One-Year All-Cause Mortality and Delivery of the Collaborative Chronic Care Model in General Mental Health Clinics.

OBJECTIVE: This study aimed to determine whether the evidence-based collaborative chronic care model (CCM) is associated with reduced all-cause mortality among adult patients treated in general mental health clinics. METHODS: Data came from a stepped-wedge, cluster-randomized CCM implementation trial across nine U.S. Department of Veterans Affairs medical centers. Survival analysis was used to estimate the relative effect of the treatment (N=5,570) compared with a control group (N=46,443) over 1 year. RESULTS: After adjustment for site-level and individual-level acute care utilization factors, analyses indicated that patients treated with the CCM experienced a reduction in all-cause mortality relative to patients in the control cohort (hazard ratio=0.76, 95% CI=0.60-0.95). CONCLUSIONS: This study is the first in which CCM has been shown to reduce all-cause mortality for patients treated in general mental health clinics. Care delivery models should be considered part of efforts to reduce the life expectancy gap between individuals with psychiatric conditions and those without such conditions.

Interdisciplinary behavioral health provider perceptions of implementing the Collaborative Chronic Care Model: an i-PARIHS-guided qualitative study.

BACKGROUND: The evidence-based Collaborative Chronic Care Model (CCM), developed to help structure care for chronic health conditions, comprises six elements: work role redesign, patient self-management support, provider decision support, clinical information systems, linkages to community resources, and organizational/leadership support. As the CCM is increasingly implemented in real-world settings, there is heightened interest in understanding specific influences upon implementation. Therefore, guided by the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, we (i) identified innovation-, recipient-, context-, and facilitation-related influences on CCM implementation and (ii) assessed the influences' relationship to each CCM element's implementation. METHODS: Using semi-structured interviews, we examined interdisciplinary behavioral health providers' experiences at nine VA medical centers that implemented the CCM. We used i-PARIHS constructs as a priori codes for directed content analysis, then analyzed the data for cross-coding by CCM element and i-PARIHS construct. RESULTS: Participants (31 providers) perceived the CCM innovation as enabling comprehensive care but challenging to coordinate with existing structures/procedures. As recipients, participants recounted not always having the authority to design CCM-consistent care processes. They perceived local leadership support to be indispensable to implementation success and difficult to garner when CCM implementation distracted from other organizational priorities. They found implementation facilitation helpful for keeping implementation on track. We identified key themes at the intersection of i-PARIHS constructs and core CCM elements, including (i) the CCM being an innovation that offers a formal structure to stepping down care intensity for patients to encourage their self-management, (ii) recipients accessing their multidisciplinary colleagues' expertise for provider decision support, (iii) relationships with external services in the community (e.g., homelessness programs) being a helpful context for providing comprehensive care, and (iv) facilitators helping to redesign specific interdisciplinary team member roles. CONCLUSIONS: Future CCM implementation would benefit from (i) facilitating strategic development of supportive maintenance plans for patients' self-management, (ii) collocating multidisciplinary staff (on-site or virtually) to enhance provider decision support, (iii) keeping information on available community resources up to date, and (iv) making clearer the explicit CCM-consistent care processes that work roles can be designed around. This work can inform concrete tailoring of implementation efforts to focus on the more challenging CCM elements, which is crucial to better account for multiple influences that vary across diverse care settings in which the CCM is being implemented.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Bipolar Disorder.

Bipolar disorder (BD) affects approximately 2% of U.S. adults and is the most costly mental health condition for commercial insurers nationwide. Rates of BD are elevated among persons with depression, anxiety disorders, and substance use disorders-conditions frequently seen by primary care clinicians. In addition, antidepressants can precipitate manic or hypomanic symptoms or rapid cycling in persons with undiagnosed BD. Thus, screening in these high-risk groups is indicated. Effective treatments exist, and many can be safely and effectively administered by primary care clinicians.

Time-motion analysis of external facilitation for implementing the Collaborative Chronic Care Model in general mental health clinics: Use of an interval-based data collection approach.

Background: Facilitation is an effective strategy to implement evidence-based practices, often involving external facilitators (EFs) bringing content expertise to implementation sites. Estimating time spent on multifaceted EF activities is complex. Furthermore, collecting continuous time-motion data for facilitation tasks is challenging. However, organizations need this information to allocate implementation resources to sites. Thus, our objectives were to conduct a time-motion analysis of external facilitation, and compare continuous versus noncontinuous approaches to collecting time-motion data. Methods: We analyzed EF time-motion data from six VA mental health clinics implementing the evidence-based Collaborative Chronic Care Model (CCM). We documented EF activities during pre-implementation (4-6 weeks) and implementation (12 months) phases. We collected continuous data during the pre-implementation phase, followed by data collection over a 2-week period (henceforth, "a two-week interval") at each of three time points (beginning/middle/end) during the implementation phase. As a validity check, we assessed how closely interval data represented continuous data collected throughout implementation for two of the sites. Results: EFs spent 21.8 ± 4.5 h/site during pre-implementation off-site, then 27.5 ± 4.6 h/site site-visiting to initiate implementation. Based on the 2-week interval data, EFs spent 2.5 ± 0.8, 1.4 ± 0.6, and 1.2 ± 0.6 h/week toward the implementation's beginning, middle, and end, respectively. Prevalent activities were preparation/planning, process monitoring, program adaptation, problem identification, and problem-solving. Across all activities, 73.6% of EF time involved email, phone, or video communication. For the two continuous data sites, computed weekly time averages toward the implementation's beginning, middle, and end differed from the interval data's averages by 1.0, 0.1, and 0.2 h, respectively. Activities inconsistently captured in the interval data included irregular assessment, stakeholder engagement, and network development. Conclusions: Time-motion analysis of CCM implementation showed initial higher-intensity EF involvement that tapered. The 2-week interval data collection approach, if accounting for its potential underestimation of irregular activities, may be promising/efficient for implementation studies collecting time-motion data.

Patient and provider predictors of telemental health use prior to and during the COVID-19 pandemic within the Department of Veterans Affairs.

The current study examined patient and provider differences in use of phone, video, and in-person mental health (MH) services. Participants included patients who completed ≥ 1 MH appointment within the Department of Veterans Affairs (VA) from 10/1/17-7/10/20 and providers who completed ≥ 100 VA MH appointments from 10/1/17-7/10/20. Adjusted odds ratios (aORs) are reported of patients and providers: (a) completing ≥1 video MH appointment in the pre-COVID (10/1/17-3/10/20) and COVID (3/11/20-7/10/20) periods; and (b) completing the majority of MH visits via phone, video, or in-person during COVID. The sample included 2,480,119 patients/31,971 providers in the pre-COVID period, and 1,054,670 patients/23,712 providers in the COVID period. During the pre-COVID and COVID periods, older patients had lower odds of completing ≥ 1 video visit (aORs < .65). During the COVID period, older age and low socioeconomic status predicted lower odds of having ≥ 50% of visits via video versus in-person or phone (aORs < .68); schizophrenia and MH hospitalization history predicted lower odds of having ≥ 50% of visits via video or phone versus in-person (aORs < . 64). During the pre-COVID and COVID periods, nonpsychologists (e.g., psychiatrists) had lower odds of completing video visits (aORs < . 44). Older providers had lower odds of completing ≥ 50% of visits via video during COVID (aORs <. 69). Findings demonstrate a digital divide, such that older and lower income patients, and older providers, engaged in less video care. Nonpsychologists also had lower video use. Barriers to use must be identified and strategies must be implemented to ensure equitable access to video MH services. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Provider Perceptions of Virtual Care During the Coronavirus Disease 2019 Pandemic: A Multispecialty Survey Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to a dramatic increase in virtual care (VC) across outpatient specialties, but little is known regarding provider acceptance of VC. OBJECTIVE: The objective of this study was to assess provider perceptions of the quality, efficiency, and challenges of VC versus in-person care with masks. DESIGN: This was a voluntary survey. PARTICIPANTS: Mental health (MH), primary care, medical specialty, and surgical specialty providers across the 8 VA New England Healthcare System medical centers. MEASURES: Provider ratings of: (1) quality and efficiency of VC (phone and video telehealth) compared with in-person care with masks; (2) challenges of VC; and (3) percentage of patients that providers are comfortable seeing via VC in the future. RESULTS: The sample included 998 respondents (49.8% MH, 20.6% primary care, 20.4% medical specialty, 9.1% surgical specialty; 61% response rate). Most providers rated VC as equivalent to or higher in quality and efficiency compared with in-person care with masks. Quality ratings were significantly higher for video versus phone (χ2=61.4, P<0.0001), but efficiency ratings did not differ significantly. Ratings varied across specialties (highest in MH, lowest in SS; all χ2s>24.1, Ps<0.001). Inability to conduct a physical examination and patient technical difficulties were significant challenges. MH providers were comfortable seeing a larger proportion of patients virtually compared with the other specialties (all χ2s>12.2, Ps<0.01). CONCLUSIONS: Broad provider support for VC was stratified across specialties, with the highest ratings in MH and lowest ratings in SS. Findings will inform the improvement of VC processes and the planning of health care delivery during the COVID-19 pandemic and beyond.

Time course and heterogeneity of treatment effect of the collaborative chronic care model on psychiatric hospitalization rates: A survival analysis using routinely collected electronic medical records.

BACKGROUND: Health systems are undergoing widespread adoption of the collaborative chronic care model (CCM). Care structured around the CCM may reduce costly psychiatric hospitalizations. Little is known, however, about the time course or heterogeneity of treatment effects (HTE) for CCM on psychiatric hospitalization. RATIONALE: Assessment of CCM implementation support on psychiatric hospitalization might be more efficient if the timing were informed by an expected time course. Further, understanding HTE could help determine who should be referred for intervention. OBJECTIVES: (i) Estimate the trajectory of CCM effect on psychiatric hospitalization rates. (ii) Explore HTE for CCM across demographic and clinical characteristics. METHODS: Data from a stepped wedge CCM implementation trial were reanalyzed using 5 570 patients in CCM treatment and 46 443 patients receiving usual care. Time-to-event data was constructed from routine medical records. Effect trajectory of CCM on psychiatric hospitalization was simulated from an extended Cox model over one year of implementation support. Covariate risk contributions were estimated from subset stratified Cox models without using simulation. Ratios of hazard ratios (RHR) allowed comparison by trial arm for HTE analysis, also without simulation. No standard Cox proportional hazards models were used for either estimating the time-course or heterogeneity of treatment effect. RESULTS: The effect of CCM implementation support increased most rapidly immediately after implementation start and grew more gradually throughout the rest of the study. On the final study day, psychiatric hospitalization rates in the treatment arm were 17% to 49% times lower than controls, with adjustment for all model covariates (HR 0.66; 95% CI 0.51-0.83). Our analysis of HTE favored usual care for those with a history of prior psychiatric hospitalization (RHR 4.92; 95% CI 3.15-7.7) but favored CCM for those with depression (RHR 0.61; 95% CI: 0.41-0.91). Having a single medical diagnosis, compared to having none, favored CCM (RHR 0.52; 95% CI 0.31-0.86). CONCLUSION: Reduction of psychiatric hospitalization is evident immediately after start of CCM implementation support, but assessments may be better timed once the effect size begins to stabilize, which may be as early as six months. HTE findings for CCM can guide future research on utility of CCM in specific populations.

Collaborative chronic care model implementation within outpatient behavioral health care teams: qualitative results from a multisite trial using implementation facilitation.

BACKGROUND: This paper reports on a qualitative evaluation of a hybrid type II stepped-wedge, cluster randomized trial using implementation facilitation to implement team-based care in the form of the collaborative chronic care model (CCM) in interdisciplinary outpatient mental health teams. The objective of this analysis is to compare the alignment of sites' clinical processes with the CCM elements at baseline (time 1) and after 12 months of implementation facilitation (time 2) from the perspective of providers. METHODS: We conducted semi-structured interviews to assess the extent to which six CCM elements were in place: work role redesign, patient self-management support, provider decision support, clinical information systems, linkages to community resources, and organizational/leadership support. Interviews were transcribed and a priori CCM elements were coded using a directed content analysis approach at times 1 and 2. We sought consensus on, and compared, the extent to which each CCM element was in place at times 1 and 2. RESULTS: We conducted 27 and 31 telephone interviews at times 1 and 2, respectively, with outpatient mental health providers at nine participating sites. At time 1 and time 2, three CCM elements were most frequently present across the sites: work role redesign, patient self-management support, and provider decision support. The CCM elements with increased implementation from time 1 to time 2 were work role redesign, patient self-management support, and clinical information systems. For two CCM elements, linkages to community resources and organizational/leadership support, some sites had increased implementation at time 2 compared to time 1, while others had reductions. For the provider decision support element, we saw little change in the extent of its implementation. CONCLUSIONS: Sites increased the extent of implementation on several CCM elements. The most progress was made in the CCM elements where sites had CCM-aligned processes in place at time 1. Teams made progress on elements they could more easily control, such as work role redesign. Our results suggest that maximizing the benefits of CCM-based outpatient mental health care may require targeting resources and training toward specific CCM elements-especially in the use of clinical information systems and linking with community resources. TRIAL REGISTRATION: Clinical Trials NCT02543840 .

Implementing the Collaborative Chronic Care Model in Mental Health Clinics: Achieving and Sustaining Clinical Effects.

OBJECTIVE: Collaborative chronic care models (CCMs) were established with implementation support in nine mental health clinics. This study sought to determine whether their clinical impact was maintained after implementation support ceased. METHODS: Posttrial data were analyzed from a randomized stepped-wedge CCM implementation trial in general mental health clinics in nine Department of Veterans Affairs medical centers. Sites received 1 year of implementation support, which was associated with reduced mental health hospitalization rates compared with non-CCM clinics in the same medical centers. Hospitalization rates for the year after implementation support were analyzed by using repeated measures logistic regression comparing the same clinics. RESULTS: Hospitalization rates for the postsupport year did not differ from comparison clinics either in the population that initially showed the difference or the population active in the clinics at the end of the year of implementation support. CONCLUSIONS: Clinical effects of the CCM may wane after cessation of active implementation support.

Rapid Increase in Telemental Health Within the Department of Veterans Affairs During the COVID-19 Pandemic.

Background: The use of telemental health via videoconferencing (TMH-V) became critical during the Coronavirus disease 2019 (COVID-19) pandemic due to restriction of non-urgent in-person appointments. The current brief report demonstrates the rapid growth in TMH-V appointments in the weeks following the pandemic declaration within the Department of Veterans Affairs (VA), the largest healthcare system in the United States. Methods: COVID-19 changes in TMH-V appointments were captured during the six weeks following the World Health Organization's pandemic declaration (March 11, 2020-April 22, 2020). Pre-COVID-19 TMH-V encounters were assessed from October 1, 2017 to March 10, 2020. Results: Daily TMH-V encounters rose from 1,739 on March 11 to 11,406 on April 22 (556% growth, 222,349 total encounters). Between March 11-April 22, 114,714 patients were seen via TMH-V, and 77.5% were first-time TMH-V users. 12,342 MH providers completed a TMH-V appointment between March 11-April 22, and 34.7% were first-time TMH-V users. The percentage growth of TMH-V appointments was higher than the rise in telephone appointments (442% growth); in-person appointments dropped by 81% during this time period. Discussion and Conclusions: The speed of VA's growth in TMH-V appointments in the wake of the COVID-19 pandemic was facilitated by its pre-existing telehealth infrastructure, including earlier national efforts to increase the number of providers using TMH-V. Longstanding barriers to TMH-V implementation were lessened in the context of a pandemic, during which non-urgent in-person MH care was drastically reduced. Future work is necessary to understand the extent to which COVID-19 related changes in TMH-V use may permanently impact mental health care provision.

Comparative effectiveness of external vs blended facilitation on collaborative care model implementation in slow-implementer community practices.

OBJECTIVE: To evaluate the comparative effectiveness of external facilitation (EF) vs external + internal facilitation (EF/IF), on uptake of a collaborative chronic care model (CCM) in community practices that were slower to implement under low-level implementation support. STUDY SETTING: Primary data were collected from 43 community practices in Michigan and Colorado at baseline and for 12 months following randomization. STUDY DESIGN: Sites that failed to meet a pre-established implementation benchmark after six months of low-level implementation support were randomized to add either EF or EF/IF support for up to 12 months. Key outcomes were change in number of patients receiving the CCM and number of patients receiving a clinically significant dose of the CCM. Moderators' analyses further examined whether comparative effectiveness was dependent on prerandomization adoption, number of providers trained or practice size. Facilitation log data were used for exploratory follow-up analyses. DATA COLLECTION: Sites reported monthly on number of patients that had received the CCM. Facilitation logs were completed by study EF and site IFs and shared with the study team. PRINCIPAL FINDINGS: N = 21 sites were randomized to EF and 22 to EF/IF. Overall, EF/IF practices saw more uptake than EF sites after 12 months (ΔEF/IF-EF  = 4.4 patients, 95% CI = 1.87-6.87). Moderators' analyses, however, revealed that it was only sites with no prerandomization uptake of the CCM (nonadopter sites) that saw significantly more benefit from EF/IF (ΔEF/IF-EF  = 9.2 patients, 95% CI: 5.72, 12.63). For sites with prerandomization uptake (adopter sites), EF/IF offered no additional benefit (ΔEF/IF-EF  = -0.9; 95% CI: -4.40, 2.60). Number of providers trained and practice size were not significant moderators. CONCLUSIONS: Although stepping up to the more intensive EF/IF did outperform EF overall, its benefit was limited to sites that failed to deliver any CCM under the low-level strategy. Once one or more providers were delivering the CCM, additional on-site personnel did not appear to add value to the implementation effort.

An Economic Analysis of the Implementation of Team-based Collaborative Care in Outpatient General Mental Health Clinics.

BACKGROUND: Collaborative Chronic Care Models represent an evidence-based way to structure care for chronic conditions, including mental health conditions. Few studies, however, have examined the cost implications of collaborative care for mental health. OBJECTIVE: We aimed to conduct an economic analysis of implementing collaborative care in 9 outpatient general mental health clinics. RESEARCH DESIGN: Analyses were derived from a stepped wedge hybrid implementation-effectiveness trial. We conducted cost-minimization analyses from the health system perspective, incorporating implementation costs, outpatient costs, and inpatient costs for the year before collaborative care implementation and the implementation year. We used a difference-in-differences approach and conducted 1-way sensitivity analyses to determine the robustness of results to variations ±15% in model parameters, along with probabilistic sensitivity analysis using Monte Carlo simulation. SUBJECTS: Our treatment group included 5507 patients who were initially engaged in care within 9 outpatient general mental health teams that underwent collaborative care implementation. We compared costs for this group to 45,981 control patients who received mental health treatment as usual at the same medical centers. RESULTS: Collaborative care implementation cost about $40 per patient and was associated with a significant decrease in inpatient costs and a nonsignificant increase in outpatient mental health costs. This implementation was associated with $78 in cost savings per patient. Monte Carlo simulation suggested that implementation was cost saving in 78% of iterations. CONCLUSIONS: Collaborative care implementation for mental health teams was associated with significant reductions in mental health hospitalizations, leading to substantial cost savings of about $1.70 for every dollar spent for implementation.

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

External facilitators' perceptions of internal facilitation skills during implementation of collaborative care for mental health teams: a qualitative analysis informed by the i-PARIHS framework.

BACKGROUND: Facilitation is a key strategy that may contribute to successful implementation of healthcare innovations. In blended facilitation, external facilitators (EFs) guide and support internal facilitators (IFs) in directing implementation processes. Developers of the i-PARIHS framework propose that successful facilitation requires project management, team/process, and influencing/negotiating skills. It is unclear what IF skills are most important in real-world settings, which could inform recruitment and training efforts. As prior qualitative studies of IF skills have only interviewed IFs, the perspectives of their EF partners are needed. Furthermore, little is known regarding the distribution of implementation tasks between IFs and EFs, which could impact sustainability once external support is removed. In the context of an implementation trial, we therefore: 1) evaluated IFs' use of i-PARIHS facilitation skills, from EFs' perspectives; 2) identified attributes of IFs not encompassed within the i-PARIHS skills; and 3) investigated the relative contributions of IFs and EFs during facilitation. METHODS: Analyses were conducted within a hybrid type II trial utilizing blended facilitation to implement the collaborative chronic care model within mental health teams of nine VA medical centers. Each site committed one team and an IF to weekly process design meetings and additional implementation activities over 12 months. Three EFs worked with three sites each. Following study completion, the EFs completed semi-structured qualitative interviews reflecting on the facilitation process, informed by the i-PARIHS facilitation skill areas. Interviews were analyzed via directed content analysis. RESULTS: EFs emphasized the importance of IFs having strong project management, team/process, and influencing/negotiating skills. Prior experience in these areas and a mental health background were also benefits. Personal characteristics (e.g., flexible, assertive) were described as critical, particularly when faced with conflict. EFs discussed the importance of clear delineation of EF/IF roles, and the need to shift facilitation responsibilities to IFs. CONCLUSIONS: Key IF skills, according to EFs, are aligned with i-PARIHS recommendations, but IFs' personal characteristics were also emphasized as important factors. Findings highlight traits to consider when selecting IFs and potential training areas (e.g., conflict management). EFs and IFs must determine an appropriate distribution of facilitation tasks to ensure long-term sustainability of practices. TRIAL REGISTRATION: Clinicaltrials.gov, September 7, 2015, #NCT02543840.

A systematic review of providers' attitudes toward telemental health via videoconferencing.

Telemental health conducted via videoconferencing (TMH-V) has the potential to improve access to care, and providers' attitudes toward this innovation play a crucial role in its uptake. This systematic review examined providers' attitudes toward TMH-V through the lens of the unified theory of acceptance and use of technology (UTAUT). Findings suggest that providers have positive overall attitudes toward TMH-V despite describing multiple drawbacks. Therefore, the relative advantages of TMH-V, such as its ability to increase access to care, may outweigh its disadvantages, including technological problems, increased hassle, and perceptions of impersonality. Providers' attitudes may also be related to their degree of prior TMH-V experience, and acceptance may increase with use. Limitations and implications of findings for implementation efforts are discussed.

Implementation science: What is it and why should I care?

Centuries of experience make it clear that establishing the effectiveness of a clinical innovation is not sufficient to guarantee its uptake into routine use. The relatively new field of implementation science has developed to enhance the uptake of evidence-based practices and thereby increase their public health impact. Implementation science shares many characteristics, and the rigorous approach, of clinical research. However, it is distinct in that it attends to factors in addition to the effectiveness of the clinical innovation itself, to include identifying and addressing barriers and facilitators to the uptake of evidence-based clinical innovations. This article reviews the definition, history, and scope of implementation science, and places the field within the broader enterprise of biomedical research. It also provides an overview of this Special Issue of Psychiatry Research, which introduces the principles and methods of implementation science to mental health researchers.

Comparing variations in implementation processes and influences across multiple sites: What works, for whom, and how?

Traditional analyses and interpretation of controlled trials rely on measures of central tendency (e.g., mean findings for treatment versus control) to detect treatment effects. These trial designs therefore emphasize homogeneity of results, with variations within the experimental or control groups treated as error to be controlled for or ignored. For implementation trials, however, heterogeneity of results is an expected result to be explored rather than an imperfection to be minimized. Thus, many implementation trials seek to understand not only "Does it work?" but also "What works, for whom, and how?" Hence, mixed quantitative-qualitative methods that can capitalize on heterogeneity are needed to (i) comprehensively identify factors that influence the implementation process and (ii) understand their impact on implementation outcomes. This paper outlines the matrixed multiple case study approach, which allows for understanding how these processes and influences similarly or differently interact with outcomes across multiple implementation sites. We provide an example of this approach using data from a multi-site trial that tested the implementation of the evidence-based Collaborative Chronic Care Model at nine US Department of Veterans Affairs medical centers.

The Collaborative Chronic Care Model for Mental Health Conditions: From Evidence Synthesis to Policy Impact to Scale-up and Spread.

BACKGROUND: Extensive evidence indicates that Collaborative Chronic Care Models (CCMs) improve outcome in chronic medical conditions and depression treated in primary care. Beginning with an evidence synthesis which indicated that CCMs are also effective for multiple mental health conditions, we describe a multistage process that translated this knowledge into evidence-based health system change in the US Department of Veterans Affairs (VA). EVIDENCE SYNTHESIS: In 2010, recognizing that there had been numerous CCM trials for a wide variety of mental health conditions, we conducted an evidence synthesis compiling randomized controlled trials of CCMs for any mental health condition. The systematic review demonstrated CCM effectiveness across mental health conditions and treatment venues. Cumulative meta-analysis and meta-regression further informed our approach to subsequent CCM implementation. POLICY IMPACT: In 2015, based on the evidence synthesis, VA Office of Mental Health and Suicide Prevention (OMHSP) adopted the CCM as the model for their outpatient mental health teams. RANDOMIZED IMPLEMENTATION TRIAL: In 2015-2018 we partnered with OMHSP to conduct a 9-site stepped wedge implementation trial, guided by insights from the evidence synthesis. SCALE-UP AND SPREAD: In 2017 OMHSP launched an effort to scale-up and spread the CCM to additional VA medical centers. Seventeen facilitators were trained and 28 facilities engaged in facilitation. DISCUSSION: Evidence synthesis provided leverage for evidence-based policy change. This formed the foundation for a health care leadership/researcher partnership, which conducted an implementation trial and subsequent scale-up and spread effort to enhance adoption of the CCM, as informed by the evidence synthesis.

An Exploratory Analysis of Self-Reported Protective Factors against Self-Harm in an Enrolled Veteran General Mental Health Population.

INTRODUCTION: The purpose of this study is to characterize self-reported protective factors against suicide or self-harm within free-response comments from a harm-risk screening. MATERIALS AND METHODS: Veterans enrolled in Department of Veterans Affairs mental health care were administered a self-harm and suicide screening as part of the baseline assessment in an ongoing implementation trial. Veterans indicated if they had thoughts of harming themselves and if so, what kept them from acting on them. Responses were coded based on established Centers for Disease Control protective factor categories. Descriptive analyses of demographic factors (such as age, gender, and race), clinical factors, and quality of life measures were conducted across groups depending on levels of self-harm risk. RESULTS: Of 593 Veterans, 57 (10%) screened positive for active thoughts of self-harm or suicide. Those with thoughts of self-harm had lower quality of life scores and higher rates of depression diagnoses. Of those individuals, 41 (72%) reported protective factors including Personal Resources (17%), Community Resources or Relationships (68%), and Other including pets and hobbies (15%). Those with stated protective factors had higher rates of employment and lower rates of PTSD diagnoses. CONCLUSION: This is one of the first open-response studies of harm-risk protective factors, allowing for a patient-centered approach that prioritizes the individual's voice and values. New protective factors emerged through the open-response format, indicating important factors that kept Veterans safe from self-harm or suicide such as pets and hobbies. Increasing focus on strengths and positive aspects of Veterans' lives that serve as protective factors may ultimately improve mental health treatment and prevention of suicide and self-harm.