Implementing eScreening technology in four VA clinics: a mixed-method study

Symptoms of major depressive disorder and post-traumatic stress disorder in veterans with mild traumatic brain injury: A network analysis

Mild Traumatic Brain Injury (mTBI, or concussion) is a debilitating condition that often leads to persistent cognitive and mental health problems post-injury. Post-traumatic Stress Disorder (PTSD) and Major Depressive Disorder (MDD) are two most commonly occurring mental health problems following mTBI and are suggested to be strong contributors to the persistent post-concussion symptoms. Thus, it is important to understand the symptomatology of PTSD and MDD post-mTBI, to better inform targets for behavioral health interventions. Therefore, the current study examined the symptom structure of post-mTBI co-morbid PTSD and MDD through network approaches; we compared the network structure of participants with a positive mTBI screen (N = 753) to the network structure of participants with a negative mTBI screen (N = 2044); lastly, we examined a network of PTSD and MDD symptoms with clinical covariates in a positive mTBI sample. We found that feeling distant/cutoff (P10) and difficulty concentrating (P15) were the most central symptoms in the positive mTBI network and sleep problems were the most prominent bridge nodes across the disorders. No significant difference between the positive and negative mTBI network were found through network comparison tests. Moreover, anxiety and insomnia were strongly associated with sleep symptoms and irritability symptoms, and emotional support and resilience were potential buffers against most of the PTSD and MDD symptoms. The results of this study might be particularly useful for identifying targets (i.e., feeling distant, concentration and sleep problems) for screening, monitoring and treatment after concussion to better inform post-mTBI mental health care and to improve treatment outcomes.

The role of organizational culture in health information technology implementations: A scoping review

Introduction

Methods

Results

Conclusions

Patient Characteristics of VA Telehealth Users During Hurricane Harvey

Introduction: Research on patient characteristics of telehealth users is relatively new. More studies are needed to understand the characteristics of telehealth users during disasters. This study attempts to bridge this gap and examines patient characteristics of telehealth users compared with nontelehealth users at the Houston VA Medical Center (VAMC) immediately before and after Hurricane Harvey (2017). Methods: Since use of telehealth services reached its peak and gradually declined within 2 weeks after the landfall, the data analyses focused on 14 days before/14 days after Harvey. Two sets of analyses were conducted using chi-square, t test, and one-way analysis of variance: (1) Patient characteristics of telehealth users were compared with nontelehealth users. (2) Patient characteristics were compared between 3 subgroups of telehealth users. Results: Compared with nontelehealth users, telehealth users were older (mean age: 60.8 vs 58.5 years, P < .001) and had a higher mean Nosos health risk score (1.9 vs 1.4, P < .001). They also had a higher mean number of outpatient visits (28.0 vs 19.8, P < .001), higher emergency room use (37% vs 29%, P < .001), and higher rates of hospitalizations (21% vs 13%, P < .001) during the 12 months before Harvey. When compared to less frequent telehealth users, the most frequent telehealth users were the oldest and most medically complex patients. Conclusions: As the largest integrated health care system in the United States, the VA has many advantages favoring successful implementation of telehealth services during disasters. However, more research is needed to better understand how VA telehealth could meet the varying needs of veterans to lower risk of harm during differing types of disasters.

Adaptation of a quality improvement approach to implement eScreening in VHA healthcare settings: innovative use of the Lean Six Sigma Rapid Process Improvement Workshop

BACKGROUND

The Veterans Health Administration (VHA) developed a comprehensive mobile screening technology (eScreening) that provides customized and automated self-report health screening via mobile tablet for veterans seen in VHA settings. There is agreement about the value of health technology, but limited knowledge of how best to broadly implement and scale up health technologies. Quality improvement (QI) methods may offer solutions to overcome barriers related to broad scale implementation of technology in health systems. We aimed to develop a process guide for eScreening implementation in VHA clinics to automate self-report screening of mental health symptoms and psychosocial challenges.

METHODS

This was a two-phase, mixed methods implementation project building on an adapted quality improvement method. In phase one, we adapted and conducted an RPIW to develop a generalizable process guide for eScreening implementation (eScreening Playbook). In phase two, we integrated the eScreening Playbook and RPIW with additional strategies of training and facilitation to create a multicomponent implementation strategy (MCIS) for eScreening. We then piloted the MCIS in two VHA sites. Quantitative eScreening pre-implementation survey data and qualitative implementation process "mini interviews" were collected from individuals at each of the two sites who participated in the implementation process. Survey data were characterized using descriptive statistics, and interview data were independently coded using a rapid qualitative analytic approach.

RESULTS

Pilot data showed overall satisfaction and usefulness of our MCIS approach and identified some challenges, solutions, and potential adaptations across sites. Both sites used the components of the MCIS, but site 2 elected not to include the RPIW. Survey data revealed positive responses related to eScreening from staff at both sites. Interview data exposed implementation challenges related to the technology, support, and education at both sites. Workflow and staffing resource challenges were only reported by site 2.

CONCLUSIONS

Our use of RPIW and other QI methods to both develop a playbook and an implementation strategy for eScreening has created a testable implementation process to employ automated, patient-facing assessment. The efficient collection and communication of patient information have the potential to greatly improve access to and quality of healthcare.