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Using data from a Veterans Health Administration national primary care survey, this study identified the most highly rated tools and care approaches for patients with complex needs and how preferences varied by professional role, staffing, and training. Nurses were significantly more likely to rate most tools as very important as compared with primary care providers. Having a fully staffed team was also significantly associated with a very important rating on all tools. Nurses and fully staffed teams reported a greater likeliness to use most care approaches, and those with perceived need for training reporting a lower likeliness to use., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract: U.S. Veterans Affairs (VA) patients' multi-system use can create challenges for VA clinicians who are responsible for coordinating Veterans' use of non-VA care, including VA-purchased care ("Community Care") and Medicare.To examine the relationship between drive distance and time-key eligibility criteria for Community Care-and VA reliance (proportion of care received in VA versus Medicare and Community Care) among Veterans at high risk for hospitalization. We used prepolicy data to anticipate the impact of the 2014 Choice Act and 2018 Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION Act), which expanded access to Community Care.Cross-sectional analysis using fractional logistic regressions to examine the relationship between a Veteran's reliance on VA for outpatient primary, mental health, and other specialty care and their drive distance/time to a VA facility.Thirteen thousand seven hundred three Veterans over the age of 65 years enrolled in VA and fee-for-service Medicare in federal fiscal year 2014 who were in the top 10th percentile for hospitalization risk.Key explanatory variables were patients' drive distance to VA > 40 miles (Choice Act criteria) and drive time to VA ≥ 30 minutes for primary and mental health care and ≥60 minutes for specialty care (MISSION Act criteria).Veterans at high risk for hospitalization with drive distance eligibility had increased odds of an outpatient specialty care visit taking place in VA when compared to Veterans who did not meet Choice Act eligibility criteria (odds ratio = 1.10, 95% confidence interval 1.05-1.15). However, drive time eligibility (MISSION Act criteria) was associated with significantly lower odds of an outpatient specialty care visit taking place in VA (odds ratio = 0.69, 95% confidence interval 0.67, 0.71). Neither drive distance nor drive time were associated with reliance for outpatient primary care or mental health care.VA patients who are at high risk for hospitalization may continue to rely on VA for outpatient primary care and mental health care despite access to outside services, but may increase use of outpatient specialty care in the community in the MISSION era, increasing demand for multi-system care coordination., Competing Interests: The authors have no conflicts of interests to disclose., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death., Study Design and Setting: SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression., Results: Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14)., Conclusion: Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Engaging patients as partners in their care is clinically appealing, yet challenging to implement, and we lack a measurement framework that is applicable to vulnerable populations. To address this gap, we conducted a qualitative study to refine a conceptual framework that reflects an individual's propensity to engage with care., Objectives: Our objectives were to refine the framework's domains of engagement behavior; identify key behaviors within each domain that describe engagement with providers, health systems or settings; and illustrate examples for each behavior where higher self-efficacy describes an opportunity to enhance engagement, and lower self-efficacy describes difficulties with engagement that risk burden., Research Design and Sample: We elicited patient perspectives by conducting individual semistructured interviews with veterans receiving care for mental health and/or chronic conditions from the Veterans Health Administration. Data were analyzed using the framework method., Results: The resulting engagement framework encompassed 4 interrelated domains: Self-Management, Health Information Use, Collaborative Communication, and Healthcare Navigation. The propensity to engage with care was conceptualized as the cumulative self-efficacy to engage in behaviors across these domains. Results emphasize the collaborative nature of engagement behaviors and the impact of veteran cultural influences via perceptions of collective efficacy., Conclusions: This framework can be applied to judgments regarding a patient's propensity to engage in care. Because self-efficacy is an individual's context-specific judgment of their capabilities, this framework may inform health care and social service interventions that aim to engage patients. This maybe especially useful for public sector settings and populations with social risks.

Objective: The intensive and varied services required by high-need patients have inspired a number of new care delivery models; however, evidence of their effectiveness is mixed. This study evaluated whether augmenting a patient-centered medical home (PCMH) with intensive outpatient management enhances high-need patients' care processes., Research Design: Retrospective analysis using differences-in-differences and χ tests., Subjects: Of 545 high-need patients receiving PCMH care, 140 were previously randomly selected for the intensive outpatient management program; the remaining received usual care., Measures: We evaluated program effects on care continuity (proportion of primary care visits with assigned primary care physician); access (proportion of telephone visits out of all primary care encounters, missed appointment rate); care coordination (rate of follow-up after hospital discharge, new telehealth enrollment); and patient engagement (rates of online personal health record registration, advance directive completion)., Results: Compared with patients receiving usual care, patients enrolled in intensive management experienced a 5.9% increase in proportion of primary care visits with an assigned primary care physician (P<0.001) and a 17.9% increase in proportion of telephone-based visits (P<0.001). Patients in the program had 7.5% higher rates of telehealth referral (P=0.01), 17.2% higher rates of advance directive completion (P<0.01), and 9.3% higher rates of personal health record registration (P=0.02). There was no effect on missed appointments or posthospital discharge visit rates., Conclusions: Augmenting a PCMH with intensive outpatient management may have positive effects on primary care processes related to continuity, access, coordination, and patient engagement.

Background: Variable adherence to assigned conditions is common in randomized clinical trials., Objectives: A generalized modeling framework under longitudinal data structures is proposed for regression estimation of the causal effect of variable adherence on outcome, with emphasis upon adjustment for unobserved confounders., Research Design: A nonlinear, nonparametric random-coefficients modeling approach is described. Estimates of local average treatment effects among compliers can be obtained simultaneously for all assigned conditions to which participants are randomly assigned within the trial. Two techniques are combined to address time-varying and time-invariant unobserved confounding-residual inclusion and nonparametric random-coefficients modeling. Together these yield a compound, 2-stage residual inclusion, instrumental variables model., Subjects: The proposed method is illustrated through a set of simulation studies to examine small-sample bias and in application to neurocognitive outcome data from a large, multicenter, randomized clinical trial in sleep medicine for continuous positive airway pressure treatment of obstructive sleep apnea., Results: Results of simulation studies indicate that, relative to a standard comparator, the proposed estimator reduces bias in estimates of the causal effect of variable adherence. Bias reductions were greatest at higher levels of residual variance and when confounders were time varying., Conclusions: The proposed modeling framework is flexible in the distributions of outcomes that can be modeled, applicable to repeated measures longitudinal structures, and provides effective reduction of bias due to unobserved confounders.

Background: Posttraumatic stress disorder (PTSD) is associated with an increased risk for medical comorbidities that may prevent participation in psychotherapy. The present study investigated whether medical comorbidities were associated with lower initiation rates and fewer psychotherapy visits for PTSD. Because women are more likely to initiate psychotherapy after traumatic events, we also assessed whether relationships were weaker among women., Methods: Veterans (N=482, 47% women) recently diagnosed with PTSD completed a survey assessing demographics, mood, functional status, and interest in treatment. Data on medical comorbidities, psychotherapy visits, antidepressant prescriptions, and service connection were assessed longitudinally through administrative files. Logistic and negative binomial regressions assessed associations between number of medical comorbidities in the 2 years before the survey and the initiation and number of psychotherapy visits for PTSD in the year after the survey. All analyses were stratified by sex and controlled for survey and administrative variables., Results: The relationship between medical comorbidities and number of psychotherapy visits was stronger among women than among men. A greater number of medical comorbidities was associated with significantly fewer psychotherapy visits in the total sample [incidence rate ratio: 0.91; 95% confidence interval (CI): 0.83, 1.00] and among women (incidence rate ratio: 0.87; 95% CI: 0.77, 0.99), but not among men (95% CI: 0.75, 1.01). Medical comorbidities were not associated with the initiation of psychotherapy among men or women., Conclusions: Addressing medical comorbidities may help individuals remain in psychotherapy for PTSD. Medical comorbidities may play a larger role in the number of psychotherapy visits among women than men.