Dugani, Sagar B

Abstract Introduction: There is growing focus on community-engaged research (CEnR) in the clinical and translational sciences, supportive policies, environments, and processes within academic medical centers. The purpose of this evaluation was to gather academic and community member perspectives on key actionable strategies for supporting CEnR in an academic medical center. Materials and Methods: We used a sequential mixed method design by first conducting qualitative in-depth interviews with academic leaders, investigators, and staff (total, n = 54) and focus group discussions with community members ( n = 33). Themes from these qualitative data informed the adaptation of an institutional survey that gathered perspectives from a broader group of academic and community members affiliated with the institution ( n = 85). Both qualitative and quantitative data were integrated to inform actionable strategies. Results: Survey findings revealed that the institution was perceived as committed to health equity and CEnR, but institutional respondents expressed more concerns than community members about instrumental support of the work. Interviews and focus groups highlighted the opportunity to better align research efforts with community needs through more robust CEnR infrastructure and through interface with the clinical practice to promote patient access. Academic and community members emphasized the need for authentic, sustained engagement beyond federal mandates, including financial investment, capacity-building, and fostering trust. Leaders acknowledged structural challenges, siloed efforts, and the necessity for stronger coordination to increase integration of community engagement into the institutional mission. Conclusion: Findings suggest that aligning academic leaders, researchers, staff perspectives, and community members is essential to support and promote CEnR at an institutional level.

ABSTRACT Importance Rural-urban disparities in chronic disease prevalence are well established; however, the extent to which individual-level socioeconomic status (SES) contributes to these disparities remains unclear. Objective To examine the associations of rurality and SES with the prevalence of five most burdensome chronic diseases among adults. Design We conducted a retrospective cross-sectional study of adults across 27 Upper Midwest counties using the Expanded Rochester Epidemiology Project (E-REP) medical record data linkage system to evaluate associations between rurality, SES and chronic disease prevalence. Prevalence of clinically diagnosed asthma, diabetes, hypertension, coronary heart disease, and mood disorders was identified from International Classification of Diseases ICD9/10 codes over a five-year period (2014-2019). Setting Population based Participants Adults over 18 years residing in the 27 E-REP counties, excluding those missing rural-urban residence status. Exposure HOUSES index, an individual-level measure of SES, served as the primary measure, while rurality based on Rural Urban Commuting Area (RUCA) codes 4-10 was the secondary measure. Main Outcome Prevalence of the five clinically diagnosed chronic diseases was identified using ICD9/10 codes from 2014–2019. Mixed effect logistic regression models were used and adjusted for demographics and general medical examination receipt, to assess rural-urban and SES differences for prevalence of each chronic disease. Results Among 455,802 adults with available HOUSES index, 42.8% lived in rural areas, 53.8% were female and 87.4% were non-Hispanic White. In the unadjusted analysis, rural and urban populations showed comparable asthma and CHD prevalence, while mood disorders, hypertension, and diabetes were more common in urban areas. After adjusting for demographic factors and healthcare utilization, rural-urban differences were no longer statistically significant, whereas SES remained strongly associated with all diseases in a dose response manner (e.g., adjusted Odds Ratio for hypertension (ref: HOUSES index Q4): 1.14, 1.27, and 1.42 for HOUSES index Q3, Q2, and Q1, respectively). Conclusions and Relevance Individual-level SES measured by the HOUSES index, was more strongly associated with chronic disease prevalence than rurality, supporting its integration into population health assessment and risk stratification.

We highlight the complex management considerations in a patient with cerebral amyloid angiopathy who developed venous thromboembolism, explicitly navigating the balance between risks of thromboembolism and bleeding with anticoagulation therapy. This case highlights a man in his 70s who presented to the emergency department with an acute right frontal intracerebral haemorrhage secondary to cerebral amyloid angiopathy with an acute segmental pulmonary embolism managed with an inferior vena cava (IVC) filter. During hospitalisation, the patient developed a large expansile thrombus extending inferior from the IVC filter to the right popliteal vein. This necessitated the initiation of anticoagulation therapy, beginning with unfractionated heparin, transitioning to enoxaparin and ultimately to apixaban. He was treated with 3 months of anticoagulation with no haemorrhagic complications. The patient’s successful outcome underscored the potential for safe and effective anticoagulation management in this complex clinical scenario.

Poor diet is the leading preventable risk factor for death worldwide, associated with over 10 million premature deaths and USD 8 trillion related costs every year. Artificial intelligence or AI is rapidly emerging as the most historically disruptive, innovatively dynamic, rapidly scaled, cost-efficient, and economically productive technology (which is increasingly providing transformative countermeasures to these negative health trends, especially in low- and middle-income countries (LMICs) and underserved communities which bear the greatest burden from them). Yet widespread confusion persists among healthcare systems and policymakers on how to best identify, integrate, and evolve the safe, trusted, effective, affordable, and equitable AI solutions that are right for their communities, especially in public health nutrition. We therefore provide here the first known global, comprehensive, and actionable narrative review of the state of the art of AI-accelerated nutrition assessment and healthy eating for healthcare systems, generated by the first automated end-to-end empirical index for responsible health AI readiness and maturity: the Responsible Health AI readiness and Maturity Index (RHAMI). The index is built and the analysis and review conducted by a multi-national team spanning the Global North and South, consisting of front-line clinicians, ethicists, engineers, executives, administrators, public health practitioners, and policymakers. RHAMI analysis identified the top-performing healthcare systems and their nutrition AI, along with leading use cases including multimodal edge AI nutrition assessments as ambient intelligence, the strategic scaling of practical embedded precision nutrition platforms, and sovereign swarm agentic AI social networks for sustainable healthy diets. This index-based review is meant to facilitate standardized, continuous, automated, and real-time multi-disciplinary and multi-dimensional strategic planning, implementation, and optimization of AI capabilities and functionalities worldwide, aligned with healthcare systems’ strategic objectives, practical constraints, and local cultural values. The ultimate strategic objectives of the RHAMI’s application for AI-accelerated public health nutrition are to improve population health, financial efficiency, and societal equity through the global cooperation of the public and private sectors stretching across the Global North and South.

Abstract Hospital medicine (HM), a well‐established clinical specialty, requires clarity of research priorities to identify target areas for investment in HM research infrastructure and activities. The Society of Hospital Medicine's Research Committee developed a 20‐item survey and used purposeful sampling of US hospitalist leaders to prioritize primary research topic domains and subdomains. Respondents were asked to rank their HM research priorities using a scale (1 [highest] to 8 [lowest]). Of 239 surveys distributed, 81 (34%) responded. Respondents were predominantly White (53%) and from academic institutions (57%). “ Overall, 46% of respondents were male and 33% were female. ” Top‐ranked research domains were innovation in care delivery (median 2, IQR 1,3), value‐based care (median 3.5, IQR 2,6), and health disparities (median 4, IQR 2,5). This survey identified top HM research priorities including systems‐based topics and health disparities, aligning with HM's identity as a specialty focused on improving systems of care and inequities in care.

Abstract Background Premature coronary heart disease (CHD) is a major cause of death in women. We aimed to characterize biomarker profiles of women who developed CHD before and after age 65 years. Methods In the Women’s Health Study (median follow-up 21.5 years), women were grouped by age and timing of incident CHD: baseline age <65 years with premature CHD by age 65 years (25 042 women; 447 events) and baseline age ≥65 years with nonpremature CHD (2982 women; 351 events). Associations of 44 baseline plasma biomarkers measured using standard assays and a nuclear magnetic resonance (NMR)-metabolomics assay were analyzed using Cox models adjusted for clinical risk factors. Results Twelve biomarkers showed associations only with premature CHD and included lipoprotein(a), which was associated with premature CHD [adjusted hazard ratio (HR) per SD: 1.29 (95% CI 1.17–1.42)] but not with nonpremature CHD [1.09(0.98–1.22)](Pinteraction = 0.02). NMR-measured lipoprotein insulin resistance was associated with the highest risk of premature CHD [1.92 (1.52–2.42)] but was not associated with nonpremature CHD (Pinteraction <0.001). Eleven biomarkers showed stronger associations with premature vs nonpremature CHD, including apolipoprotein B. Nine NMR biomarkers showed no association with premature or nonpremature CHD, whereas 12 biomarkers showed similar significant associations with premature and nonpremature CHD, respectively, including low-density lipoprotein (LDL) cholesterol [1.30(1.20–1.45) and 1.22(1.10–1.35)] and C-reactive protein [1.34(1.19–1.50) and 1.25(1.08–1.44)]. Conclusions In women, a profile of 12 biomarkers was selectively associated with premature CHD, driven by lipoprotein(a) and insulin-resistant atherogenic dyslipoproteinemia. This has implications for the development of biomarker panels to screen for premature CHD.

AbstractBackgroundHospital‐at‐home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost‐effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates.ObjectiveTo describe the development and implementation of a BPA for identifying HaH eligible patients in Mayo Clinic's Advanced Care at Home (ACH) program, and to evaluate the provider response and the patient characteristics that triggered the BPA.Design, Setting, and ParticipantsWe conducted a retrospective multicenter study of hospitalized patients who triggered the BPA notification for ACH eligibility between March and December 2021 at Mayo Clinic in Jacksonville, FL and Mayo Clinic Health System in Eau Claire, WI. We extracted demographic and diagnosis data from the patients as well as characteristics of the providers who received the BPA notification.InterventionThe BPA was developed based on the ACH inclusion and exclusion criteria, which were derived from clinical guidelines, literature review, and expert consensus. The BPA was integrated into the EHR and displayed a pop‐up message to the provider when a patient met the criteria for ACH eligibility. The provider could choose to refer the patient to ACH, dismiss the notification, or defer the decision.Main Outcomes and MeasuresThe main outcomes were the number and proportion of BPA notifications that resulted in a referral to ACH, and the number and proportion of referrals that were accepted by the ACH clinical team and transferred to ACH. We also analyzed the factors associated with the provider's decision to refer or not refer the patient to ACH, such as the provider's role, location, and specialty.ResultsDuring the study period, 8962 notifications were triggered for 2847 patients. Providers opted to refer 711 (11.4%) of the total notifications linked to 324 unique patients. After review by the ACH clinical team, 31 of the 324 referrals (9.6%) met clinical and social criteria and were transferred to ACH. In multivariable analysis, Wisconsin nurses, physician assistants, and in‐training personnel had lower odds of referring the patients to ACH when compared to attending physicians.