Purpose: Availability of mental health services is limited in the rural United States. Two promising models to reach patients with limited access to care are telehealth referral and collaborative care. The objective of this study was to assess telepsychiatrist- and telepsychologist-level facilitators and barriers to satisfaction with and implementation of these 2 telehealth models in rural settings.
Methods: Focus groups were held in 2019 using a semistructured interview guide. Participants were off-site telepsychiatrists (N = 10) and telepsychologists (N = 4) for primary care clinics across 3 states (Washington, Michigan, and Arkansas) involved in a recent pragmatic comparative effectiveness trial. Qualitative analysis occurred inductively by 2 independent coders.
Findings: Participants were satisfied with the models partly owing to good patient rapport and expanding access to care. Teamwork was highlighted as a facilitator in collaborative care and was often related to work with care managers. However, participants described communication with primary care providers as a challenge, especially in the telehealth referral arm. Barriers centered on variability of logistical processes (eg, symptom monitoring, scheduling, electronic medical record processes, and credentialing) among sites. Staff turnover, variable clinic investment, and inadequacy of training were possible explanations for these barriers.
Conclusions: Participants described high motivation to provide team-based, remote care for patients, though they experienced operational challenges. Centralized credentialing, scheduling, and record keeping are possible solutions. These findings are important because consulting psychiatrists and psychologists may play a leadership role in the dissemination of these models.
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http://dx.doi.org/10.1111/jrh.12523 | DOI Listing |
JMIR Res Protoc
January 2025
Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Background: Acquired neurological diseases entail significant changes and influence the relationship between a patient and their significant other. In the context of long-term rehabilitation, those affected collaborate with health care professionals who are expected to have a positive impact on the lives of the affected individuals.
Objective: This study aims to examine the changes in the relationship between the patient and their loved ones due to acquired neurological disorders and the influence of health care professionals on this relationship.
Psilocybin-assisted therapy (PAT) is an experimental treatment with transformative promise. Developing standards for PAT psychotherapy protocols is a priority, but psychotherapeutic protocol components of PAT have been subjected to little rigorous research. This study was designed to assess protocol components in a trial of PAT.
View Article and Find Full Text PDFJ Patient Saf
January 2025
Department of Surgery, University of North Dakota School of Medicine and Health Sciences.
Background: PSI-90, a composite measure comprising ten indicators, reflects the quality of care during hospital stays. The Hospital-Acquired Condition Reduction Program (HACRP), a Centers for Medicare and Medical Services (CMS) program, assesses hospital performance based on quality measures, including PSI-90, with financial implications for poor performers.
Objectives: To evaluate PSI events, establish workflows for accurate documentation, and foster collaboration across clinical and administrative teams, with the ultimate objective of reducing PSI events.
Dtsch Arztebl Int
January 2025
Background: Osteoporosis is a common disease that affects approximately 6 million people in Germany alone. Osteoporotic fractures impair the quality of life and may make independent living impossible. Recommendations on the diagnosis and treatment of osteoporosis are indispensable for the effective care of this large group of patients.
View Article and Find Full Text PDFPrehosp Emerg Care
January 2025
Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO.
Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.
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