Literature Collection

Rural populations rely on primary care services for depression care due to shortages and maldistributions of specialty mental health care favoring urban areas. Yet, it is unknown which primary care models are effective at reducing depressive symptoms and emergency department (ED) use for depression among rural populations. The purpose of this systematic review is to synthesize the effectiveness of primary care models on depressive symptoms and ED utilization for depression in rural populations. PubMed, PsycINFO, CINAHL, and reference lists of included studies were searched. Eligible articles focused on the impact of primary care models on depressive symptoms or ED utilization for depression among rural populations in the United States. Seventeen studies met the inclusion criteria. Three care models were identified in the studies, including collaborative care (i.e., team-based integrated care that tracks patient populations with a registry; n = 7), tele-psychotherapy (i.e., identification of patients in primary care and referral to virtual psychotherapy; n = 6), or self-management support (i.e., identification of patients in primary care and referral to community support for depression self-management; n = 4). These care models were associated with improved patient-reported depressive symptoms such as Patient-Health Questionnaire reported remission of depression (score < 5). No studies assessed depression ED utilization as an outcome. Collaborative care, tele-psychotherapy, and self-management support may be effective at reducing depressive symptoms, specifically in rural populations and should be implemented at the practice level. Research focused on primary care models and ED utilization for depression among rural populations is needed.

OBJECTIVE: To explore primary care patients' and practitioners' views and experiences of remote consulting for common mental disorders (CMDs), to optimise their management in primary care. DESIGN: Qualitative study using in-depth interviews and thematic analysis. A topic guide was used to ensure consistency across interviews. The interviews were audio-recorded, transcribed verbatim and analysed thematically. There was patient and public input throughout. SETTING: Participants were recruited from general practices. Interviews were held by telephone or videocall between March 2023 and October 2023. PARTICIPANTS: We interviewed 20 practitioners and 21 patients. RESULTS: Interviewees suggested benefits included convenience, increased anonymity and were easier for those feeling very low or anxious. Challenges included practitioners finding it hard to assess risk, which lengthened consultation duration or led to further contact, increasing practice workload and patients feeling anxious waiting for the practitioner to call. In-person appointments were viewed as important for initial consultations and providing a safe space. Continuity of care and practitioner training were identified as facilitators for telephone consultations, and both patients and practitioners identified training needs around how to deliver mental healthcare remotely. CONCLUSIONS: Practitioners should aim to offer continuity of care and in-person appointments when patients initially seek help. Remote consultations may not be more time or cost-efficient for individuals with CMDs as risk is harder to assess. There is a need to evaluate existing training on delivering remote consultations to identify whether remote mental healthcare is included or should be incorporated in the future.

BACKGROUND: The COVID-19 pandemic catalyzed a rapid shift in healthcare delivery towards telehealth services, impacting patient care, including opioid use disorder (OUD) treatment. Regulatory changes eliminated the in-person evaluation requirement for buprenorphine treatment, encouraging adoption of telehealth. This study focused on understanding experiences of primary care providers in predominantly rural areas who used telehealth for OUD treatment during the pandemic. METHODS: Semi-structured interviews were conducted with 22 primary care providers. Participants practiced in 13 rural and 9 urban counties in Kentucky and Arkansas. Data were analyzed using conventional content analysis. RESULTS: The pandemic significantly impacted healthcare delivery. While telehealth was integrated for behavioral health counseling, in-person visits remained crucial, especially for urine drug screenings. Telehealth experiences varied, with some facing technology issues, while others found it efficient. Telehealth proved valuable for behavioral health counseling and sustaining relationships with established patients. Patients with OUD faced unique challenges, including housing, internet, transportation, and counseling needs. Stigma surrounding OUD affected clinical relationships. Building strong patient-provider relationships emerged as a central theme, emphasizing the value of face-to-face interactions. Regarding buprenorphine training, most found waiver training helpful but lacked formal education. CONCLUSION: This research offers vital guidance for improving OUD treatment services, especially in rural areas during crises like the COVID-19 pandemic. It highlights telehealth's value as a tool while acknowledging its limitations. The study underscores the significance of strong patient-provider relationships, the importance of reducing stigma, and the potential for training programs to elevate quality of care in OUD treatment.

BACKGROUND: Integrated primary care teams are ideally positioned to support the mental health care needs arising during the COVID-19 pandemic. Understanding how COVID-19 has affected mental health care delivery within primary care settings will be critical to inform future policy and practice decisions during the later phases of the pandemic and beyond. The objective of our study was to describe the impact of the COVID-19 pandemic on primary care teams' delivery of mental health care. METHODS: A qualitative study using focus groups conducted with primary care teams in Ontario, Canada. Focus group data was analysed using thematic analysis. RESULTS: We conducted 11 focus groups with 10 primary care teams and a total of 48 participants. With respect to the impact of the COVID-19 pandemic on mental health care in primary care teams, we identified three key themes: i) the high demand for mental health care, ii) the rapid transformation to virtual care, and iii) the impact on providers. CONCLUSIONS: From the outset of the COVID-19 pandemic, primary care quickly responded to the rising mental health care demands of their patients. Despite the numerous challenges they faced with the rapid transition to virtual care, primary care teams have persevered. It is essential that policy and decision-makers take note of the toll that these demands have placed on providers. There is an immediate need to enhance primary care's capacity for mental health care for the duration of the pandemic and beyond.

BACKGROUND: The prevalence of depression among older adults places a considerable strain on healthcare systems due to a shortage of psychiatrists for professional evaluations. This study reports a machine learning (ML) model to assist in screening for geriatric depression, enabling primary care practitioners to detect and respond to cases effectively and on time. METHODS: Data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES) were used. To identify relevant variables for depression screening, features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance in the training set. Seven ML algorithms were employed to develop predictive screening models. Model performance was evaluated using standard ML metrics and clinically relevant impact measures, ensuring a comprehensive assessment. To improve interpretability, Shapley Additive exPlanations (SHAP) were used to visualize the contribution of each feature. RESULTS: The analysis included 3802 participants, with 933 (24.54 %) identified as having depression. Thirteen key variables were selected for model development. The Extreme Gradient Boosting (XGBoost) model demonstrated the best performance, with an accuracy of 0.82 in test set and a maximum Area Under the Curve (AUC) of 0.88 in Receiver Operating Characteristic (ROC) analysis. Sleep disorders, gender, poverty-income ratio (PIR), serum albumin levels, and segmented neutrophil count were identified as the influential predictors. CONCLUSION: The ML model developed for screening depression in older adults showed strong predictive performance and clinical applicability, supporting health workers in the early identification and management of depression.

Background: Our rural health system sought to (1) increase the number of primary care clinicians waivered to prescribe buprenorphine for treatment of opioid use disorder (OUD) and (2) consequently increase the number of our patients receiving this treatment. Methods: We used the Project for Extension for Community Health Outcomes (ECHO) tele-education model as an implementation strategy. We examined the number of clinicians newly waivered, the number of patients treated with buprenorphine, the relationship between clinician engagement with ECHO training and rates of buprenorphine prescribing, and treatment retention at 180 days. Results: The number of clinicians with a waiver and number of patients treated increased during and after ECHO training. There was a moderate correlation between the number of ECHO sessions attended by a clinician and number of their buprenorphine prescriptions (r = 0.50, p = 0.01). The 180-day retention rate was 80.7%. Conclusions: Project ECHO was highly effective for increasing access to this evidence-based treatment. The high retention rate in this rural context indicates that most patients are increasing their likelihood of favorable outcomes.

The integration of behavioral health and serious mental illness assessment and treatment into primary care remains a challenge. While the increase in telehealth usage due to the COVID-19 pandemic helped reduce a key barrier to access, other challenges remain including a shortage of trained providers and an increased demand for services. A collaboration between ECHO-Chicago and Americares established a unique virtual medical education program that provided training and telementoring using the Project ECHO model with the integration of clinic-wide quality improvement (QI) projects. In this paper, we outline the process of adapting the existing Project ECHO(®) (Extension for Community Health Outcomes) series on behavioral health integration and serious mental illness to fit the needs of Free and Charitable Clinics (FCCs). This project highlights the process and organizational-wide outcomes of creating a partnership between an ECHO hub and a national cross-section of FCCs to create a telehealth program to improve mental healthcare delivery within the FCC space that can be replicated and scaled more broadly. Through this process, we highlight evaluation methods to examine the impact of ECHO series beyond the individual to the clinic-wide level.