In many mental health and some somatic disorders, efficacy of therapist-guided remote CBT and in-person CBT does not differ.

SOURCE CITATION: Zandieh S, Abdollahzadeh SM, Sadeghirad B, et al. Therapist-guided remote versus in-person cognitive behavioural therapy: a systematic review and meta-analysis of randomized controlled trials. CMAJ. 2024;196:E327-E340. 38499303.

The association between population health management tools and clinician burnout in the United States VA primary care patient-centered medical home.

BACKGROUND: Technological burden and medical complexity are significant drivers of clinician burnout. Electronic health record(EHR)-based population health management tools can be used to identify high-risk patient populations and implement prophylactic health practices. Their impact on clinician burnout, however, is not well understood. Our objective was to assess the relationship between ratings of EHR-based population health management tools and clinician burnout. METHODS: We conducted cross-sectional analyses of 2018 national Veterans Health Administration(VA) primary care personnel survey, administered as an online survey to all VA primary care personnel (n = 4257, response rate = 17.7%), using bivariate and multivariate logistic regressions. Our analytical sample included providers (medical doctors, nurse practitioners, physicians' assistants) and nurses (registered nurses, licensed practical nurses). The outcomes included two items measuring high burnout. Primary predictors included importance ratings of 10 population health management tools (eg. VA risk prediction algorithm, recent hospitalizations and emergency department visits, etc.). RESULTS: High ratings of 9 tools were associated with lower odds of high burnout, independent of covariates including VA tenure, team role, gender, ethnicity, staffing, and training. For example, clinicians who rated the risk prediction algorithm as important were less likely to report high burnout levels than those who did not use or did not know about the tool (OR 0.73; CI 0.61-0.87), and they were less likely to report frequent burnout (once per week or more) (OR 0.71; CI 0.60-0.84). CONCLUSIONS: Burned-out clinicians may not consider the EHR-based tools important and may not be using them to perform care management. Tools that create additional technological burden may need adaptation to become more accessible, more intuitive, and less burdensome to use. Finding ways to improve the use of tools that streamline the work of population health management and/or result in less workload due to patients with poorly managed chronic conditions may alleviate burnout. More research is needed to understand the causal directional of the association between burnout and ratings of population health management tools.

Understanding How Contingency Staffing Programs Can Support Mental Health Services in the Veterans Health Administration.

Introduction: Beginning in 2019, the Department of Veterans Affairs (VA) prioritized improving access to care nationally to deliver virtual care and implemented 18 regionally based Clinical Resource Hubs (CRHs) to meet this priority. This observational study describes the quantity and types of care delivered by CRH Mental Health teams, and the professions of those hired to deliver it. Methods: A retrospective cohort study, based on national VA CRH mental health care utilization data and CRH staffing data for CRH's first 3 years, was conducted. Results: CRH Mental Health teams primarily used Telemental Health (TMH) to provide care (98.1% of all CRH MH encounters). The most common disorders treated included depression, post-traumatic stress disorder, and anxiety disorders. The amount of care delivered overtime steadily increased as did the racial and ethnic diversity of Veterans served. Psychologists accounted for the largest share of CRH staffing, followed by psychiatrists. Conclusions: CRH TMH delivered from a regional hub appears to be a feasible and acceptable visit modality, based on the continuously increasing CRH TMH visit rates. Our results showed that CRH TMH was predominantly used to address common mental health diagnoses, rather than serious mental illnesses. Traditionally marginalized patient populations increased over the 3-year window, suggesting that CRH TMH resources were accessible to many of these patients. Future research should assess barriers and facilitators for accessing CRH TMH, especially for difficult-to-service patient populations, and should consider whether similar results to ours occur when regional TMH is delivered to non-VA patient populations.

Implementation of Video-Based Care in Interdisciplinary Primary Care Settings at the Veterans Health Administration: Qualitative Study.

BACKGROUND: With the rapid shift to telehealth, there remains a knowledge gap in how video-based care is implemented in interdisciplinary primary care (PC) settings. OBJECTIVE: The objective of this study was to gain an in-depth understanding of how video telehealth services were implemented in PC from the perspectives of patients and interdisciplinary PC team members at the Veterans Health Administration (VHA) 2 years after the onset of the COVID-19 pandemic. METHODS: We applied a positive and negative deviance approach and selected the 6% highest (n=8) and the 6% lowest (n=8) video-using PC sites in 2022 from a total of 130 VHA medical centers nationally. A total of 12 VHA sites were included in the study, where 43 PC interdisciplinary team members (August-October 2022) and 25 patients (February-May 2023) were interviewed. The 5 domains from the diffusion of innovation theory and the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework guided the development of the 2 study interview guides (provider and patient). We identified themes that emerged across all interviews that were associated with the implementation of video-based care in interdisciplinary PC settings, using directed-content rapid analysis of the interview transcripts. The analysis was guided by 5 a priori NASSS domains: (1) patient condition or characteristic, (2) technology, (3) adopter system, (4) health care organization, and (5) adaptation over time. RESULTS: The study findings include the following common themes and factors, organized by the 5 NASSS domains: (1) patient condition or characteristic-visit type or purpose (eg, follow-up visits that do not require physical examination), health condition (eg, homebound or semihomebound patients), and sociodemographic characteristic (eg, patients who have a long commute time); (2) technology-key features (eg, access to video-enabled devices), knowledge (eg, how to use videoconferencing software), and technical support for patients and providers; (3) adopter system-changes in staff roles and clinical practice (eg, coordination of video-based care), provider and patient preference or comfort to use video-based care, and caregiver's role (eg, participation of caregivers during video visits); (4) health care organization-leadership support and access to resources, scheduling for video visits (eg, schedule or block off digital half or full days), and training and telehealth champions (eg, hands-on or on-site training for staff, patients, or caregivers); (5) adaptation over time-capacity to improve all aspects of video-based care and provide continued access to resources (eg, effective communication about updates). CONCLUSIONS: This study identified key factors associated with the implementation of video-based services in interdisciplinary PC settings at the VHA from the perspectives of PC team members and patients. The identified multifaceted factors may inform recommendations on how to sustain and improve the provision of video-based care in VHA PC settings as well as non-VHA patient-centered medical homes.

Characteristics of Veterans Experiencing Homelessness using Telehealth for Primary Care Before and After COVID-19 Pandemic Onset.

BACKGROUND: The COVID-19 pandemic expanded telehealth use across healthcare systems, including the Veterans Health Administration (VA). Little is known about how large-scale telehealth rollout affected access to primary care for patients experiencing homelessness. OBJECTIVE: To examine the extent to which homeless-experienced veterans used telehealth services in primary care and to characterize users before and after the onset of the COVID-19 pandemic. DESIGN: Retrospective cohort study, 3/16/2019-3/15/2022. PARTICIPANTS: 394,731 veterans with homelessness diagnoses nationally using 4,068,109 primary care visits. MAIN MEASURES: The outcomes were use of 1 + telehealth visits (video, phone, secure messaging) for primary care during each year. Through multivariable regression models, we examined associations between telehealth use, patient characteristics (e.g., age, sex, race-ethnicity, comorbidity), and VA homeless services use (e.g., homeless-tailored primary care (HPACT), permanent supportive housing). KEY RESULTS: Compared to pre-pandemic, telehealth in primary care among homeless-experienced veterans increased substantially 2 years post-pandemic (video: 1.37% versus 20.56%, phone: 60.74% versus 76.58%). Secure messaging was low over time (1.57-2.63%). In adjusted models, video users were more likely to be young (65 + years: OR = 0.43, CI: 0.42-0.44), women (OR = 1.74, CI: 1.70-1.78), Black (OR = 1.14, CI: 1.12-1.16), Hispanic (OR = 1.34, CI: 1.30-1.38), and with more comorbidities (2 + on the Charlson Comorbidity Index; OR = 1.16, CI: 1.14-1.19), compared to video non-users. HPACT patients were less likely to use video (OR = 0.68, CI: 0.66-0.71) than other primary care patients. This was not observed among users of other VA homeless services. CONCLUSIONS: Despite decreased access to health information technology and low pre-pandemic telehealth use, veterans experiencing homelessness still sustained high use of telehealth in primary care post-pandemic. Women and racial-ethnic minorities had higher video uptake proportionately, suggesting that telehealth may address access disparities among these homeless-experienced patient groups. Identifying and targeting organizational characteristics (e.g., HPACT users) that predict telehealth use for improvement may be key to increasing adoption among VA primary care patients experiencing homelessness.

Disparities in Video-Based Primary Care Use Among Veterans with Cardiovascular Disease.

BACKGROUND: Cardiovascular disease (CVD) is prevalent among Veterans, and video care enhances access to CVD care. However, it is unknown which patients with CVD conditions receive video care in primary care clinics, where a large proportion of CVD services is delivered. OBJECTIVE: Characterize use of VA video primary care for Veterans with two common CVDs, heart failure and hypertension. DESIGN: Retrospective cohort study. PATIENTS: Veterans seen in VA primary care with diagnoses of heart failure and/or hypertension in the year prior to the COVID-19 pandemic and for the first two pandemic-years. MAIN MEASURES: The primary outcome was use of any video-based primary care visits. Using multilevel regressions, we examined the association between video care use and patient sociodemographic and clinical characteristics, controlling for time and adjusting for patient- and site-level clustering. KEY RESULTS: Of 3.8M Veterans with 51.9M primary care visits, 456,901 Veterans had heart failure and hypertension, 50,753 had heart failure only, and 3,300,166 had hypertension only. Veterans with heart failure and hypertension had an average age of 71.6 years. 2.9% were female, and 34.8% lived in rural settings. Patients who were male, aged 75 or older, or rural-dwelling had lower odds of using video care than female patients, 18-44-year-olds, and urban-dwellers, respectively (male patients' adjusted odds ratio [AOR] 0.73, 95% confidence interval [CI] 0.72-0.74; 75 years or older, AOR 0.38, 95% CI 0.37-0.38; rural-dwellers, AOR 0.71, 95% CI 0.70-0.71). Veterans with heart failure had higher odds of video care use than those with hypertension only (AOR 1.05, 95% CI 1.04-1.06). CONCLUSIONS: Given lower odds of video primary care use among some patient groups, continued expansion of video care could make CVD services increasingly inequitable. These insights can inform equitable triage of patients, for example by identifying patients who may benefit from additional support to use virtual care.

Telework Arrangements and Physician Burnout in the Veterans Health Administration.

This survey study of physicians in the Veterans Health Administration examines the association of burnout with various telework arrangements.

Depression care quality among patients with solid tumor cancers detected to have depression in Veterans Health Administration primary care clinics.

Patients with cancer, especially advanced cancer, experience depression at high rates. We aimed to evaluate the quality of depression care received by patients with solid tumor cancer and advanced solid tumor cancer in Veterans Affairs (VA) primary care clinics. This is a retrospective cohort study of patients seen in 82 VA primary care clinics who newly screened positive for depression on the Patient Health Questionnaire (PHQ-2). Outcomes included timely follow-up within 84 or 180 days (3+ mental health specialty, 3+ psychotherapy, or 3+ primary care visits with depression diagnosis codes) and minimum treatment within 1 year (60+ days antidepressants prescribed, 4+ mental health specialty visits, or 3+ psychotherapy visits). 608,042 individuals were seen in VA primary care clinics during this period; 49,839 patients (8.2%) had solid tumor cancer and 9,278 (1.5%) had advanced or poor-prognosis solid tumor cancer. For 686 observations of patients with cancer and new depression, rates of appropriate follow-up were 22.3% within 84 days and 38.2% within 180 days. For 73 observations of patients with advanced or poor-prognosis cancer and new depression, rates of appropriate follow-up were 21.9% within 84 days and 34.3% within 180 days. Rates of minimum treatment within 1 year were 68.4% and 64.4% for patients with cancer and patients with advanced or poor-prognosis cancer, respectively. Quality of timely depression management is low in patients with solid tumor cancers. Even in health systems with well-integrated mental health services, care gaps remain for patients with cancer and depression. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Associations Between Primary Care Providers and Staff-Reported Access Management Challenges and Patient Perceptions of Access.

BACKGROUND/OBJECTIVE: Optimizing patients' access to primary care is critically important but challenging. In a national survey, we asked primary care providers and staff to rate specific care processes as access management challenges and assessed whether clinics with more of these challenges had worse access outcomes. METHODS: Study design: Cross sectional. National Primary Care Personnel Survey (NPCPS) (2018) participants included 6210 primary care providers (PCPs) and staff in 813 clinics (19% response rate) and 158,645 of their patients. We linked PCP and staff ratings of access management challenges to veterans' perceived access from 2018-2019 Survey of Healthcare Experiences of Patients-Patient Centered Medical Home (SHEP-PCMH) surveys (35.6% response rate). MAIN MEASURES: The NPCPS queried PCPs and staff about access management challenges. The mean overall access challenge score was 28.6, SD 6.0. The SHEP-PCMH access composite asked how often veterans reported always obtaining urgent appointments same/next day; routine appointments when desired and having medical questions answered during office hours. ANALYTIC APPROACH: We aggregated PCP and staff responses to clinic level, and use multi-level, multivariate logistic regressions to assess associations between clinic-level access management challenges and patient perceptions of access. We controlled for veteran-, facility-, and area-level characteristics. KEY RESULTS: Veterans at clinics with more access management challenges (> 75th percentile) had a lower likelihood of reporting always receiving timely urgent care appointments (AOR: .86, 95% CI: .78-.95); always receiving routine appointments (AOR: .74, 95% CI: .67-.82); and always reporting same- or next-day answers to telephone questions (AOR: .79, 95% CI: .70-.90) compared to veterans receiving care at clinics with fewer (< 25th percentile) challenges. DISCUSSION/CONCLUSION: Findings show a strong relationship between higher levels of access management challenges and worse patient perceptions of access. Addressing access management challenges, particularly those associated with call center communication, may be an actionable path for improved patient experience.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Primary care provider perspectives on virtual and in-person depression management during the COVID-19 pandemic.

INTRODUCTION: During the COVID-19 pandemic, primary care providers (PCPs), nurses, and integrated mental health specialists continued to collaboratively manage depression among patients using both in-person and virtual (i.e., hybrid) modalities. Few studies have characterized how hybrid services are currently delivered within interdisciplinary primary care teams. This study aimed to understand frontline PCPs' perspectives on providing hybrid virtual and in-person depression care during the pandemic. METHOD: From September to November 2020, 12 semistructured individual interviews focused on depression management were conducted with PCPs in two Veterans Health Administration (VA) clinics in Los Angeles, which resumed in-person services while balancing rising COVID-19 cases. Interviews were audio-recorded, transcribed, and coded for depression management patterns. Themes were derived using a team-based constant comparative analytic approach. RESULTS: The pandemic and subsequent expanded use of virtual care necessitated clinic adaptations to depression assessments and procedures. PCPs perceived increased depression and anxiety among patients with existing psychiatric conditions, attributed to social distancing and isolation restrictions. They expressed acceptance of virtual care modalities for patients' depression management. PCPs did not perceive a delay in mental health care delivery in the shift to virtual care but noted the possibility of patients being lost to follow-up. CONCLUSIONS: During the pandemic, there has been heightened PCP concern for patients' emotional well-being and adaptations of clinic processes to meet needs for depression care. While PCPs were optimistic about new virtual care options for depression management, virtual care transfers remained poorly defined and the extent to which patient care experiences and health outcomes have been disrupted remains unknown. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Primary Care Team Perspectives on the Suitability of Telehealth Modality (Phone vs Video) at the Veterans Health Administration.

INTRODUCTION: Telehealth has the potential to improve access and timeliness of care, enabling primary care teams to deliver hybrid (virtual/in-person) services that will likely extend beyond the COVID-19 pandemic. To optimize its use and sustainability, it is imperative to understand how primary care teams perceive the suitability of telehealth services, including appropriate choice of mode (telephone or video). However, there is limited research on providers' decision-making processes surrounding telehealth use in primary care, including whether to use telephone or video, which this VA-focused study addresses. We examined how primary care (PC) team members determine whether to use telehealth and the mode of delivery, in the care of patients. METHODS: Qualitative case study that included 15 semi-structured interviews with employees who provided or supported telehealth care at primary care clinics at the Veterans Health Administration. We used a team-based rapid analysis approach to identify experiences using telehealth soon after COVID-19's emergence, consisting of the creation of structured summaries of each transcribed interview. The lead author then identified and compiled themes and sub-themes related to the suitability of telehealth in primary care, as well as associated quotes from transcripts. Resulting themes and quotes were reviewed and validated by 2 members of the project team. RESULTS: Primary care team members considered several factors when assessing both the suitability of telehealth for appointments, and mode of use. They were largely guided by patient-related factors including patient preferences, specific health issues, and access to technology. Additional considerations centered on team members' personal preferences and factors that supported the wellbeing of the team, such as the flexibility to work from home (work-life balance) and protection from infection. Generally, participants viewed the option of both telephone and video telehealth modalities as useful tools in the care of patients although a few respondents emphasized its inferiority to in-person care. CONCLUSIONS: Determining the suitability of telehealth services, including appropriate choice of mode is complex. Its suitability is tied to a variety of factors related to multilevel resources, preferences, and timing. When appropriate, telehealth should be considered a useful tool in the care of patients and for employee well-being.

Rates of Primary Care and Integrated Mental Health Telemedicine Visits Between Rural and Urban Veterans Affairs Beneficiaries Before and After the Onset of the COVID-19 Pandemic.

Importance: Telemedicine can increase access to care, but uptake has been low among people living in rural areas. The Veterans Health Administration initially encouraged telemedicine uptake in rural areas, but telemedicine expansion efforts have broadened since the COVID-19 pandemic. Objective: To examine changes over time in rural-urban differences in telemedicine use for primary care and for mental health integration services among Veterans Affairs (VA) beneficiaries. Design, Setting, and Participants: This cohort study examined 63.5 million primary care and 3.6 million mental health integration visits across 138 VA health care systems nationally from March 16, 2019, to December 15, 2021. Statistical analysis took place from December 2021 to January 2023. Exposures: Health care systems with most clinic locations designated as rural. Main Outcomes and Measures: For every system, monthly visit counts for primary care and mental health integration specialties were aggregated from 12 months before to 21 months after pandemic onset. Visits were categorized as in person or telemedicine, including video. A difference-in-difference approach was used to examine associations in visit modality by health care system rurality and pandemic onset. Regression models also adjusted for health care system size as well as relevant patient characteristics (eg, demographic characteristics, comorbidities, broadband internet access, and tablet access). Results: The study included 63 541 577 primary care visits (6 313 349 unique patients) and 3 621 653 mental health integration visits (972 578 unique patients) (6 329 124 unique patients among the cohort; mean [SD] age, 61.4 [17.1] years; 5 730 747 men [90.5%]; 1 091 241 non-Hispanic Black patients [17.2%]; and 4 198 777 non-Hispanic White patients [66.3%]). In fully adjusted models for primary care services before the pandemic, rural VA health care systems had higher proportions of telemedicine use than urban ones (34% [95% CI, 30%-38%] vs 29% [95% CI, 27%-32%]) but lower proportions of telemedicine use than urban health care systems after pandemic onset (55% [95% CI, 50%-59%] vs 60% [95% CI, 58%-62%]), signifying a 36% reduction in the odds of telemedicine use (odds ratio [OR], 0.64; 95% CI, 0.54-0.76). The rural-urban telemedicine gap was even larger for mental health integration (OR, 0.49; 95% CI, 0.35-0.67) than for primary care services. Few video visits occurred across rural and urban health care systems (unadjusted percentages: before the pandemic, 2% vs 1%; after the pandemic, 4% vs 8%). Nonetheless, there were rural-urban divides for video visits in both primary care (OR, 0.28; 95% CI, 0.19-0.40) and mental health integration services (OR, 0.34; 95% CI, 0.21-0.56). Conclusions and Relevance: This study suggests that, despite initial telemedicine gains at rural VA health care sites, the pandemic was associated with an increase in the rural-urban telemedicine divide across the VA health care system. To ensure equitable access to care, the VA health care system's coordinated telemedicine response may benefit from addressing rural disparities in structural capacity (eg, internet bandwidth) and from tailoring technology to encourage adoption among rural users.

Quality of Depression Care for Veterans Affairs Primary Care Patients with Experiences of Homelessness.

BACKGROUND: Persons who experience homelessness (PEH) have high rates of depression and incur challenges accessing high-quality health care. Some Veterans Affairs (VA) facilities offer homeless-tailored primary care clinics, although such tailoring is not required, within or outside VA. Whether services tailoring enhances care for depression is unstudied. OBJECTIVE: To determine whether PEH in homeless-tailored primary care settings receive higher quality of depression care, compared to PEH in usual VA primary care. DESIGN: Retrospective cohort study of depression treatment among a regional cohort of VA primary care patients (2016-2019). PARTICIPANTS: PEH diagnosed or treated for a depressive disorder. MAIN MEASURES: The quality measures were timely follow-up care (3 + completed visits with a primary care or mental health specialist provider, or 3 + psychotherapy sessions) within 84 days of a positive PHQ-2 screen result, timely follow-up care within 180 days, and minimally appropriate treatment (4 + mental health visits, 3 + psychotherapy visits, 60 + days antidepressant) within 365 days. We applied multivariable mixed-effect logistic regressions to model differences in care quality for PEH in homeless-tailored versus usual primary care settings. KEY RESULTS: Thirteen percent of PEH with depressive disorders received homeless-tailored primary care (n = 374), compared to usual VA primary care (n = 2469). Tailored clinics served more PEH who were Black, who were non-married, and who had low income, serious mental illness, and substance use disorders. Among all PEH, 48% received timely follow-up care within 84 days of depression screening, 67% within 180 days, and 83% received minimally appropriate treatment. Quality metric attainment was higher for PEH in homeless-tailored clinics, compared to PEH in usual VA primary care: follow-up within 84 days (63% versus 46%; adjusted odds ratio [AOR] = 1.61, p = .001), follow-up within 180 days (78% versus 66%; AOR = 1.51, p = .003), and minimally appropriate treatment (89% versus 82%; AOR = 1.58, p = .004). CONCLUSIONS: Homeless-tailored primary care approaches may improve depression care for PEH.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Examining telehealth use among primary care patients, providers, and clinics during the COVID-19 pandemic.

BACKGROUND: At the onset of COVID-19, there was a rapid expansion of telehealth (video/telephone) visits to maintain delivery of primary care (PC) services at the Veterans Health Administration (VA). This study examines patient, provider, and site-level characteristics of any virtual and video-based care in PC. METHODS: Interrupted time series (ITS) design was conducted using VA administrative/clinical, electronic healthcare data, 12-months before and 12-months after COVID-19 onset (set at March 2020) at the VA Greater Los Angeles Healthcare System (GLA), between 2019 and 2021. Patients with at least one visit to a VA PC clinic at GLA (n = 547,730 visits) were included in the analysis. The two main outcomes for this study were 1) any telehealth (versus in-person), as well as 2) video-based care (versus telephone). For the ITS analysis, segmented logistic regression on repeated monthly observations of any telehealth and video-based care was used. RESULTS: Percent telehealth and video use increased from 13.9 to 63.1%, and 0.3 to 11.3%, respectively, before to after COVID-19 onset. According to adjusted percentages, GLA community-based clinics (37.7%, versus 29.8% in hospital-based clinics, p < .001), social workers/pharmacists/dietitians (53.7%, versus 34.0% for PC clinicians, p < .001), and minority groups, non-Hispanic African Americans (36.3%) and Hispanics (34.4%, versus 35.3% for Whites, p < .001) were more likely to use telephone than video. Conversely, mental health providers (43.3%) compared to PC clinicians (15.3%), and women (for all age groups, except 75+) compared to men, were more likely to use video than telephone (all p's < .001). CONCLUSIONS: Since telehealth care provision is likely to continue after COVID-19, additional research is needed to identify which PC outpatient services are better suited for telephone (e.g., case management) versus video-based care (e.g., integrated mental health visits). Additionally, it is important to understand how all clinics can systematically increase access to both telephone- and video-based PC services, while ensuring equitable care for all patient populations.

Electronic Population-Based Depression Detection and Management Through Universal Screening in the Veterans Health Administration.

Importance: In 2016, the US Preventive Services Task Force newly recommended universal screening for depression, with the expectation that screening would be associated with appropriate treatment. Few studies have been able to assess the population-based trajectory from screening to receipt of follow-up and treatment for individuals with depression. Objective: To examine adherence to guidelines for follow-up and treatment among primary care patients who newly screened positive for depression in the Veterans Health Administration (VA). Design, Setting, and Participants: This retrospective cohort study used VA electronic data to identify patients who newly screened positive for depression on the 2-item Patient Health Questionnaire at 82 primary care VA clinics in California, Arizona, and New Mexico between October 1, 2015, and September 30, 2019. Data analysis was performed from December 2020 to August 2021. Main Outcomes and Measures: Receipt of guideline-concordant care for screen-positive patients who were determined by clinicians as having depression was assessed. Timely follow-up (within 84 days of screening) was defined as receiving 3 or more mental health specialty visits, 3 or more psychotherapy visits, or 3 or more primary care visits with a depression diagnosis according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Completing at least minimal treatment (within 12 months) was defined as having 60 days or more of antidepressant prescriptions filled, 4 or more mental health specialty visits, or 3 or more psychotherapy visits. Results: The final cohort included 607 730 veterans (mean [SD] age, 59.4 [18.2] years; 546 516 men [89.9%]; 339 811 non-Hispanic White [55.9%]); 8%, or 82 998 of 997 185 person-years, newly screened positive for depression. Clinicians identified fewer than half with depression (15 155 patients), of whom 32% (5034 of 15 650 person-years) met treatment guidelines for timely follow-up and 77% (12 026 of 15 650 person-years) completed at least minimal treatment. Younger age (odds ratio, 0.990; 95% CI, 0.986-0.993; P < .001), Black race (odds ratio, 1.19; 95% CI, CI 1.05-1.34; P = .01), and having comorbid psychiatric diagnoses were significantly associated with timely follow-up. Individual quality metric components (eg, medication or psychotherapy) were associated differently with overall quality results among patient groups, except for age. Conclusions and Relevance: In this cohort study, most patients met the guidelines for completing at least minimal treatment, but only a minority received timely follow-up after screening positive and being identified as having depression. More research is needed to understand whether the discrepancy between patients who screened positive and patients identified as having depression reflects a gap in recognition of needed care.

Association of Integrated Mental Health Services with Physical Health Quality Among VA Primary Care Patients.

BACKGROUND: Integrated care for comorbid depression and chronic medical disease improved physical and mental health outcomes in randomized controlled trials. The Veterans Health Administration (VA) implemented Primary Care-Mental Health Integration (PC-MHI) across all primary care clinics nationally to increase access to mental/behavioral health treatment, alongside physical health management. OBJECTIVE: To examine whether widespread, pragmatic PC-MHI implementation was associated with improved care quality for chronic medical diseases. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included 828,050 primary care patients with at least one quality metric among 396 VA clinics providing PC-MHI services between October 2013 and September 2016. MAIN MEASURE(S): For outcome measures, chart abstractors rated whether diabetes and cardiovascular quality metrics were met for patients at each clinic as part of VA's established quality reporting program. The explanatory variable was the proportion of primary care patients seen by integrated mental health specialists in each clinic annually. Multilevel logistic regression models examined associations between clinic PC-MHI proportion and patient-level quality metrics, adjusting for regional, patient, and time-level effects and clinic and patient characteristics. KEY RESULTS: Median proportion of patients seen in PC-MHI per clinic was 6.4% (IQR=4.7-8.7%). Nineteen percent of patients with diabetes had poor glycemic control (hemoglobin A1c >9%). Five percent had severely elevated blood pressure (>160/100 mmHg). Each two-fold increase in clinic PC-MHI proportion was associated with 2% lower adjusted odds of poor glycemic control (95% CI=0.96-0.99; p=0.046) in diabetes. While there was no association with quality for patients diagnosed with hypertension, patients without diagnosed hypertension had 5% (CI=0.92-0.99; p=0.046) lower adjusted odds of having elevated blood pressures. CONCLUSIONS AND RELEVANCE: Primary care clinics where integrated mental health care reached a greater proportion of patients achieved modest albeit statistically significant gains in key chronic care quality metrics, providing optimism about the expected effects of large-scale PC-MHI implementation on physical health.