressure injuries (PIs).

Setting: Structured (coded) and text EHR data, for veterans with SCI/D treated in a VHA SCI/D Center between October 1, 2008, and September 30, 2013.

Complementary and Integrative Health Approaches and Pain Care Quality in the Veterans Health Administration Primary Care Setting: A Quasi-Experimental Analysis.

Complementary and integrative health (CIH) approaches have been recommended in national and international clinical guidelines for chronic pain management. We set out to determine whether exposure to CIH approaches is associated with pain care quality (PCQ) in the Veterans Health Administration (VHA) primary care setting. We followed a cohort of 62,721 Veterans with newly diagnosed musculoskeletal disorders between October 2016 and September 2017 over 1-year.

Mental Health Diagnoses are Not Associated With Indicators of Lower Quality Pain Care in Electronic Health Records of a National Sample of Veterans Treated in Veterans Health Administration Primary Care Settings.

Prior research has demonstrated disparities in general medical care for patients with mental health conditions, but little is known about disparities in pain care. The objective of this retrospective cohort study was to determine whether mental health conditions are associated with indicators of pain care quality (PCQ) as documented by primary care clinicians in the Veterans Health Administration (VHA). We used natural language processing to analyze electronic health record data from a national sample of Veterans with moderate to severe musculoskeletal pain during primary care visits in the Fiscal Year 2017.

Comparing Outcomes of the Veterans Health Administration's Traumatic Brain Injury and Mental Health Screening Programs: Types and Frequency of Specialty Services Used.

The Veterans Health Administration (VHA) screens veterans who deployed in support of the wars in Afghanistan and Iraq for traumatic brain injury (TBI) and mental health (MH) disorders. Chronic symptoms after mild TBI overlap with MH symptoms, for which there are already established screens within the VHA. It is unclear whether the TBI screen facilitates treatment for appropriate specialty care over and beyond the MH screens.

The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records.

Background: Affective characteristics are associated with depression severity, course, and prognosis. Patients' affect captured by clinicians during sessions may provide a rich source of information that more naturally aligns with the depression course and patient-desired depression outcomes.

Objective: In this paper, we propose an information extraction vocabulary used to pilot the feasibility and reliability of identifying clinician-recorded patient affective states in clinical notes from electronic health records.

Optimizing the Impact of Pragmatic Clinical Trials for Veteran and Military Populations: Lessons From the Pain Management Collaboratory.

Pragmatic clinical trials (PCTs) are well-suited to address unmet healthcare needs, such as those arising from the dual public health crises of chronic pain and opioid misuse, recently exacerbated by the COVID-19 pandemic. These overlapping epidemics have complex, multifactorial etiologies, and PCTs can be used to investigate the effectiveness of integrated therapies that are currently available but underused. Yet individual pragmatic studies can be limited in their reach because of existing structural and cultural barriers to dissemination and implementation.

Assessing the impact of the COVID-19 pandemic on pragmatic clinical trial participants.

Characterizing the impacts of disruption attributable to the COVID-19 pandemic on clinical research is important, especially in pain research where psychological, social, and economic stressors attributable to the COVID-19 pandemic may greatly impact treatment effects. The National Institutes of Health - Department of Defense - Department of Veterans Affairs Pain Management Collaboratory (PMC) is a collective effort supporting 11 pragmatic clinical trials studying nonpharmacological approaches and innovative integrated care models for pain management in veteran and military health systems. The PMC rapidly developed a brief pandemic impacts measure for use across its pragmatic trials studying pain while remaining broadly applicable to other areas of clinical research.

Measuring pain care quality in the Veterans Health Administration primary care setting.

The lack of a reliable approach to assess quality of pain care hinders quality improvement initiatives. Rule-based natural language processing algorithms were used to extract pain care quality (PCQ) indicators from documents of Veterans Health Administration primary care providers for veterans diagnosed within the past year with musculoskeletal disorders with moderate-to-severe pain intensity across 2 time periods 2013 to 2014 (fiscal year [FY] 2013) and 2017 to 2018 (FY 2017). Patterns of documentation of PCQ indicators for 64,444 veterans and 124,408 unique visits (FY 2013) and 63,427 veterans and 146,507 visits (FY 2017) are described.

Examining the Relationship Between Environmental Factors and Inpatient Hospital Falls: Protocol for a Mixed Methods Study.

Background: Patient falls are the most common adverse events reported in hospitals. Although it is well understood that the physical hospital environment contributes to nearly 40% of severe or fatal hospital falls, there are significant gaps in the knowledge about the relationship between inpatient unit design and fall rates. The few studies that have examined unit design have been conducted in a single hospital (non-Veterans Health Administration [VHA]) or a small number of inpatient units, limiting generalizability.

Serious Falls in Middle-Aged Veterans: Development and Validation of a Predictive Risk Model.

Background/objectives: Due to high rates of multimorbidity, polypharmacy, and hazardous alcohol and opioid use, middle-aged Veterans are at risk for serious falls (those prompting a visit with a healthcare provider), posing significant risk to their forthcoming geriatric health and quality of life. We developed and validated a predictive model of the 6-month risk of serious falls among middle-aged Veterans.

Design: Cohort study.

Mortality and Associated Risk Factors in Community-Dwelling Persons With Early Dementia.

Objective: Examine mortality and associations with baseline characteristics among Veterans with early dementia.

Methods: Participants included dyads of community-based Veterans with early dementia and their caregivers (N=143) enrolled in a previous longitudinal study. Department of Veterans Health Affairs' electronic records were used to retrospectively collect Veteran mortality outcomes, over a 6-year period.

Evaluation of Complementary and Integrative Health Approaches Among US Veterans with Musculoskeletal Pain Using Propensity Score Methods.

Objectives: To examine the treatment effectiveness of complementary and integrative health approaches (CIH) on chronic pain using Propensity Score (PS) methods.

Design, Settings, And Participants: A retrospective cohort of 309,277 veterans with chronic musculoskeletal pain assessed over three years after initial diagnosis.

Methods: CIH exposure was defined as one or more clinical visits for massage, acupuncture, or chiropractic care.

Classifying Clinical Notes with Pain Assessment.

Pain is a significant public health problem, affecting an estimated 100 million Americans. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ). Efforts to improve PCQ hinge on the identification of reliable PCQ indicators such as pain assessment.

Classifying clinical notes with pain assessment using machine learning.

Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ) including pain assessment, treatment, and reassessment. Currently, there is no intelligent and reliable approach to identify PCQ indicators inelectronic health records (EHR).

Factors Associated With Wandering Behaviors in Veterans With Mild Dementia: A Prospective Longitudinal Community-Based Study.

Objective: To investigate baseline factors associated with caregiver-reported wandering among community-dwelling veterans with mild dementia.

Methods: Veterans with mild dementia (N = 143) and their caregivers participated in a 2-year prospective longitudinal study. Measures assessed wandering, daily function, behavior, cognition, and personality features.

Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol.

Background: Pressure ulcers (PrUs) are a frequent, serious, and costly complication for veterans with spinal cord injury (SCI). The health care team should periodically identify PrU risk, although there is no tool in the literature that has been found to be reliable, valid, and sensitive enough to assess risk in this vulnerable population.

Objective: The immediate goal is to develop a risk assessment model that validly estimates the probability of developing a PrU.

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs.

In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews.

Qualitative Inquiry Explores Health-Related Quality of Life of Female Veterans With Post-Traumatic Stress Disorder.

As the number of female veterans increases, health care systems must be prepared to meet the individualized needs of this population. To date, published data on health-related quality of life (HRQOL) of veterans with post-traumatic stress disorder (PTSD) focus on quantitative data and primarily represent the male population. The purpose of this study was to qualitatively explore the impact of PTSD on female veterans' HRQOL.

Development and initial validation of the Seated Posture Scale.

Literature shows that some health outcomes (e.g., eating, breathing, and speaking) are directly related to posture.

Improving identification of fall-related injuries in ambulatory care using statistical text mining.

Objectives: We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities.

Methods: We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review.