Emerging needs in behavioral health and the integrated care model

Medically vulnerable populations are constantly at risk of having poor health related outcomes, low satisfaction in the healthcare system and increased mortality. Studies have shown the increased prevalence rates of various medical comorbidities in patients with severe mental illness. These patients are obviously vulnerable because of their mental illness but they are also more likely to have severe cases of medical conditions commonly seen in the general population. Expenditures and utilization of resources is often inappropriate due to frequent visits for acute needs and low rates of preventative care and primary care appointments. My proposed model focuses on the implementation of the integrated care model which encourages collaboration between mental health professionals and primary care physicians through referral programs or integrated clinic settings. This model is initiated with education to both current clinicians as well as future clinicians through medical schools and residency programs. Once the education component has begun, the next steps are formal exploration, preparation, implementation and evaluation of the model in clinics. The aim is to improve health outcomes by increasing preventative care and using behavioral techniques to assist with adherence, increase satisfaction in the healthcare system and contain expenditures by utilizing primary care services instead of emergency services when appropriate

TeamSTEPPS Training and Vital Signs Chart to Improve Situational Monitoring for Clinical Deterioration

Failure to monitor early warning signs of patient deterioration can result in cardiopulmonary arrests and patient death. Implementation of team building programs emphasizing vital sign data, with consistent monitoring and trending have demonstrated positive outcomes in multiple health care environments. This project implemented TeamSTEPPS© education for 23 registered nurse (RN) residents in an acute care medical center. Specific aims included: (a) increased knowledge of team communication techniques; (b) improved attitudes towards vital sign monitoring, especially respiratory rate assessment; and (c) improved attitudes towards early rapid response system activation. The education program included support tools, behavioral-modeling, simulation exercises based on de-identified patient data and debriefing. Paired t-tests evaluated the impact of the intervention on total TeamSTEPPS Teamwork Attitudes Questionnaire (T-TAQ) and V-Scale scores. There were statistically significant increases in T-TAQ and V-Scale scores post intervention (1.78 p =.04 and 1.87 p = .04 respectively). Eta square calculation indicated a large effect size for T-TAQ and V-Scale measures. The TeamSTEPPS simulation-enhanced curriculum was successful in improving RN residents’ attitudes toward teamwork, and vital signs monitoring and surveillance practices

Study Protocol for Investigating Physician Communication Behaviours that Link Physician Implicit Racial Bias and Patient Outcomes in Black Patients with Type 2 Diabetes Using an Exploratory Sequential Mixed Methods Design

Introduction Patient-physician racial discordance is associated with Black patient reports of dissatisfaction and mistrust, which in turn are associated with poor adherence to treatment recommendations and underutilisation of healthcare. Research further has shown that patient dissatisfaction and mistrust are magnified particularly when physicians hold high levels of implicit racial bias. This suggests that physician implicit racial bias manifests in their communication behaviours during medical interactions. The overall goal of this research is to identify physician communication behaviours that link physician implicit racial bias and Black patient immediate (patient-reported satisfaction and trust) and long-term outcomes (eg, medication adherence, self-management and healthcare utilisation) as well as clinical indicators of diabetes control (eg, blood pressure, HbA1c and history of diabetes complication). Methods and analysis Using an exploratory sequential mixed methods research design, we will collect data from approximately 30 family medicine physicians and 300 Black patients with type 2 diabetes mellitus. The data sources will include one physician survey, three patient surveys, medical interaction videos, video elicitation interviews and medical chart reviews. Physician implicit racial bias will be assessed with the physician survey, and patient outcomes will be assessed with the patient surveys and medical chart reviews. In video elicitation interviews, a subset of patients (approximately 20–40) will watch their own interactions while being monitored physiologically to identify evocative physician behaviours. Information from the interview will determine which physician communication behaviours will be coded from medical interactions videos. Coding will be done independently by two trained coders. A series of statistical analyses (zero-order correlations, partial correlations, regressions) will be conducted to identify physician behaviours that are associated significantly with both physician implicit racial bias and patient outcomes

Using a Pediatric Early Warning Score Algorithm for Activating a Rapid Response Team

The nursing culture of an inpatient pediatric unit was resistant to activating pediatric rapid response team (PRRT) alerts despite guidelines for activation. Nurses routinely assessed patients and assigned a pediatric early warning score (PEWS); however, the level of illness severity was not interpreted consistently among nurses and a PEWS action algorithm did not exist to guide nurses\u27 minimal actions based on the PEWS score. Guided by 3 adult learning theories (Knowles, Kolb, and Bandura) and 1 evaluation model (Kirkpatrick), this staff education project sought to educate pediatric nurses on a PEWS action algorithm and determine whether this project improved nurses\u27 knowledge, situational awareness, and attitude toward activating PRRT alerts. A convenience sample of 30 pediatric nurses completed a preeducation knowledge survey (EKS), attended an interactive PEWS education class, and completed a postEKS. After participating in the class, correct responses on the EKS increased from 43% to 82% and, using the Wilcoxon-signed rank test, a significant increase was noted in nurses\u27 responses to questions related to self-efficacy, factual knowledge, and application. The overall increase in the nurses\u27 self-efficacy and knowledge about the PEWS might enhance critical-thinking skills, foster identification of patients at risk for clinical deterioration, and empower nurses to follow the PEWS action algorithm including activation of PRRT alerts when indicated. This project has the potential to effect positive social change by supporting nurses\u27 actions designed to improve pediatric patient outcomes

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Diabetes Management for Low-Income Patients: Within-Case Analyses in Primary Care

The study evaluated the effectiveness of a diabetes management program in a co-located mental health and primary care setting in Nashua, New Hampshire. The patient-participants were primarily underserved, low-income, working-class or homeless patients from the surrounding region. A few participants were also older adults. Examination of the literature highlighted the growing diabetes epidemic at local, state, and national levels. A review of past clinical trials of diabetes programs indicated further investigation into diabetes self-management education programs that integrate medical and behavioral health components under the biopsychosocial model. Thus, for the present study, the Stanford Diabetes Self-Management Program (SDSMP; Lorig, Ritter, Villa, & Armas, 2009), was utilized as the cornerstone for building a comprehensive program for the population of the treatment site. SDSMP is a semi-scripted six-week peer-led psychoeducation course designed to enhance self-efficacy in the self-management of diabetes. The course meets the American Association of Diabetes Education Standards and teaches skills and knowledge around proper diet, exercise, lifestyle, and treatment adherence. Archived quantitative data from participant medical records (N = 12) were analyzed at the individual case level to verify whether the program was effective at increasing use of self-care practices and management of symptoms in individual diabetes patients. In addition, qualitative analysis of participant follow-up interviews (n = 4) depicted how and why individual changes may have occurred within each case. Some of these themes included Positive Changes, Social Influences, and Workshop Feedback. Quantitative results found that depression and anxiety scores decreased modestly for the group as a whole to a similar degree as shown by previous studies (Lorig et al., 2009, 2016). One patient’s depression score showed a clinically significant decrease. The group achieved similar results in their diabetes knowledge and adherence to diet and physical activity recommendations compared to regional norms for the SDSMP (New England Quality Innovation Network–Quality Improvement Organizations, 2016). Two patients also improved medically in their BMI and HbA1c% scores after attending the program. These results implied that the SDSMP may be an appropriate fit for the site’s population

Diabetes Management for Low-Income Patients: Within-Case Analyses in Primary Care

The study evaluated the effectiveness of a diabetes management program in a co-located mental health and primary care setting in Nashua, New Hampshire. The patient-participants were primarily underserved, low-income, working-class or homeless patients from the surrounding region. A few participants were also older adults. Examination of the literature highlighted the growing diabetes epidemic at local, state, and national levels. A review of past clinical trials of diabetes programs indicated further investigation into diabetes self-management education programs that integrate medical and behavioral health components under the biopsychosocial model. Thus, for the present study, the Stanford Diabetes Self-Management Program (SDSMP; Lorig, Ritter, Villa, & Armas, 2009), was utilized as the cornerstone for building a comprehensive program for the population of the treatment site. SDSMP is a semi-scripted six-week peer-led psychoeducation course designed to enhance self-efficacy in the self-management of diabetes. The course meets the American Association of Diabetes Education Standards and teaches skills and knowledge around proper diet, exercise, lifestyle, and treatment adherence. Archived quantitative data from participant medical records (N = 12) were analyzed at the individual case level to verify whether the program was effective at increasing use of self-care practices and management of symptoms in individual diabetes patients. In addition, qualitative analysis of participant follow-up interviews (n = 4) depicted how and why individual changes may have occurred within each case. Some of these themes included Positive Changes, Social Influences, and Workshop Feedback. Quantitative results found that depression and anxiety scores decreased modestly for the group as a whole to a similar degree as shown by previous studies (Lorig et al., 2009, 2016). One patient’s depression score showed a clinically significant decrease. The group achieved similar results in their diabetes knowledge and adherence to diet and physical activity recommendations compared to regional norms for the SDSMP (New England Quality Innovation Network–Quality Improvement Organizations, 2016). Two patients also improved medically in their BMI and HbA1c% scores after attending the program. These results implied that the SDSMP may be an appropriate fit for the site’s population

Enhancing Mental Health with Artificial Intelligence: Current Trends and Future Prospects

Artificial Intelligence (AI) has emerged as a transformative force in various fields, and its application in mental healthcare is no exception. Hence, this review explores the integration of AI into mental healthcare, elucidating current trends, ethical considerations, and future directions in this dynamic field. This review encompassed recent studies, examples of AI applications, and ethical considerations shaping the field. Additionally, regulatory frameworks and trends in research and development were analyzed. We comprehensively searched four databases (PubMed, IEEE Xplore, PsycINFO, and Google Scholar). The inclusion criteria were papers published in peer-reviewed journals, conference proceedings, or reputable online databases, papers that specifically focus on the application of AI in the field of mental healthcare, and review papers that offer a comprehensive overview, analysis, or integration of existing literature published in the English language. Current trends reveal AI's transformative potential, with applications such as the early detection of mental health disorders, personalized treatment plans, and AI-driven virtual therapists. However, these advancements are accompanied by ethical challenges concerning privacy, bias mitigation, and the preservation of the human element in therapy. Future directions emphasize the need for clear regulatory frameworks, transparent validation of AI models, and continuous research and development efforts. Integrating AI into mental healthcare and mental health therapy represents a promising frontier in healthcare. While AI holds the potential to revolutionize mental healthcare, responsible and ethical implementation is essential. By addressing current challenges and shaping future directions thoughtfully, we may effectively utilize the potential of AI to enhance the accessibility, efficacy, and ethicality of mental healthcare, thereby helping both individuals and communities