Virtual Geriatric Mental Health Care Offered Where Needed Most: An Evaluation of a Telehealth Consultation Model

Many VA facilities serving large rural populations do not have geriatric mental health specialists available to assist with managing the aging Veteran population’s complex medical and behavioral comorbidities. We applied mixed-methods to evaluate an innovative model utilizing a geriatric psychiatrist who provides cross-facility consultation in a 5-state region. During a 3-month period, the consultant completed 135 consults and 20 e-consults to settings ranging from outpatient to long-term care. Leadership stakeholder and provider interviews highlight the importance of the availability of the consultant, collaboration with local care teams, staff education, person-centered approach, and work ethic/passion. The core challenges that the consultant helps manage include complex comorbidities, medication questions, and dementia with behavioral disturbance. Initial provider survey responses (n = 11) show high satisfaction with services (100%) and strong agreement (80%) that providers could follow through with recommendations. Next steps include replication of this model in other VA facilities.

Dissemination of a COVID-19 Rapid Response Telehealth Group Addressing Worry and Social Isolation Among Older Adults

In response to the urgent need for virtual mental health treatments during the COVID-19 pandemic, an 8-week group intervention deliverable over video or telephone was developed and disseminated in March 2020. Manual content addressed social isolation and information related to COVID-19. In August 2020, a national web-based provider feedback survey was disseminated to evaluate feasibility of the manual. Respondents (n = 21) across a variety of geriatric mental health clinics reported this intervention to be effective and clinically useful with their patients in providing social support and in mitigating COVID-19 anxieties. The majority of respondents delivered the group in multiple cohorts and found the manual adaptable beyond the early pandemic period.

Optimizing the Geriatric Mental Health Workforce Through Innovative Approaches

The drastic demand for geriatrics-trained providers in medical and mental healthcare persists years after the Institute of Medicine first highlighted this need (2008; 2012). New innovative approaches must instead optimize the current workforce through leveraging existing geriatric experts’ knowledge and skills related to working aging adults. This symposium will highlight four approaches spanning post-licensure education to using technology to deliver specialized services and training. First, Dr. Gregg will discuss the evaluation of an advanced topics workshop in Geropsychology which has significantly enhanced depth of Geropsychology competencies for psychologists working in primarily rural areas. Next, Dr. Asghar-Ali will describe the multi-modal interactive geriatric educational opportunities for interprofessional staff developed by the South East Texas Geriatric Workforce Enhancement Program (SETx GWEP). He will discuss how these training opportunities have been tailored to address the impact of COVID-19 and healthcare disparities among older adults. Third, Dr. Filips will present an evaluation of a consultation model in which a geriatric psychiatrist provides tele-consultation in a 5-state region to rural aging Veterans with complex medical and behavioral comorbidities. Finally, Dr. Beaudreau will describe adaptations to a national VA Problem Solving Training program for mental health clinicians of older Veterans with complex comorbidities. Dr. Karel, VA National Geriatric Mental Health Director, will serve as discussant and comment on the ways in which these novel approaches are meeting the ever-growing need for competent geriatric mental health providers.

Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust.

Public Health Perspectives of Geriatric Mental Health Care

Background: In older adults (aged 60 years and above), mental health problems are gaining public health importance because of the increasing prevalence, disease burden, disability, morbidity, and mortality. Epidemiological studies on major mental health disorders such as depression and dementia in older adults have contributed to a better understanding of the distribution and determinants of these conditions. Identifying potential risk factors has stimulated interventional research on preventing these conditions under the public health framework towards their management. The increasing burden of geriatric mental health conditions like dementia in developing countries like India can contribute to significant challenges if there is no adequate strengthening of the public health response. This includes scaling up the measures of prevention, public awareness, early diagnosis, and quality health and social care equitably available to all sections of the population. The Decade of Healthy Ageing (2021–2030) provides the opportunity for concerted and coordinated initiatives to improve intrinsic capacity (physical and mental) and offer an age-friendly environment to enhance the functional ability of all older adults. Methods: This article reviews the critical public health issues related to geriatric mental health in India.

100 - Artificial Intelligence in Geriatric Mental Health: Recent Advances in Clinical Research

SynopsisWith the rise of wearable sensors, advancement in comprehensible artificial intelligence (AI) algorithms, and growing acceptance of AI in medicine, AI has great potential to more reliably diagnose, prognose, and treat mental illnesses. The rapidly rising number of older adults worldwide presents a unique challenge for clinicians due to increased mental health needs in the setting of a dwindling clinical workforce. AI has enabled researchers to better understand mental illnesses by taking advantage of ‘big data.’This symposium will present an overview of novel research leveraging AI (machine learning, natural language processing) to better track, understand, and support mental health and cognitive functioning in older adults.Helmet Karim, PhD will present on prediction of treatment response in late-life major depressive disorder and the implications of those models.Ellen Lee, MD will present on using natural language processing to understand psychosocial functioning in older adults.Ipsit Vahia, MD will present on radio-based sensors to phenotype changes in behavior patterns that may correlate with a range of geropsychiatric symptoms.Andrea Iaboni, MD DPhil FRCPC will present on multimodal wearable and vision-based sensors for the detection and categorization of behavioural symptoms of dementia.The symposium includes three physician-scientists (Iaboni, Lee, Vahia), two women (Iaboni, Lee), and two early career faculty (Lee, Karim – co-chairs). The symposium represents four different institutions across the country (McLean/Harvard, Toronto Rehabilitation Institute/University of Toronto, UC San Diego, University of Pittsburgh) and four very different approaches using AI technology to improve understanding and outcomes in the field of geriatric mental health.The symposium seeks to address the underutilization of AI in psychiatric research, especially in the field of aging research. The increased individual-level heterogeneity associated with aging; complex trajectories of decline in cognitive, mental, and physical health; and lack and slow adoption of older adult-centered technologies present great challenges to advancing the field. However, advances in the field of explainable AI and transdisciplinary development of AI approaches can address the unique challenges of aging research.

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.