Faculty Bibliography

BACKGROUND: Panel Management can expand prevention and chronic illness management beyond the office visit, but there is limited evidence for its effectiveness or guidance on how best to incorporate it into practice. OBJECTIVE: We aimed to test the effectiveness of incorporating panel management into clinical practice by incorporating Panel Management Assistants (PMAs) into primary care teams with and without panel management education. DESIGN: We conducted an 8-month cluster-randomized controlled trial of panel management for improving hypertension and smoking cessation outcomes among veterans. PATRICIPANTS: Twenty primary care teams from the Veterans Affairs New York Harbor were randomized to control, panel management support, or panel management support plus education groups. Teams included 69 clinical staff serving 8,153 hypertensive and/or smoking veterans. INTERVENTIONS: Teams assigned to the intervention groups worked with non-clinical Panel Management Assistants (PMAs) who monitored care gaps and conducted proactive patient outreach, including referrals, mail reminders and motivational interviewing by telephone. MAIN MEASURES: Measurements included mean systolic and diastolic blood pressure, proportion of patients with controlled blood pressure, self-reported quit attempts, nicotine replacement therapy (NRT) prescriptions, and referrals to disease management services. KEY RESULTS: Change in mean blood pressure, blood pressure control, and smoking quit rates were similar across study groups. Patients on intervention teams were more likely to receive NRT (OR = 1.4; 95 % CI 1.2-1.6) and enroll in the disease management services MOVE! (OR = 1.2; 95 % CI 1.1-1.6) and Telehealth (OR = 1.7, 95 % CI 1.4-2.1) than patients on control teams. CONCLUSIONS: Panel Management support for primary care teams improved process, but not outcome variables among veterans with hypertension and smoking. Incorporating PMAs into teams was feasible and highly valued by the clinical staff, but clinical impact may require a longer intervention.

PURPOSE: The authors performed a review of 30 Accelerating Change in Medical Education full grant submissions and an analysis of the health systems science (HSS)-related curricula at the 11 grant recipient schools to develop a potential comprehensive HSS curricular framework with domains and subcategories. METHOD: In phase 1, to identify domains, grant submissions were analyzed and coded using constant comparative analysis. In phase 2, a detailed review of all existing and planned syllabi and curriculum documents at the grantee schools was performed, and content in the core curricular domains was coded into subcategories. The lead investigators reviewed and discussed drafts of the categorization scheme, collapsed and combined domains and subcategories, and resolved disagreements via group discussion. RESULTS: Analysis yielded three types of domains: core, cross-cutting, and linking. Core domains included health care structures and processes; health care policy, economics, and management; clinical informatics and health information technology; population and public health; value-based care; and health system improvement. Cross-cutting domains included leadership and change agency; teamwork and interprofessional education; evidence-based medicine and practice; professionalism and ethics; and scholarship. One linking domain was identified: systems thinking. CONCLUSIONS: This broad framework aims to build on the traditional definition of systems-based practice and highlight the need for medical and other health professions schools to better align education programs with the anticipated needs of the systems in which students will practice. HSS will require a critical investigation into existing curricula to determine the most efficient methods for integration with the basic and clinical sciences.

Panel management, a set of tools and processes for proactively caring for patient populations, has potential to reduce morbidity and improve outcomes between office visits. We examined primary care staff's self-efficacy in implementing panel management, its correlates, and an intervention's impact on this self-efficacy. Primary care teams at two Veterans Health Administration (VA) hospitals were assigned to control or intervention conditions. Staff were surveyed at baseline and post-intervention, with a random subset interviewed post-intervention. Panel management self-efficacy was higher among staff participating in the panel management intervention. Self-efficacy was significantly correlated with sufficient training, aspects of team member interaction, and frequency of panel management use. Panel management self-efficacy was modest among primary care staff at two VA hospitals. Team level interventions may improve primary care staff's confidence in practicing panel management, with this greater confidence related to greater team involvement with, and use of panel management.

The Graduate Medical Education (GME) system in the United States (US) has garnered worldwide respect, graduating over 25,000 new physicians from over 8,000 residency and fellowship programs annually. GME is the portal of entry to medical practice and licensure in the US, and the pathway through which resident physicians develop the competence to practice independently and further develop their career plans. The number and specialty distribution of available GME positions shapes the overall composition of our national workforce; however, GME is failing to provide appropriate programs that support the delivery of our society's system of healthcare. This paper, prepared by the Health Policy Education Subcommittee of the Society of General Internal Medicine (SGIM) and unanimously endorsed by SGIM's Council, outlines a set of recommendations on how to reform the GME system to best prepare a physician workforce that can provide high quality, high value, population-based, and patient-centered health care, aligned with the dynamic needs of our nation's healthcare delivery system. These recommendations include: accurate workforce needs assessment, broadened GME funding sources, increased transparency of the use of GME dollars, and implementation of incentives to increase the accountability of GME-funded programs for the preparation and specialty selection of their program graduates.

BACKGROUND:Over a third of US adults carry a diagnosis of prediabetes, 70% of whom may progress to type 2 diabetes mellitus ("diabetes"). Community health workers (CHWs) can help patients undertake healthy behavior to prevent diabetes. However, there is limited guidance to integrate CHWs in primary care, specifically to address CHWs' dual clinic-based and community-oriented role. OBJECTIVE:Using evidence from CHWs' adaptations of a diabetes-prevention intervention in safety-net hospitals in New York City, we examine the nature, intent, and possible consequences of CHWs' actions on program fidelity. We propose strategies for integrating CHWs in primary care. DESIGN/METHODS:Case study drawing on the Model for Adaptation Design and Impact (MADI) to analyze CHWs' actions during implementation of CHORD (Community Health Outreach to Reduce Diabetes), a cluster-randomized pragmatic trial (2017-2022) at Manhattan VA and Bellevue Hospital. PARTICIPANTS/METHODS:CHWs and clinicians in the CHORD study, with a focus in this analysis on CHWs. APPROACH/METHODS:Semi-structured interviews and focus group discussion with CHWs (n=4); semi-structured interviews with clinicians (n=17). Interpretivist approach to explain CHWs' adaptations using a mix of inductive and deductive analysis. KEY RESULTS/RESULTS:CHWs' adaptations extended the intervention in three ways: by extending social assistance, healthcare access, and operational tasks. The adaptations were intended to improve fit, reach, and retention, but likely had ripple effects on implementation outcomes. CHWs' focus on patients' complex social needs could divert them from judiciously managing their caseload. CONCLUSIONS:CHWs' community knowledge can support patient engagement, but overextension of social assistance may detract from protocolized health-coaching goals. CHW programs in primary care should explicitly delineate CHWs' non-health support to patients, include multiprofessional teams or partnerships with community-based organizations, establish formal communication between CHWs and clinicians, and institute mechanisms to review and iterate CHWs' work to resolve challenges in their community-oriented role.

OBJECTIVES:This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes. DESIGN, SETTING AND PARTICIPANTS:We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes. PRIMARY OUTCOME AND METHODS:We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas. RESULTS:We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.

BACKGROUND:It is critical to assess implementation fidelity of evidence-based interventions and factors moderating fidelity, to understand the reasons for their success or failure. However, fidelity and fidelity moderators are seldom systematically reported. The study objective was to conduct a concurrent implementation fidelity evaluation and examine fidelity moderators of CHORD (Community Health Outreach to Reduce Diabetes), a pragmatic, cluster-randomized, controlled trial to test the impact of a Community Health Workers (CHW)-led health coaching intervention to prevent incident type 2 Diabetes Mellitus in New York (NY). METHODS:We applied the Conceptual Framework for Implementation Fidelity to assess implementation fidelity and factors moderating it across the four core intervention components: patient goal setting, education topic coaching, primary care (PC) visits, and referrals to address social determinants of health (SDH), using descriptive statistics and regression models. PC patients with prediabetes receiving care from safety-net patient-centered medical homes (PCMHs) at either, VA NY Harbor or at Bellevue Hospital (BH) were eligible to be randomized into the CHW-led CHORD intervention or usual care. Among 559 patients randomized and enrolled in the intervention group, 79.4% completed the intake survey and were included in the analytic sample for fidelity assessment. Fidelity was measured as coverage, content adherence and frequency of each core component, and the moderators assessed were implementation site and patient activation measure. RESULTS:Content adherence was high for three components with nearly 80.0% of patients setting ≥ 1 goal, having ≥ 1 PC visit and receiving ≥ 1 education session. Only 45.0% patients received ≥ 1 SDH referral. After adjusting for patient gender, language, race, ethnicity, and age, the implementation site moderated adherence to goal setting (77.4% BH vs. 87.7% VA), educational coaching (78.9% BH vs. 88.3% VA), number of successful CHW-patient encounters (6 BH vs 4 VA) and percent of patients receiving all four components (41.1% BH vs. 25.7% VA). CONCLUSIONS:The fidelity to the four CHORD intervention components differed between the two implementation sites, demonstrating the challenges in implementing complex evidence-based interventions in different settings. Our findings underscore the importance of measuring implementation fidelity in contextualizing the outcomes of randomized trials of complex multi-site behavioral interventions. TRIAL REGISTRATION:The trial was registered with ClinicalTrials.gov on 30/12/2016 and the registration number is NCT03006666 .

INTRODUCTION:Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS:We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS:The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS:PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.

While every disease could affect a patient's prognosis, published studies continue to use indices that include a selective list of diseases to predict prognosis, which may limit its accuracy. This paper compares 6-month mortality predicted by a multimorbidity index (MMI) that relies on all diagnoses to the Deyo version of the Charlson index (DCI), a popular index that utilizes a selective set of diagnoses. In this retrospective cohort study, we used data from the Veterans Administration Diabetes Risk national cohort that included 6,082,018 diabetes-free veterans receiving primary care from January 1, 2008 to December 31, 2016. For the MMI, 7805 diagnoses were assigned into 19 body systems, using the likelihood that the disease will increase risk of mortality. The DCI used 17 categories of diseases, classified by clinicians as severe diseases. In predicting 6-month mortality, the cross-validated area under the receiver operating curve for the MMI was 0.828 (95% confidence interval of 0.826-0.829) and for the DCI was 0.749 (95% confidence interval of 0.748-0.750). Using all available diagnoses (MMI) led to a large improvement in accuracy of predicting prognosis of patients than using a selected list of diagnosis (DCI).

BACKGROUND:While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. METHODS:We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018-2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. RESULTS:In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110-140) to 50 (95%CI,30-80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640-2040) to 810 (95%CI,710-930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020-2021 from 330 (95%CI,310-350) to 770 (95%CI,720-820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. CONCLUSIONS:Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity.