Publications

The COVID-19 pandemic has increased the need for mental health care despite novel barriers to services. Little is known about how the pandemic has affected mental health providers and their practice. In July 2020, we conducted a web-based survey of 500 licensed mental health providers to assess their employment and caseloads, logistics of care, quality of care, and patient-provider relationships and communication during the pandemic. Over 90% of providers reported changes to their employment (e.g., furloughs), with 64% no longer practicing. Providers who reported no longer practicing were older in age, racial minorities, served rural communities, worked in small clinics/provider networks, were social workers and marriage and family therapists, and relied on private insurance or out-of-pocket payment. Most practicing providers reported similar-to-increased caseloads (62%), new patients seeking services (67%), and appointment frequency (70%). Approximately 97% of providers used telemedicine, with 54% providing services mostly-to-exclusively via telemedicine. Most providers reported losing contact with patients deemed unstable (76%) or a danger to themselves/others (71%). Most providers reported maintained-to-improved quality of care (83%), patient-provider relationships (80%), and communication (80%). Results highlight concerns relating to mental health services during the pandemic, however practicing providers have demonstrated resilience to coordinate and provide high quality care.

The COVID-19 pandemic caused widespread challenges and revealed vulnerabilities across global health care systems. In response, many health care providers turned to telehealth solutions, which have been widely embraced and are likely to become standard for modern care. Immersive extended reality (XR) technologies have the potential to enhance telehealth with greater acceptability, engagement, and presence. However, numerous technical, logistic, and clinical barriers remain to the incorporation of XR technology into telehealth practice. COVID-19 may accelerate the union of XR and telehealth as researchers explore novel solutions to close social distances. In this viewpoint, we highlight research demonstrations of XR telehealth during the COVID-19 pandemic and discuss future directions to make XR the next evolution of remote health care.

Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.

Despite its effectiveness, limited research has examined the provision of telemental health (TMH) and how practices may vary according to treatment paradigm. We surveyed 276 community mental health providers registered with a commercial telemedicine platform. Most providers reported primarily offering TMH services to adults with anxiety, depression, and trauma-and stressor-related disorders in individual therapy formats. Approximately 82% of TMH providers reported endorsing the use of Cognitive Behavioral Therapy (CBT) in their remote practice. The most commonly used in-session and between-session (i.e., homework) exercises included coping and emotion regulation, problem solving, mindfulness, interpersonal skills, relaxation, and modifying and addressing core beliefs. CBT TMH providers had a higher odds of using in-session and homework exercises and assigning them through postal mail, email or fax methods, as compared to non-CBT TMH providers. TMH providers, regardless of treatment paradigm, felt that assigning homework was neither easy nor difficult and they believed their patients were somewhat-to-moderately compliant to their assigned exercises. CBT TMH providers also collected clinical information from their patients more often than non-CBT TMH providers. They reported being less satisfied with their method, which was identified most often as paper-based surveys and forms. Overall, TMH providers employ evidence-based treatments to their patients remotely, with CBT TMH providers most likely to do so. Findings highlight the need for innovative solutions to improve how TMH providers that endorse following the CBT treatment paradigm remotely assign homework and collect clinical data to increase their satisfaction via telemedicine.

INTRODUCTION: Chronic insomnia is a common and debilitating disease that increases risk for significant morbidity and workplace difficulties. Cognitive behavioral therapy for insomnia (CBT-I) is the first-line treatment, but there is a critical lack of behavioral health providers trained in CBT-I because, in part, of a bottleneck in training availability and costs. The current project developed and evaluated a web-based provider training course for CBT-I: CBTIweb.org.

MATERIALS AND METHODS: Subject matter experts developed the content for CBTIweb.org. Then, trainees completed alpha testing (n = 24) and focus groups, and the site was improved. Next, licensed behavioral health providers and trainees completed beta testing (n = 41) and the site underwent another round of modifications. Finally, to compare CBTIweb.org to an in-person workshop, licensed behavioral health providers were randomly assigned to CBTIweb.org (n = 21) or an in-person workshop (n = 23). All participants were CBT-I naïve and completed the following assessments: Computer System Usability Questionnaire, Website Usability Satisfaction Questionnaire, Website Content Satisfaction Questionnaire, and Continuing Education knowledge acquisition questionnaires.

RESULTS: Alpha and beta testers of CBTIweb.org reported high levels of usability and satisfaction with the site and showed significant within-group knowledge acquisition. In the pilot comparison study, linear fixed-effects modeling on the pre-/postquestionnaires revealed a significant main effect for time, indicating a significant increase in knowledge acquisition from 69% correct at baseline to 92% correct at posttraining collapsed across in-person and CBTIweb.org groups. The interaction effect of Time by Condition was nonsignificant, indicating equivalence in knowledge gains across both groups.

CONCLUSION: CBTIweb.org appears to be an engaging, interactive, and concise provider training that can be easily navigated by its users and produce significant knowledge gains that are equivalent to traditional in-person workshops. CBTIweb.org will allow for worldwide dissemination of CBT-I to any English-speaking behavioral health providers. Future research will work on translating this training to other languages and extending this web-based platform to the treatment of other sleep disorders (e.g., nightmares) and populations (e.g., pediatric populations with insomnia).

INTRODUCTION: Mental health disorders (MHD) are prevalent within surgical patient populations and can be associated with poorer postoperative outcomes, particularly in those with more severe MHD (schizophrenia and bipolar disorder). However, these associations have not been examined in vascular surgery patients. This study investigated patients undergoing lower extremity revascularization, hypothesizing that those with severe MHD would experience worse health and postoperative outcomes.

METHODS: A retrospective chart review of patients from 2010-2015 with peripheral arterial disease (PAD) requiring revascularization was conducted, with subsequent narrowing to those with concurrent MHD diagnoses, including severe MHD (sMHD) defined as bipolar disorder or schizophrenia and non-severe MHD (nsMHD), defined as anxiety or depression. The primary endpoints were 30-day mortality; Major Adverse Limb Events (MALE) including amputation at the above or below knee level; and Major Adverse Cardiac Events (MACE) including myocardial infarction (MI), congestive heart failure (CHF) exacerbation, and arrhythmia. Secondary endpoints were readmission within 30 days, pulmonary complications, and wound infection. Statistical analyses included Fisher Exact Test and Student's T-test.

RESULTS: Eighteen patients with MHD (sMHD, n=10; nsMHD, n=8) were identified and stratified. Twenty-four limbs were revascularized (sMHD, n=13; nsMHD, n=11). Overall incidence of 30-day mortality, MALE, and MACE were 4.2%, 33.3%, and 50.0%, respectively. Readmission rate, pneumonia, and wound infection occurred in 41.7%, 20.8%, and 16.7% of the population. Stratifying by MHD severity, no significant differences were observed for medical comorbidities, MALE, intervention type (open vs. endovascular), or treatment indication (claudication vs. critical limb ischemia). Patients with sMHD had significantly higher rates of MACE compared to patients with nsMHD (30.8% vs. 18.2%, p<.05). Pneumonia was also more prevalent in this group (38.5% vs. 0.0%, p<.05).

CONCLUSION: While patients with concurrent diagnoses of MHD and PAD presented with similar comorbidities, comparable disease severity, and were equally treated by open versus endovascular techniques, those with severe MHD suffered significantly elevated rates of cardiopulmonary complications, specifically MACE and pneumonia. Further investigation is warranted to identify opportunities to optimize post-operative care for these complex patients.

BACKGROUND: Homework, or between-session practice of skills learned during therapy, is integral to effective youth mental health TREATMENTS. However, homework is often under-utilized by providers and patients due to many barriers, which might be mitigated via mHealth solutions.

METHODS: Semi-structured qualitative interviews were conducted with nationally certified trainers in Trauma Focused Cognitive Behavioral Therapy (TF-CBT; n=21) and youth TF-CBT patients ages 8-17 (n=15) and their caregivers (n=12) to examine barriers to the successful implementation of homework in youth mental health treatment and potential mHealth solutions to those barriers.

RESULTS: The results indicated that many providers struggle to consistently develop, assign, and assess homework exercises with their patients. Patients are often difficult to engage and either avoid or have difficulty remembering to practice exercises, especially given their busy/chaotic home lives. Trainers and families had positive views and useful suggestions for mHealth solutions to these barriers in terms of functionality (e.g., reminders, tracking, pre-made homework exercises, rewards) and user interface (e.g., easy navigation, clear instructions, engaging activities).

CONCLUSIONS: This study adds to the literature on homework barriers and potential mHealth solutions to those barriers, which is largely based on recommendations from experts in the field. The results aligned well with this literature, providing additional support for existing recommendations, particularly as they relate to treatment with youth and caregivers.

Understanding what motivates mental health providers to use telemedicine (i.e., telemental health) is critical for optimizing its uptake, especially during unprecedented times (e.g., the COVID-19 pandemic). Drawing from the Technology Acceptance Model (TAM), this report examined the characteristics of telemental health providers and how the acceptability of telemedicine features contributes to their intention to use the technology more often in practice. Telemental health providers (N = 177) completed an online survey between March and May 2019. Most providers (75%) spent less than 25% of their work-week using telemedicine, but 70% reported an intention to use telemedicine more in the future. The belief that telemedicine affords greater access to patients, work-life balance, flexibility in providing care, and the opportunity to be at the forefront of innovative care were significant predictors of intentions to use the technology more in the future. Other significant predictors included needing assistance to coordinate insurance reimbursements, manage a successful telemedicine practice, and integrate the telemedicine program with other health IT software. Findings have important implications for increasing the frequency of telemedicine use among telemental health providers. Future research and practice should leverage providers' positive beliefs about telemedicine acceptability and consider their needs to enhance its uptake.

BACKGROUND: Annually, post-traumatic stress disorder, depression, or both, develop in the first year after injury in more than 400,000 adults treated in US trauma centers (≥20%). Yet, few trauma centers monitor and address mental health recovery, and there is limited evaluation and high structural variability across existing programs. More research is needed to guide efforts to establish such programs and to inform national standards and recommendations.

STUDY DESIGN: This article describes patient engagement in a stepped-care service to address patients' mental health needs. Trauma-activation patients admitted to our Level I trauma center for at least 24 hours were approached before discharge. Patients were provided education in person at the bedside (step 1), symptom monitoring via a 30-day text-messaging tool (step 2), telephone screening approximately 30 days post injury (step 3), and, when appropriate, mental health treatment referrals and treatment (step 4).

RESULTS: We approached and educated 1,122 patients (56%) on the floor during a 33-month period. Of these, 1,096 patients (98%) enrolled in our program and agreed to 30-day follow-up mental health screening. We reached 676 patients for the 30-day screen, 243 (36%) of these patients screened positive for post-traumatic stress disorder and/or depression. Most of the 243 patients who graduated to step 4 accepted treatment referrals (68%) or were already receiving services from a provider (7%). Home-based telemental health was preferred by 66% of patients who accepted referrals.

CONCLUSIONS: This work demonstrates the feasibility of an evidence-based, technology-enhanced, stepped-care intervention to address the mental health needs of trauma center patients. Strategies to reach a higher percentage of patients in follow-up are needed. We recommend trauma centers test and adopt broad-based approaches to ensure optimal long-term patient outcomes.

BACKGROUND: Suicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum.

OBJECTIVE: This study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events.

METHODS: We extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words-based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance.

RESULTS: The area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance.

CONCLUSIONS: The strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.