Citations

ABSTRACT Purpose To explore the opioid decision‐making process of prescribers in primary care, surgery, and subspecialty pain management. Methods This was a multistage qualitative‐qualitative study with clinicians involved in opioid prescribing and management from a single healthcare organization with locations in Minnesota, Florida, Arizona, and Wisconsin. Clinicians in this study included physicians, nurse practitioners (NPs), and physician assistants (PAs) practicing in primary care, surgery, or pain medicine. The interview guide was organized in three domains of a conceptual model for opioid use: (1) prescriber characteristics, (2) patient characteristics, and (3) practice environment characteristics. Individual interviews ( N = 20) were performed first, and data were summarized using a rapid template‐based approach. Subsequently, focus groups (FGs) ( N = 4) were conducted with an additional 21 clinicians to confirm and explore interview results. Data from interviews and FGs were analyzed using a common codebook. Results Findings were organized as decision factors into the three domains. Factors in the prescriber domain included characteristics such as formal pain management training, as well as factors that influenced knowledge and beliefs (e.g., advice from or observation of supervising clinicians and expert colleagues). Decision factors in the patient domain included patient pain etiology, comorbid medical or mental health conditions, and demographic characteristics. Clinicians' perceptions of these factors, such as viewing older age as having lesser risk for opioid misuse, influenced prescribing decisions. Clinicians also described challenges associated with “legacy patients” whose therapy was previously managed by a different clinician. Primary care clinicians, in particular, expressed difficulties managing post‐surgical or trauma‐related pain after care transition. Relevant decision factors reported by prescribers in the practice environment domain included guidelines and practice oversight. Most participants reported practicing in an environment that promoted conservative opioid use and having access to standardized institutional guidelines that reduced perceived decision‐making burden. Clinicians described how they used prescribing guidelines to help them communicate with patients about opioid use and prescribing decisions. Conclusions Intersecting prescriber, patient, and practice environment factors shape opioid analgesic prescribing decisions. Available resources in the practice environment, including peer input and institutional guidelines, may support clinician decision‐making and conversations with patients at the point of care, including in cases where patients have complex histories or personal characteristics that influence decision making.

ABSTRACT Introduction Community health workers (CHWs) help patients navigate community resources. CHW programs can improve health outcomes and reduce healthcare utilization, but identifying eligible patients is challenging. We developed an electronic health record (EHR)‐based algorithm trained to predict referrals to a CHW program. Our objective was to evaluate whether an algorithm trained on historical referrals identifies a subgroup of patients that differs from the subgroup identified through questionnaire‐based health‐related social needs (HRSN) screening alone. Methods This analysis used data from Mayo Clinic primary care patients in Southeast Minnesota. We developed a gradient boosted time‐to‐event model that incorporated demographics, diagnoses, HRSN questionnaire responses, and HRSNs derived from clinical notes using natural language processing. We compared the characteristics of algorithm‐identified patients with those identified through questionnaires and assessed the variable importance of model features. Results The algorithm had AUC: 0.87 in the training and 0.92 in the hold out set. Patients flagged by the algorithm tended to use interpreter services more commonly (12.6% vs. 1.3%), have a non‐English preferred language (14.1% vs. 2.3%), and have more instances of health literacy documented (5.36 vs. 1.29) compared to patients identified by the HRSN questionnaire. Only 47.0% of patients flagged by the algorithm had responded to the HRSN questionnaire within 2 years. Variable importance was highest for health literacy and financial strain from unstructured notes. Conclusions The algorithm is a chart‐review prioritization tool for referral to the CHW program for patients that may overcome challenges to identifying patients with HRSNs, especially when questionnaire data is incomplete.

Background: Southeast Asian Americans (SEAAs) experience a disproportionately high burden of hepatocellular carcinoma (HCC), with incidence in several subgroups (i.e., Cambodian, Laotian, and Vietnamese individuals) reaching up to nine times that of non-Hispanic Whites. HCC in SEAAs is largely driven by chronic hepatitis B (HBV), hepatitis C (HCV), metabolic dysfunction–associated steatotic liver disease (MASLD), and alcohol-associated liver disease (ALD). Despite established screening guidelines, under-detection and delayed diagnosis remain common. Objective: To summarize epidemiologic patterns, risk factors, screening challenges, and potential interventions aimed at reducing HCC disparities among SEAAs. Design and Methods: This narrative review synthesized evidence from population based epidemiologic studies, community-based interventions, health services research, and policy analyses. Attention was given to studies reporting disaggregated SEAA subgroup data. Findings derived from SEAA specific studies were distinguished from evidence drawn from broader Asian American or general cirrhosis populations, with inferential steps explicitly noted where subgroup specific data were limited. Key Findings: HCC incidence varies widely across SEAA subgroups, with elevated HBV- and HCV-related HCC in Vietnamese, Cambodian, and Laotian communities, and increasing MASLD-related HCC including among lean individuals who fall outside many surveillance frameworks. Screening and surveillance remain suboptimal, with fewer than 30% of patients with cirrhosis receiving recommended semiannual HCC surveillance and even lower uptake among SEAAs. Barriers include low HBV/HCV screening rates, limited disease awareness, language barriers, underinsurance, provider knowledge gaps, and lack of automated EHR-based reminders. Structural challenges such as poverty, transportation barriers, and limited access to specialty care further delay diagnosis. Proposed Interventions: Culturally tailored outreach programs, bilingual navigators, and community-based screening initiatives have demonstrated improved HBV/HCV testing and linkage to care. AI-enabled EHR tools may enhance identification of high-risk patients, streamline follow-up, and increase surveillance adherence. Expanded use of non-invasive fibrosis assessment and recognition of MASLD-related risk in non-obese individuals may support earlier detection. Policy priorities include mandatory Asian subgroup data disaggregation, expanded insurance coverage, and strengthened community-level healthcare infrastructure. Conclusions: SEAAs face a substantial and preventable HCC burden. A coordinated approach combining culturally tailored community engagement, improved provider support systems, and policy reforms is essential to improving early detection and reducing HCC disparities in this diverse population.

PURPOSE This national study was undertaken to determine screening mammography rates among transgender and gender diverse (TGD) individuals. METHODS TGD individuals and cisgender women who qualified to receive screening mammography based on American College of Radiology guidelines were identified in the longitudinal OptumLabs Data Warehouse. The primary end point was the percentage of patients who had high rates of screening (>75% recommended screenings). Multivariable analyses were conducted in a matched population of transgender men to cisgender women to identify and adjust for factors associated with high adherence. Individuals were matched at a 1:4 ratio (transgender men: cisgender women) on age, race/ethnicity, year of enrollment in their health care plan, and duration of follow-up. RESULTS This study focused on 10,478 TGD individuals (3,778 transgender men; 1,294 transgender women; 5,406 with gender dysphoria not meeting other TGD criteria, NMOT) and 6,218,369 cisgender women. For transgender men, transgender women, individuals with gender dysphoria NMOT, and cisgender women, high mammography rates were observed in 41.1% (95% CI, 39.5 to 42.6), 7.4% (95% CI, 6.0 to 8.8), 11.9% (95% CI, 11.0 to 12.7), and 38.3% (95% CI, 38.3 to 38.4), respectively ( P < .0001). When transgender men were compared with 15,112 matched cisgender women, an age-based differential association was observed between sex and high mammography rates (interaction P = .0003); compared with cisgender women, transgender men appeared more likely to manifest high rates of screening at age 40-49 years (odds ratios [ORs], 1.38 [95% CI, 1.28 to 1.50]; P < .0001) and 50-59 years (OR, 1.57 [95% CI, 1.35 to 1.81]; P < .0001) but not in the older age groups. CONCLUSION The relatively high rates of screening mammography in cisgender women and transgender men suggest clinicians are offering mammography to individuals who are at average or high breast cancer risk, as compared with cisgender women, and not to transgender women at low risk.

459 Background: Quality improvement (QI) addresses local quality gaps to optimize care delivery, while implementation science (IS) seeks to increase adoption of evidence-based practices (EBPs) to close research-practice gaps and improve population health. Identifying opportunities for QI-IS synergy can foster collaboration and ensure attention to rapid and sustainable improvements in evidence-based care. Our objectives were to 1) assess how QI projects submitted for silver or gold certification in our academic medical center addressed evidence-based gaps in cancer care and 2) consider how their methods align with IS approaches. Methods: The QI project database was keyword searched to identify proposals in cancer. Data including detailed description of the quality gap and methods to identify and evaluate interventions were abstracted and entered in a database organized by the DMAIC framework (Define, Measure, Analyze, Improve, and Control). Two investigators reviewed data to identify EBPs and describe evidence using a typology for IS (Type 1 etiology and burden, Type 2 intervention effectiveness, and Type 3 dissemination and implementation in context). Data in DMAIC domains were mapped to relevant IS constructs for side-by-side comparison. Results: The search identified 81 proposals submitted between May 2017 and April 2025, of which 62 targeted cancer-related care gaps (5.2% of applications). Fourteen referenced published EBPs, including American Society for Clinical Oncology or National Comprehensive Cancer Network guidelines and state or federal quality metrics on cancer screening or evaluations for diagnosis and treatment. An additional 17 addressed processes relevant to EBPs, e.g., screening documentation. QI tools like cause-and-effect diagrams identified multilevel causes, but few proposals deployed multilevel interventions (strategies in IS), and none referenced theory. Reported “lessons learned” further detailed unanticipated patient, care team, and system-level challenges. Most (n=24, 77%) of EBP-related proposals addressed Type 2 evidence (e.g., performance, quality, efficiency) but could have also been framed as Type 3 evidence on implementation context/strategies targeting implementation outcomes (e.g., clinician adoption, patient reach, implementation fidelity). Conclusions: Although focused on local improvement, about 1 in every 20 QI proposals addressed implementation of cancer EBPs. Framing QI purpose as addressing Type 3 evidence could help link local QI projects to IS theory and methods to inform strategy selection and evaluation of implementation outcomes that precede service-level outcomes. Organizations that combine rapid-cycle QI improvements and rigorous IS methodologies may more effectively close care gaps and tackle persistent healthcare challenges.

ABSTRACT Background The Geriatric Emergency Department (ED) guidelines recommend screening older adults for delirium using evidence‐based screening tools, but these tools are often underutilized. We aimed to identify barriers and facilitators to implementing an electronic health record (EHR)‐based delirium screening tool as intended. Methods This mixed methods and focused ethnographic study combined a review of structured delirium screening data with observations of real‐time delirium screenings and semi‐structured in‐person interviews at an academic ED that previously implemented the Delirium Triage Screen (DTS) and Brief Confusion Assessment Method (bCAM) tool. Content analysis of field notes from observations and interview transcripts was guided by the Exploration, Preparation, Implementation, and Sustainment implementation framework. Results From a total of 40,818 ED visits, 6196 (15.2%) had incomplete screens (i.e., without fidelity). We observed 62 encounters, of which 25 (40.3%) screenings were implemented without fidelity due to (1) not asking the required screening questions, (2) assuming patient responses, or (3) not completing the screen due to uncertainty regarding a patient's baseline mentation. We conducted 32 interviews. Identified barriers included staff prioritization of other tasks (e.g., stroke code, cleaning patients), limited knowledge of the screen's importance and utility, and language discordance. Facilitators included care partners providing baseline mental status information and nurses prefacing the screening to increase patient engagement. Conclusion The fidelity of ED delirium screening is influenced by an interplay of environmental, patient, provider, and caregiver factors. Fidelity may be improved by focusing education on the importance of screening, training screeners on how to determine whether a patient's mentation is acutely changed, providing clear action steps when a patient screens positive, and addressing how to administer the delirium screen to patients who do not speak English fluently.

AbstractElectronic patient-reported outcome measures (ePROMs) can improve care for people with cancer, but effectiveness hinges on well-supported integration in clinical settings.We evaluated clinician use of specific clinical decision support (CDS) tools in the electronic health record (EHR) designed to facilitate timely, clinically appropriate responses to ePROM scores for six symptoms commonly experienced by cancer patients.The parent pragmatic trial, which took place at Mayo Clinic (Rochester, Minnesota, United States) and its affiliated community health care system between March 2019 and January 2023, evaluated the population-level effectiveness and implementation of an ePROM surveillance and EHR-facilitated collaborative care symptom management intervention. The present evaluation used a case study approach with four data sources: (1) clinician interactions with CDS tools abstracted from the EHR; (2) clinician notes identified with an institution-specific textual search tool; (3) qualitative interviews and group discussions with care teams; and (4) administrative records reviewed to identify training and outreach to care teams.EHR metrics showed very low adoption of CDS tools including alerts and symptom-specific order sets, despite educational outreach and information technology support provided to clinical care teams. Qualitative findings revealed that CDS use was not easy to integrate into busy clinical workflows and highlighted clinician perceptions that the collaborative care intervention provided additional patient support that reduced clinicians' need to utilize CDS tools. They also highlight the importance of contextual factors, including institutional priorities and EHR changes.This pragmatic clinical trial case study found limited adoption of EHR CDS tools that had been developed to increase clinicians' awareness of and responses to ePROM data. Findings suggest the need to align clinician and organizational implementation strategies, simplify CDS tools to fit practice expectations, and identify and address contextual factors that could undercut strategies like education and peer support. This may be especially important for teams who aim to iteratively evaluate and refine CDS and implementation strategies for multicomponent interventions or introduce new strategies that are responsive to barriers while maintaining scalability.

Abstract Background Patients living with diabetes and chronic conditions may face significant burden managing their health. Many of these patients use digital medicine tools such as continuous glucose monitoring systems. Although measures exist to assess treatment burden from tasks such as managing medications and attending healthcare visits, there is no patient-reported measure that captures the burden of digital care. Therefore, the purpose of this study is to validate the Treatment Burden Questionnaire Plus Digital (TBQ + D), a patient-reported measure of treatment burden that includes using digital tools for adults with diabetes. Methods Adult patients with type 1 or type 2 diabetes mellitus completed the 25-item TBQ + D (scored 0 [none] to 10 [maximum] per item; total score range 0–250). We evaluated ease of administration, internal consistency, and tested hypotheses about the relationship between TBQ + D scores and treatment complexity, digital tool use intensity, social risk factors, and digital comfort to assess TBQ + D’s validity. Results Of 324 patients approached, 300 (93%) consented and completed the TBQ + D (mean age 57 [SD 17]; 50% female; 50% with type 2). The mean TBQ + D score was 53.7 (SD 41.6). Internal consistency was excellent (Cronbach’s α = 0.94). As hypothesized, higher TBQ + D scores were reported by patients with type 1 vs. type 2 diabetes mellitus (61.7 vs. 45.7; p = .0008), maximal/moderate vs. minimal to no digital tool use (56.5/60.7 vs. 41.3; p = .001), those on intensive insulin therapy vs. other treatments (61.4 vs. 38.0; p < .0001), and those with greater social vulnerability (p < .0106). TBQ + D scores were not significantly higher in patients with HbA1c ≥ 8% (p = .055) or less comfortable with digital technology (p = .08). Conclusions TBQ + D is a novel and valid measure of treatment burden in patients living with diabetes, inclusive of digital burden, that can play a role in fostering minimally disruptive care for patients with diabetes.