Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Cerner Millennium's Care Pathways for Specialty Care Referrals: Provider and Nurse Experiences, Perceptions, and Recommendations for Improvements.

BACKGROUND: Using structured templates to guide providers in communicating key information in electronic referrals is an evidence-based practice for improving care quality. To facilitate referrals in Veterans Health Administration's (VA) Cerner Millennium electronic health record, VA and Cerner have created "Care Pathways"-templated electronic forms, capturing needed information and prompting ordering of appropriate pre-referral tests. OBJECTIVE: To inform their iterative improvement, we sought to elicit experiences, perceptions, and recommendations regarding Care Pathways from frontline clinicians and staff in the first VA site to deploy Cerner Millennium. DESIGN: Qualitative interviews, conducted 12-20 months after Cerner Millennium deployment. PARTICIPANTS: We conducted interviews with primary care providers, primary care registered nurses, and specialty providers requesting and/or receiving referrals. APPROACH: We used rapid qualitative analysis. Two researchers independently summarized interview transcripts with bullet points; summaries were merged by consensus. Constant comparison was used to sort bullet points into themes. A matrix was used to view bullet points by theme and participant. RESULTS: Some interviewees liked aspects of the Care Pathways, expressing appreciation of their premise and logic. However, interviewees commonly expressed frustration with their poor usability across multiple attributes. Care Pathways were reported as being inefficient; lacking simplicity, naturalness, consistency, and effective use of language; imposing an unacceptable cognitive load; and not employing forgiveness and feedback for errors. Specialists reported not receiving the information needed for referral triaging. CONCLUSIONS: Cerner Millennium's Care Pathways, and their associated organizational policies and processes, need substantial revision across several usability attributes. Problems with design and technical limitations are compounding challenges in using standardized templates nationally, across VA sites having diverse organizational and contextual characteristics. VA is actively working to make improvements; however, significant additional investments are needed for Care Pathways to achieve their intended purpose of optimizing specialty care referrals for Veterans.

Veteran Perspectives on Population-Based Suicide Risk Screening in VHA Primary Care: Mixed-Methods Study.

BACKGROUND: In late 2018, VHA implemented a multi-stage suicide risk screening and evaluation initiative, Suicide Risk Identification Strategy, or "Risk ID," in primary care settings. OBJECTIVE: The main objective of this study was to characterize VHA primary care patient perspectives regarding population-based suicide risk screening through the Risk ID program. DESIGN: Mixed methods; survey and qualitative interviews. PARTICIPANTS: Veterans screened for suicide risk using Risk ID in primary care (n = 868) participated in a survey of veteran attitudes about screening (45% response rate); thirty additionally participated in follow-up qualitative interviews. MAIN MEASURES: The quantitative survey consisted of three questions on attitudes about screening for suicidal thoughts in primary care. In qualitative interviews, veterans were asked about their experiences with the Risk ID processes and recommendations for improving Risk ID. KEY RESULTS: Over 90% of veterans reported that it is appropriate for primary care providers or nurses/medical assistants to ask veterans about thoughts of suicide during primary care visits. Approximately half of veterans indicated that veterans should be asked about suicidal thoughts at every visit. Qualitative findings revealed that while most veterans were generally supportive and appreciated VHA screening for suicidal thoughts, they also expressed concern for the potential for inadvertent harm. Participants expressed conflicting preferences for how screening should be handled and delivered. CONCLUSIONS: Findings suggest that most veterans support the integration of standardized suicide risk assessment into routine primary care visits. However, findings also suggest that population-based suicide risk assessment should further consider patient experiences and preferences. Specifically, additional guidance or training for staff conducting suicide risk screening may be warranted to ensure patients feel heard (e.g., eye contact, expressing empathy) and increase patient understanding of the purpose of the screening and potential outcomes. These patient-centered approaches may improve patient experience and facilitate disclosure of suicidal thoughts.

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.

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.

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.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

BACKGROUND: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. OBJECTIVE: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. DESIGN: Patient self-report surveys at treatment initiation linked to medical records. SETTING: Veterans Health Administration (VA) clinics nationally, 2018-2020. PARTICIPANTS: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables. KEY RESULTS: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence. CONCLUSIONS: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Characteristics and Injury Mechanisms of Veteran Primary Care Suicide Decedents with and without Diagnosed Mental Illness.

BACKGROUND: In the United States, suicide rates are increasing among nearly all age groups. Primary care is a critical setting for suicide prevention, where interventions often rely on identifying mental health conditions as indicators of elevated suicide risk. OBJECTIVE: Quantify the proportion of suicide decedents within primary care who had no antecedent mental health or substance use diagnosis. DESIGN: Retrospective cohort study. PARTICIPANTS: Veterans who received Veterans Health Administration (VHA) primary care any time from 2000 to 2014 and died by suicide before 2015 (n = 27,741). MAIN MEASURES: We categorized decedents by whether they had any mental health or substance use diagnosis (yes/no) using ICD-9 codes available from VHA records. We compared sociodemographic, clinical, and suicide mechanism characteristics between groups using chi-square, Student's T, or Wilcoxon tests. RESULTS: Forty-five percent of decedents had no mental health or substance use diagnosis. Decedents without such a diagnosis were older (68 vs. 57 years, p < 0.001), and more likely to be male (98.3% vs. 95.8%, p < 0.001), non-Hispanic White (90.6% vs. 87.9%, p < 0.001), married/partnered (50.4% vs. 36.6%, p < 0.001), and without military service-connected disability benefits (72.6% vs. 56.9%, p < 0.001). They were also more likely to die from firearm injury (78.9% vs. 60.7%, p < 0.001). There were statistically significant differences in physical health between groups, but the magnitudes of those differences were small. Decedents without a mental health or substance use diagnosis had significantly shorter durations of enrollment in VHA healthcare, less healthcare utilization in their last year of life, and had little utilization aside from primary care visits. CONCLUSIONS AND RELEVANCE: From 2000 to 2014, of nearly thirty thousand VHA primary care patients who died by suicide, almost half had no antecedent mental health or substance use diagnosis. Within VHA primary care settings, suicide risk screening for those with and without such a diagnosis is indicated.

Mortality Among Veterans with Major Mental Illnesses Seen in Primary Care: Results of a National Study of Veteran Deaths.

BACKGROUND: Premature mortality observed among the mentally ill is largely attributable to chronic illnesses. Veterans seen within Veterans Affairs (VA) have a higher prevalence of mental illness than the general population but there is limited investigation into the common causes of death of Veterans with mental illnesses. OBJECTIVE: To characterize the life expectancy of mentally ill Veterans seen in VA primary care, and to determine the most death rates of combinations of mental illnesses. DESIGN: Retrospective cohort study of decedents. SETTING/PARTICIPANTS: Veterans seen in VA primary care clinics between 2000 and 2011 were included. Records from the VA Corporate Data Warehouse (CDW) were merged with death information from the National Death Index. MAIN MEASURES: Mental illnesses were determined using ICD9 codes. Direct standardization methods were used to calculate age-adjusted gender and cause-specific death rates per 1000 deaths for patients with and without depression, anxiety, post-traumatic stress disorder (PTSD), substance use disorder (SUD), serious mental illness (SMI), and combinations of those diagnoses. KEY RESULTS: Of the 1,763,982 death records for Veterans with 1 + primary care visit, 556,489 had at least one mental illness. Heart disease and cancer were the two leading causes of death among Veterans with or without a mental illness, accounting for approximately 1 in 4 deaths. Those with SUD (n = 204,950) had the lowest mean age at time of death (64 ± 12 years). Among men, the death rates were as follows: SUD (55.9/1000); anxiety (49.1/1000); depression (45.1/1000); SMI (40.3/1000); and PTSD (26.2/1000). Among women, death rates were as follows: SUD (55.8/1000); anxiety (36.7/1000); depression (45.1/1000); SMI (32.6/1000); and PTSD (23.1/1000 deaths). Compared to men (10.8/1000) and women (8.7/1000) without a mental illness, these rates were multiple-fold higher in men and in women with a mental illness. A greater number of mental illness diagnoses was associated with higher death rates among men and women (p < 0.0001). CONCLUSIONS: Veterans with mental illnesses, particularly those with SUD, and those with multiple diagnoses, had shorter life expectancy than those without a mental illness. Future studies should examine both patient and systemic sources of disparities in providing chronic illness care to Veterans with a mental illness.

Coordinating Care Across VA Providers and Settings: Policy and Research Recommendations from VA's State of the Art Conference.

Delivering well-coordinated care is essential for optimizing clinical outcomes, enhancing patient care experiences, minimizing costs, and increasing provider satisfaction. The Veterans Health Administration (VA) has built a strong foundation for internally coordinating care. However, VA faces mounting internal care coordination challenges due to growth in the number of Veterans using VA care, high complexity in Veterans' care needs, the breadth and depth of VA services, and increasing use of virtual care. VA's Health Services Research and Development service with the Office of Research and Development held a conference assessing the state-of-the-art (SOTA) on care coordination. One workgroup within the SOTA focused on coordination between VA providers for high-need Veterans, including (1) Veterans with multiple chronic conditions; (2) Veterans with high-intensity, focused, specialty care needs; (3) Veterans experiencing care transitions; (4) Veterans with severe mental illness; (5) and Veterans with homelessness and/or substance use disorders. We report on this workgroup's recommendations for policy and organizational initiatives and identify questions for further research. Recommendations from a separate workgroup on coordinating VA and non-VA care are contained in a companion paper. Leaders from research, clinical services, and VA policy will need to partner closely as they develop, implement, assess, and spread effective practices if VA is to fully realize its potential for delivering highly coordinated care to every Veteran.

Understanding collaborative care implementation in the Department of Veterans Affairs: core functions and implementation challenges.

BACKGROUND: The collaborative care model is an evidence-based practice for treatment of depression in which designated care managers provide clinical services, often by telephone. However, the collaborative care model is infrequently adopted in the Department of Veterans Affairs (VA). Almost all VA medical centers have adopted a co-located or embedded approach to integrating mental health care for primary care patients. Some VA medical centers have also adopted a telephone-based collaborative care model where depression care managers support patient education, patient activation, and monitoring of adherence and progress over time. This study evaluated two research questions: (1) What does a dedicated care manager offer in addition to an embedded-only model? (2) What are the barriers to implementing a dedicated depression care manager? METHODS: This study involved 15 qualitative, multi-disciplinary, key informant interviews at two VA medical centers where reimbursement options were the same- both with embedded mental health staff, but one with a depression care manager. Participant interviews were recorded and transcribed. Thematic analysis was used to identify descriptive and analytical themes. RESULTS: Findings suggested that some of the core functions of depression care management are provided as part of embedded-only mental health care. However, formal structural attention to care management may improve the reliability of care management functions, in particular monitoring of progress over time. Barriers to optimal implementation were identified at both sites. Themes from the care management site included finding assertive care managers to hire, cross-discipline integration and collaboration, and primary care provider burden. Themes from interviews at the embedded site included difficulty getting care management on leaders' agendas amidst competing priorities and logistics (staffing and space). CONCLUSIONS: Providers and administrators see depression care management as a valuable healthcare service that improves patient care. Barriers to implementation may be addressed by team-building interventions to improve cross-discipline integration and communication. Findings from this study are limited in scope to the VA healthcare system. Future investigation of whether alternative barriers exist in implementation of depression care management programs in non-VA hospital systems, where reimbursement rates may be a more prominent concern, would be valuable.

Depression Quality of Care: Measuring Quality over Time Using VA Electronic Medical Record Data.

BACKGROUND: The Veterans Health Administration (VA) has invested substantially in evidence-based mental health care. Yet no electronic performance measures for assessing the level at which the population of Veterans with depression receive appropriate care have proven robust enough to support rigorous evaluation of the VA's depression initiatives. OBJECTIVE: Our objectives were to develop prototype longitudinal electronic population-based measures of depression care quality, validate the measures using expert panel judgment by VA and non-VA experts, and examine detection, follow-up and treatment rates over a decade (2000-2010). We describe our development methodology and the challenges to creating measures that capture the longitudinal course of clinical care from detection to treatment. DESIGN AND PARTICIPANTS: Data come from the National Patient Care Database and Pharmacy Benefits Management Database for primary care patients from 1999 to 2011, from nine Veteran Integrated Service Networks. MEASURES: We developed four population-based quality metrics for depression care that incorporate a 6-month look back and 1-year follow-up: detection of a new episode of depression, 84 and 180 day follow-up, and minimum appropriate treatment 1-year post detection. Expert panel techniques were used to evaluate the measure development methodology and results. Key challenges to creating valid longitudinal measures are discussed. KEY RESULTS: Over the decade, the rates for detection of new episodes of depression remained stable at 7-8 %. Follow-up at 84 and 180 days were 37 % and 45 % in 2000 and increased to 56 % and 63 % by 2010. Minimum appropriate treatment remained relatively stable over the decade (82-84 %). CONCLUSIONS: The development of valid longitudinal, population-based quality measures for depression care is a complex process with numerous challenges. If the full spectrum of care from detection to follow-up and treatment is not captured, performance measures could actually mask the clinical areas in need of quality improvement efforts.

Prevalence, Comorbidity, and Prognosis of Mental Health Among US Veterans.

OBJECTIVES: We evaluated the association of mental illnesses with clinical outcomes among US veterans and evaluated the effects of Primary Care-Mental Health Integration (PCMHI). METHODS: A total of 4 461 208 veterans were seen in the Veterans Health Administration's patient-centered medical homes called Patient Aligned Care Teams (PACT) in 2010 and 2011, of whom 1 147 022 had at least 1 diagnosis of depression, posttraumatic stress disorder (PTSD), substance use disorder (SUD), anxiety disorder, or serious mental illness (SMI; i.e., schizophrenia or bipolar disorder). We estimated 1-year risk of emergency department (ED) visits, hospitalizations, and mortality by mental illness category and by PCMHI involvement. RESULTS: A quarter of all PACT patients reported 1 or more mental illnesses. Depression, SMI, and SUD were associated with increased risk of hospitalization or death. PTSD was associated with lower odds of ED visits and mortality. Having 1 or more contact with PCMHI was associated with better outcomes. CONCLUSIONS: Mental illnesses are associated with poor outcomes, but integrating mental health treatment in primary care may be associated with lower risk of those outcomes.

Primary care mental health integration to improve early treatment engagement for veterans who screen positive for depression.

OBJECTIVE: To examine the relationship between the penetration (or reach) of a national program aiming to integrate mental health clinicians into all primary care clinics (PC-MHI) and rates of guideline-concordant follow-up and treatment among clinic patients newly identified with depression in the Veterans Health Administration (VA). DATA SOURCES/STUDY SETTING: 15,155 screen-positive patients 607,730 patients with 2-item Patient Health Questionnaire scores in 82 primary care clinics, 2015-2019. STUDY DESIGN: In this retrospective cohort study, we used established depression care quality measures to assess primary care patients who (a) newly screened positive (score ≥3) and (b) were identified with depression by clinicians via diagnosis and/or medication (n = 15,155; 15,650 patient-years). Timely follow-up included ≥3 mental health, ≥3 psychotherapy, or ≥3 primary care visits for depression. Minimally appropriate treatment included ≥4 mental health visits, ≥3 psychotherapy, or ≥60 days of medication. In multivariate regressions, we examined whether higher rates of PC-MHI penetration in clinic (proportion of total primary care patients in a clinic who saw any PC-MHI clinician) were associated with greater depression care quality among cohort patients, adjusting for year, healthcare system, and patient and clinic characteristics. DATA COLLECTION/EXTRACTION METHODS: Electronic health record data from 82 VA clinics across three states. PRINCIPAL FINDINGS: A median of 9% of all primary care patients were seen by any PC-MHI clinician annually. In fully adjusted models, greater PC-MHI penetration was associated with timely depression follow-up within 84 days (∆P = 0.5; SE = 0.1; p < 0.001) and 180 days (∆P = 0.3; SE = 0.1; p = 0.01) of a positive depression screen. Completion of at least minimal treatment within 12 months was high (77%), on average, and not associated with PC-MHI penetration. CONCLUSIONS: Greater PC-MHI program penetration was associated with early depression treatment engagement at 84-/180-days among clinic patients newly identified with depression, with no effect on already high rates of completion of minimally sufficient treatment within the year.

Determinants of readiness for primary care-mental health integration (PC-MHI) in the VA Health Care System.

BACKGROUND: Depression management can be challenging for primary care (PC) settings. While several evidence-based models exist for depression care, little is known about the relationships between PC practice characteristics, model characteristics, and the practice's choices regarding model adoption. OBJECTIVE: We examined three Veterans Affairs (VA)-endorsed depression care models and tested the relationships between theoretically-anchored measures of organizational readiness and implementation of the models in VA PC clinics. DESIGN: 1) Qualitative assessment of the three VA-endorsed depression care models, 2) Cross-sectional survey of leaders from 225 VA medium-to-large PC practices, both in 2007. MAIN MEASURES: We assessed PC readiness factors related to resource adequacy, motivation for change, staff attributes, and organizational climate. As outcomes, we measured implementation of one of the VA-endorsed models: collocation, Translating Initiatives in Depression into Effective Solutions (TIDES), and Behavioral Health Lab (BHL). We performed bivariate and, when possible, multivariate analyses of readiness factors for each model. KEY RESULTS: Collocation is a relatively simple arrangement with a mental health specialist physically located in PC. TIDES and BHL are more complex; they use standardized assessments and care management based on evidence-based collaborative care principles, but with different organizational requirements. By 2007, 107 (47.5 %) clinics had implemented collocation, 39 (17.3 %) TIDES, and 17 (7.6 %) BHL. Having established quality improvement processes (OR 2.30, [1.36, 3.87], p = 0.002) or a depression clinician champion (OR 2.36, [1.14, 4.88], p = 0.02) was associated with collocation. Being located in a VA regional network that endorsed TIDES (OR 8.42, [3.69, 19.26], p < 0.001) was associated with TIDES implementation. The presence of psychologists or psychiatrists on PC staff, greater financial sufficiency, or greater spatial sufficiency was associated with BHL implementation. CONCLUSIONS: Both readiness factors and characteristics of depression care models influence model adoption. Greater model simplicity may make collocation attractive within local quality improvement efforts. Dissemination through regional networks may be effective for more complex models such as TIDES.

Primary Care and Mental Health Staff Perspectives on Universal Suicide Risk Screening and Care Coordination.

Little is known about staff or patient perspectives on suicide risk screening programs. The objectives of this study were to characterize Veterans Health Administration (VHA) primary care and mental health staff perspectives regarding the VHA Suicide Risk Identification Strategy screening and assessment program and to describe coordination of suicide prevention-related care following positive screening results. Qualitative interviews were conducted with 40 primary care and mental health staff at 12 VHA facilities. An inductive-deductive hybrid approach was used to conduct a thematic analysis. Several key themes were identified. Primary care and mental health staff participants accepted having a structured process for screening for suicidal ideation and conducting risk assessments, but both groups noted limitations and challenges with initial assessment and care coordination following screening. Mental health staff reported more concerns than primary care staff about negative impacts of the screening and assessment process on treatment. Both groups felt that better training was needed for primary care staff to effectively discuss and evaluate suicide risk. The results suggested that additional modifications of the screening and assessment process are needed for patients already known to have elevated risk of suicide or chronic suicidal ideation.

Does screening for PTSD lead to VA mental health care? Identifying the spectrum of initial VA screening actions.

Despite the active posttraumatic stress disorder (PTSD) screening program in Department of Veterans Affairs (VA) primary care clinics and the availability of empirically supported treatments for PTSD at VA, many veterans for whom screening suggests treatment may be indicated do not gain access to VA-based mental health care. To determine where we may be losing veterans to follow-up, we need to begin by identifying the initial action taken in response to a positive PTSD screen in primary care. Using VA administrative data and chart review, we identified the spectrum of initial actions taken after veterans screened positive for PTSD in VA primary care clinics nationwide between October 2017 and September 2018 (N = 41,570). We collapsed actions into those that could lead to VA-based mental health care (e.g., consult placed to a VA mental health clinic) versus not (e.g., veteran declined care), and then examined the association between these categories of actions and contextual- and individual-level variables. More than 61% of veterans with positive PTSD screens had evidence that an initial action toward VA-based mental health care was taken. Urban-dwelling and female veterans were significantly more likely to have evidence of these initial actions, whereas White and Vietnam-era veterans were significantly less likely to have this evidence. Our findings suggest that most veterans screening positive for PTSD in VA primary care clinics have evidence of initial actions taken toward VA-based mental health care; however, a substantial minority do not, making them unlikely to receive follow-up care. Findings highlight the potential benefit of targeted primary care-based access interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

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.