Large Health Research Articles

Teaching Procedural Skills: Integrating Educational Theory Into Practice.

Teaching procedural skills is an essential part of health professions education, yet formal training is often lacking from traditional curricula. A workshop on teaching procedural skills was developed as part of a clinician educator track at a large health professions university. Participants included medical residents and fellows (postgraduate years 2-6) from various training programs. The 90-minute, interactive training integrated Gagne's model of instructional design with evidence-based teaching practices. Workshop outcomes were evaluated with pre- and postworkshop surveys. Learner reactions (Kirkpatrick level 1) were assessed via course evaluation. Learning and behavior (Kirkpatrick level 2) were evaluated via attitudinal and knowledge-based questions. Data were collected from three cohorts of participants (2022-2024). Responses were analyzed by paired t test. Residents and fellows from 11 different disciplines participated in the workshops. The survey response rate was 30 out of 35 (86%). Course evaluations were positive for all six questions (mean scores: 4.8-5.0 on a 5-point Likert scale [1 = strongly disagree, 5 = strongly agree]). Postworkshop scores improved significantly for all five attitudinal questions, including "I can apply instructional design theory when teaching procedural skills" (pre: 2.2 vs. post: 4.3 on a 5-point scale, p < .01). The number of participants correctly answering the knowledge-based questions also increased following the workshop. A workshop focused on evidence-based teaching of procedural skills was well reviewed and improved participants' attitudes and knowledge. Strengths of the workshop include its appeal to a broad range of medical trainees, integration of educational theory, and interactive design.

Telehealth and disparities in opioid use disorder treatment: Medicaid enrollees versus privately insured individuals.

To determine how the rise of telehealth during the COVID-19 pandemic impacted Medicaid enrollees' access to opioid use disorder (OUD) treatment. Electronic health records from Northwestern Medicine, a large midwestern academic health system, from January 1, 2019 to December 31, 2021. The exposure was the expansion of telehealth services during the COVID-19 pandemic. A difference-in-differences design was used to determine the impact of telehealth on the probability of receiving any OUD care, any in-person OUD care, and any telehealth OUD care in a month. The study included Medicaid and privately insured patients older than 18 years of age, diagnosed with OUD, who had any encounter with the Northwestern Medicine system. All outpatient visits with OUD as the primary diagnosis were included in the analysis. There were 486 individuals in the sample and 17,496 person-month observations. After the onset of the COVID-19 pandemic, Medicaid enrollees are 4.5 percentage points (percentage change, 43.7%; 95% confidence interval [CI] 8.7 to 0.3 percentage points; p = 0.035) less likely to receive any OUD care in a month, relative to privately insured patients. While no statistically significant differences in the likehood of receiving in-person OUD care were detected between the groups after exposure, we did observe that Medicaid enrollees are 3.6 percentage points (percentage change 64.2%; 95% CI 6.0 to 1.1 percentage points; p = 0.004) less likely to receive any telehealth OUD care in a month relative to privately insured patients. While those with private insurance were able to maintain OUD treatment during the pandemic by supplementing in-person care with telehealth, Medicaid enrollees experienced a drop in overall OUD treatment rates due to lower telehealth use. The rise of telehealth for OUD treatment might contribute to widening care gaps for Medicaid enrollees.

Demographic characteristics associated with a penicillin allergy label during pregnancy

IntroductionPenicillins and other beta-lactam antibiotics are used in greater than one-third of pregnant women as treatment for Group B Streptococcus colonization and prophylaxis for Caesarean sections. Penicillin allergy labels have been associated with increased morbidity in the pregnant population, and penicillin allergy evaluation during pregnancy is now recognized as safe and effective. Yet, demographic characteristics associated with having a penicillin allergy label during pregnancy have not been studied. We aimed to evaluate factors associated with having a penicillin allergy label in a diverse population of pregnant patients.MethodsWe performed a retrospective observational study of pregnant patients who had an outpatient visit with Obstetrics and Gynecology and a delivery encounter from 1/1/2020 through 6/30/2022 using electronic health record data in a large health system. We used a multivariable logistic regression model to evaluate factors associated with having a penicillin allergy label.ResultsWe identified 10,969 pregnant women of whom 940 (8.6%) had a penicillin allergy label. In the multivariable analysis, having a penicillin allergy label was positively associated with age 32−34 years [odds ratio (OR) = 1.31 vs. 18−27 years, p = 0.02], 35−51 years (OR = 1.41 vs. 18−27 years, p = 0.002) and having rhinitis, asthma, or eczema (OR = 1.55 vs. none, p &amp;lt; 0.0005); and negatively associated with Black race (OR = 0.59 vs. White, p &amp;lt; 0.0005).DiscussionThis study found that Black race was associated with lower likelihood of penicillin allergy label, while older age and atopic conditions were associated with a higher likelihood. This finding may impact health outcomes and interventions related to penicillin allergy in pregnant women.

EyeMatics: An Ophthalmology Use Case Within the German Medical Informatics Initiative.

The EyeMatics project, embedded as a clinical use case in Germany's Medical Informatics Initiative, is a large digital health initiative in ophthalmology. The objective is to improve the understanding of the treatment effects of intravitreal injections, the most frequent procedure to treat eye diseases. To achieve this, valuable patient data will be meaningfully integrated and visualized from different IT systems and hospital sites. EyeMatics emphasizes a governance framework that actively involves patient representatives, strictly implements interoperability standards, and employs artificial intelligence methods to extract biomarkers from tabular and clinical data as well as raw retinal scans. In this perspective paper, we delineate the strategies for user-centered implementation and health care-based evaluation in a multisite observational technology study.

COVID-19 policies and tuberculosis services in private health sectors of India, Indonesia, and Nigeria

IntroductionThe COVID-19 pandemic created unprecedented challenges in the field of global health. Nigeria, Indonesia and India are three high tuberculosis (TB) burden countries with large private health sectors. Both TB and the private health sector faced challenges in these countries because of COVID-19. This study aimed to compare the COVID-19 control measures and policies in the provision of TB care services and gain insights from policymakers on how the pandemic affected the provision of TB services in the private healthcare sector, how each country adapted, and identify lessons learned for health system preparedness MethodsQualitative, in-depth interviews were conducted among a purposive sample of 11 national and sub-national policymakers in each country. Thematic content analysis was conducted on the data collected using an adapted WHO Health Equity Policy Framework. ResultsResults revealed three policy dimensions under costs, access, and quality. Under healthcare costs, policymakers highlighted resource allocation and diversion of TB resources to COVID response, and increased operational costs for private provider. Under healthcare access, key themes included key themes included reduced TB case detection due to fear of COVID-19, disrupted diagnostic services, and adaptations such as extended medicine supplies and virtual consultations. Under healthcare quality, themes included compromised TB diagnostic accuracy due to similar respiratory symptoms with COVID-19, and strain on laboratory infrastructure due to competing demands from both diseases. Policymakers across the three countries pointed to the need for strengthening private–public partnerships (PPP) for healthcare service delivery and continued private sector investment to facilitate the continuity of TB care within a pandemic context. ConclusionThe results of this study provide an overview of the impact of the pandemic from the perspective of private facilities and policymakers in Nigeria, Indonesia and India, which can inform future policy and ways forward in strengthening PPP for healthcare service delivery in high TB burden countries.

Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance.

Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging. The aim of this study was to develop and rigorously validate a deep learning (DL) algorithm to aid in the discrimination of CA using cine and late gadolinium enhancement CMR imaging. A DL model using a retrospective cohort of 807 patients who were referred for CMR for suspicion of infiltrative disease or hypertrophic cardiomyopathy (HCM) was developed. Confirmed definitive diagnosis was as follows: 252 patients with CA, 290 patients with HCM, and 265 with neither CA or HCM (other). This cohort was split 70/30 into training and test sets. A vision transformer (ViT) model was trained primarily to identify CA. The model was validated in an external cohort of 157 patients also referred for CMR for suspicion of infiltrative disease or HCM (51 CA, 49 HCM, 57 other). The ViT model achieved a diagnostic accuracy (84.1%) and an area under the curve of 0.954 in the internal testing data set. The ViT model further demonstrated an accuracy of 82.8% and an area under the curve of 0.957 in the external testing set. The ViT model achieved an accuracy of 90% (n=55 of 61), among studies with clinical reports with moderate/high confidence diagnosis of CA, and 61.1% (n=22 of 36) among studies with reported uncertain, missing, or incorrect diagnosis of CA in the internal cohort. DL accuracy of this cohort increased to 79.1% when studies with poor image quality, dual pathologies, or ambiguity of clinically significant CA diagnosis were removed. A ViT model using only cine and late gadolinium enhancement CMR images can achieve high accuracy in differentiating CA from other underlying etiologies of suspected cardiomyopathy, especially in cases when reported human diagnostic confidence was uncertain in both a large single state health system and in an external CA cohort.

Research Review: Why do prospective and retrospective measures of maltreatment differ? A narrative review.

Childhood maltreatment contributes to a large mental health burden worldwide. Different measures of childhood maltreatment are not equivalent and may capture meaningful differences. In particular, prospective and retrospective measures of maltreatment identify different groups of individuals and are differentially associated with psychopathology. However, the reasons behind these discrepancies have not yet been comprehensively mapped. In this review, we draw on multi-disciplinary research and present an integrated framework to explain maltreatment measurement disagreement. We identified three interrelated domains. First, methodological issues related to measurement and data collection methods. Second, the role of memory in influencing retrospective reports of maltreatment. Finally, the motivations individuals may have to disclose, withhold, or fabricate information about maltreatment. A greater understanding of maltreatment measurement disagreement may point to new ways to conceptualise and assess maltreatment. Furthermore, it may help uncover mechanisms underlying maltreatment-related psychopathology and targets for novel interventions.

The Roles of Endogenous D2R Dopamine and μ-opioid Receptors of the Brain in Alcohol use Disorder.

Alcohol use disorder (AUD) affects millions of people worldwide. It is characterized by a strong physiological and psychological craving to consume large amounts of alcohol despite adverse consequences. Alcohol use disorder carries a large health and economic burden on society. Despite its prevalence, AUD is still severely undertreated. The precise molecular mechanisms of how alcohol addiction forms are yet unknown. However, previous studies on animal models show that along with the μ-opioid receptors, the D2R dopamine receptors may also be involved in alcohol craving and reward pathways. Currently, there is a limited number of treatment strategies for alcohol use disorder, which include several medications and therapy. By understanding the limitations of current treatment options and exploring new potential targets, it could be possible to find more effective ways of treating AUD in the future.

Is the PHQ-2 a Good Measure to Inform Providers About Patient Well-Being and Functioning? Data From the Veterans Health and Life Survey.

Health care systems are increasingly focused on assessing patient well-being and functioning. The objective of the current analysis was to evaluate a pragmatic question: to what extent and in what way can the PHQ-2, a routinely collected screening measure, be used to help clinicians and a learning health system understand the well-being and functioning of its beneficiaries? The current analysis focused on 2872 Veterans who completed a large-scale longitudinal survey about health and wellness for whom we were able to link survey responses to PHQ-2 scores recorded in their electronic health records (EHR). Regression analyses examined the cross-sectional and longitudinal associations between PHQ-2 scores recorded in the EHR and measures of well-being (life satisfaction, purpose in life, and social health) and functioning (pain severity and interference, physical and mental health, and perceived stress). Veterans were aged 65 years on average (11% women). PHQ-2 scores were correlated cross-sectionally with all well-being and functioning measures; however, there was minimal variance accounted for. Changes in the PHQ-2 over time were associated with 3 measures: purpose in life (b = -0.19; 95% CI: -0.34, -0.04), mental health functioning (b = -0.29, 95% CI: -0.54, -0.04), and perceived stress (b = 0.13; 95% CI: 0.02, 0.24). The PHQ-2 was minimally associated with patient well-being and functioning, with more work needed on how the PHQ-2 may be used in large health care settings within the context of VA Whole Health. Assessment of well-being is critical as VA's Whole Health transformation continues, and identifying strategies for well-being measurement is an integral next step.

Developing a prediction model for cognitive impairment in older adults following critical illness

BackgroundNew or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-discharge is recommended for those at increased risk. There is a need for prediction models of post-ICU cognitive impairment to guide delivery of screening and support resources to those in greatest need. We sought to develop and internally validate a machine learning model for new cognitive impairment or dementia in older adults after critical illness using electronic health record (EHR) data.MethodsOur cohort included patients > 60 years of age admitted to a large academic health system ICU in North Carolina between 2015 and 2021. Patients were included in the cohort if they were admitted to the ICU for ≥ 48 h with ≥ 2 ambulatory visits prior to hospitalization and at least one visit in the post-discharge year. We used a machine learning model, oblique random survival forests (ORSF), to examine the multivariable association of 54 structured data elements available by 3 months after discharge with incident diagnoses of cognitive impairment or dementia over 1-year.ResultsIn this cohort of 8,299 adults, 22% died and 4.9% were diagnosed with dementia or cognitive impairment within one year. The ORSF model showed reasonable discrimination (c-statistic = 0.83) and stability with little difference in the model’s c-statistic across time.ConclusionMachine learning using readily available EHR data can predict new cognitive impairment or dementia at 1-year post-ICU discharge in older adults with acceptable accuracy. Further studies are needed to understand how this tool may impact screening for cognitive impairment in the post-discharge period.

Exploring the Knowledge, Attitudes, and Perceptions of Hospital Staff and Patients on Environmental Sustainability in the Operating Room: Quality Improvement Survey Study.

In Canada, the health care system has been estimated to generate 33 million metric tons of greenhouse gas emissions annually. Health care systems, specifically operating rooms (ORs), are significant contributors to greenhouse gas emissions, using 3 to 6 times more energy than the hospital's average unit. This quality improvement study aimed to investigate the knowledge, attitudes, and perceptions of staff members and patients on sustainability in the OR, as well as identify opportunities for initiatives and barriers to implementation. A total of 2 surveys were developed, consisting of 27 questions for staff members and 22 questions for patients and caregivers. Topics included demographics, knowledge and attitudes regarding environmental sustainability, opportunities for initiatives, and perceived barriers. Multiple-choice, Likert-scale, and open-ended questions were used. A total of 174 staff members and 37 patients participated. The majority (152/174, 88%) of staff members had received no and minimal training on sustainability, while 93% (162/174) cited practicing sustainability at work as moderately to extremely important. Among patients and caregivers, 54% (20/37) often or always noticed when a hospital is being eco-friendly. Both staff members and patients agreed that improving sustainability would boost satisfaction (125/174, 71.8% and 22/37, 59.4%, respectively) and hospital reputation (22/37, 59.4% and 25/37, 69.5%, respectively). The staff members' highest-rated environmental initiatives included transitioning to reusables, education, and improved energy consumption, while patients prioritized increased nature, improved food sourcing, and education. Perceived barriers to these initiatives included cost, lack of education, and lack of incentives. Staff members and patients and caregivers in a large academic health care center acknowledge the significance of environmental sustainability in the OR. While they do not perceive a direct impact on patient care, they anticipate positive effects on satisfaction and hospital reputation. Aligning initiatives with staff members and patient and caregiver preferences can help drive meaningful change within the OR and beyond.

Digital Coaching to Address Health, Wellness, and Burnout Among Healthcare Workers: Pilot Study Results.

Healthcare worker (HCW) well-being is essential for safe, high-quality patient care, but clinicians and front-line staff continue to experience alarming rates of burnout. This pilot study evaluated a novel 6-week program of remote wellness coaching supported by daily digital messaging to reduce burnout and increase well-being among HCWs. In spring 2023, staff from a large community health center in California were invited to participate in this single-group pretest-posttest study in an academic-practice partnership. Thirty-four participants who were mostly female (91%), Latina (77%), 36 years old on average (range = 20-61), and represented all major job categories provided informed consent and completed the baseline survey. Of these, 17 completed 6 weekly 20-minute coaching sessions; received daily messages about stress management, self-care, workplace well-being, social connections, and lifestyle and health behaviors, and completed follow-up data collection. The Wilcoxon matched pair signed-rank tests assessed changes from baseline to 2-months follow-up. Self-reported burnout decreased from 59% at baseline to 35% at follow-up. Work exhaustion (p < .05), stress (p < .05) and sleep problems (p < .01) reduced significantly, and wellness practices (p < .05), moderate physical activity (p < .01), and healthy daily eating (p < .05) improved. Our pilot study suggests that a brief digital wellness program may address burnout and increase health and well-being among front-line staff and clinicians. Healthcare settings should consider this type of program for their workers, especially given the added burden of COVID-19 on the healthcare system.

Health system-wide access to generative artificial intelligence: the New York University Langone Health experience.

The study aimed to assess the usage and impact of a private and secure instance of a generative artificial intelligence (GenAI) application in a large academic health center. The goal was to understand how employees interact with this technology and the influence on their perception of skill and work performance. New York University Langone Health (NYULH) established a secure, private, and managed Azure OpenAI service (GenAI Studio) and granted widespread access to employees. Usage was monitored and users were surveyed about their experiences. Over 6 months, over 1007 individuals applied for access, with high usage among research and clinical departments. Users felt prepared to use the GenAI studio, found it easy to use, and would recommend it to a colleague. Users employed the GenAI studio for diverse tasks such as writing, editing, summarizing, data analysis, and idea generation. Challenges included difficulties in educating the workforce in constructing effective prompts and token and API limitations. The study demonstrated high interest in and extensive use of GenAI in a healthcare setting, with users employing the technology for diverse tasks. While users identified several challenges, they also recognized the potential of GenAI and indicated a need for more instruction and guidance on effective usage. The private GenAI studio provided a useful tool for employees to augment their skills and apply GenAI to their daily tasks. The study underscored the importance of workforce education when implementing system-wide GenAI and provided insights into its strengths and weaknesses.

Approach to Estimating Adherence to Heart Failure Medications Using Linked Electronic Health Record and Pharmacy Data.

Medication non-adherence, which is common in chronic diseases such as heart failure, is often estimated using proportion of days covered (PDC). PDC is typically calculated using medication fill information from pharmacy or insurance claims data, which lack information on when medications are prescribed. Many electronic health records (EHRs) have prescription and pharmacy fill data available, enabling enhanced PDC assessment that can be utilized in routine clinical care. To describe our approach to calculating PDC using linked EHR-pharmacy data and to compare to PDC calculated using pharmacy-only data for patients with heart failure. We performed a retrospective cohort study of adult patients with heart failure who were prescribed guideline-directed medical therapy (GDMT) and seen in a large health system. Using linked EHR-pharmacy data, we estimated medication adherence by PDC as the percent of days in which a patient possessed GDMT based on medication pharmacy fills over the number of days the prescription order was active. We also calculated PDC using pharmacy-only data, calculated as medications possessed over days with continued medication fills. We compared these two approaches for days observed and PDC using a paired t-test. Among 33,212 patients with heart failure who were prescribed GDMT, 2226 (6.7%) never filled their medications, making them unavailable in the assessment of PDC using pharmacy-only data (n = 30,995). Linked EHR-pharmacy data had slightly longer days observed for PDC assessment (164.7 vs. 163.4days; p < 0.001) and lower PDC (78.5 vs. 90.6, p < 0.001) as compared to assessment using pharmacy-only data. Linked EHR-pharmacy data can be used to identify patients who never fill their prescriptions. Estimating adherence using linked EHR-pharmacy data resulted in a lower mean PDC as compared to estimates using pharmacy-only data.

A qualitative case study of health system barriers and facilitators to living donor kidney transplantation in Canada's most populous province

AbstractPatients with kidney failure need dialysis or a kidney transplant to survive. Living donor kidney transplantation (LDKT) is the best therapeutic option, yet global rates of LDKT have minimally increased, and there are disparities in access. The need for a systems approach to improvement has been highlighted. We aimed to understand what elements of a relatively large health system interact to deliver LDKT and act as facilitators or barriers. This was an exploratory case study of Ontario, the most populous province in Canada that conducts 600–700 kidney transplantations annually, of which ~30% are LDKT. Data collection entailed interviews with multiple stakeholders (n = 37), and document review (n = 29) served as a means of triangulating the data. Data were analyzed using inductive thematic analysis. A multicomponent intervention to facilitate kidney transplantation was simultaneously being conducted which allowed us to capture its impact (EnAKT‐LKD). Eight themes were identified, that were separated into facilitators and barriers. Centralized leadership, directed resource deployment, dynamic communities of practice, and informal collaborations between various elements of the health system were found to facilitate LDKT. Barriers were inadequate donor and patient resources to support equitable access, lengthy and poorly coordinated workups for donors and recipients, and issues of jurisdictional control and competition for resources. The EnAKT‐LKD initiative was described as having boosted resource deployment and collaborative capacity and improved strategic alliances by establishing communities of practice. This case study has identified how individual elements in a health system interact to facilitate and impede the delivery of a therapy to patients.

Evaluation of virtual training delivery for health information systems implementation in Canada: A qualitative study.

As health information systems (HIS) become a critical part of patient care, it is crucial to build an effective education strategy that facilitates the adoption and sustained use of these systems. The COVID-19 pandemic (2019-2023) has contributed to the rapid shift in virtual education and training for healthcare staff. We sought to evaluate the efficacy and long-term sustainability of virtual training for using a HIS by examining opportunities and challenges. An exploratory, multimethods study was conducted with staff who had taken part in a virtual HIS training program as part of the clinical transformation journey at a large academic health science center in Canada. The study was guided by the Accelerating the Learning Cycle framework. Data were collected through pre- and post-training surveys, as well as semi-structured interviews. An iterative, inductive, constant comparative analysis approach, outlined by Braun and Clarke, was taken to thematically analyse the data. Of the 33 participants in this study, 13 were educational champions, and 20 were end-users. The pre- and post-training surveys yielded a total of 1479 responses in both groups. Three prominent themes emerged from this study: (1) fostering dynamic facilitation techniques to cultivate an inclusive culture and adapt to diverse learning needs; (2) integrating practical learning activities that contribute to knowledge retention; and (3) ensuring training resources are accessible and consistent for an optimal training experience. As HIS continue to be part of the transformation of the healthcare ecosystem, education is vital in preparing healthcare providers to perform their clinical tasks and effectively use these technologies. Findings from this study can be used to inform the development of virtual training that is inclusive and addresses the needs of care providers.

Artificial intelligence integration in healthcare: perspectives and trends in a survey of U.S. health system leaders

BackgroundThe healthcare sector is rapidly integrating artificial intelligence-derived predictive models (AIDPM) to enhance clinical decision support, operational efficiency, and patient experiences. However, research on management strategies for AIDPM acquisition, deployment, and governance remains limited. This study examines changes in AIDPM integration and governance since 2021, with a particular focus on large language models and health equity considerations.ResultsOur survey of health system leaders achieved a 49% response rate (32/65). While 84% of institutions reported using AIDPM in clinical practice, only 53% had established dedicated teams for these models. Compared to 2021, there was a significant increase in representation from experts in clinical informatics, operations, and quality improvement on AIDPM teams. Most organizations (41%) primarily purchased AIDPM from external vendors. Support for integrating large language models into healthcare practices was unanimous among respondents. The principal obstacles to AIDPM adoption included regulatory concerns, data security, workflow integration, and clinician acceptance. A large majority (72%) of respondents supported government regulation of AI in healthcare. While 76% of organizations reported having a team member dedicated to health equity, ethicists and diversity leaders were underrepresented on AIDPM teams (18%). Organizations reported various efforts to promote health equity, but involvement of frontline clinicians in AIDPM development and its impact on health equity was significantly less common.ConclusionsClinical adoption of AIDPM faces challenges due to the absence of established best practices. Health system leaders strongly support federal regulations for AI in healthcare. These regulations could provide quality and safety standards. The study highlights the need for developing evaluation guidelines, especially for large language models. It also reveals a lack of uniform involvement of frontline clinicians and equity experts in AIDPM governance. Their involvement could increase adoption and trust of these new AI tools. Future research should assess healthcare systems' adherence to emerging regulations and best practice frameworks. This research should emphasize patient safety and health equity. These findings underscore the urgent need for a comprehensive roadmap. This roadmap would guide the responsible implementation of AIDPM in healthcare settings.

Evaluating the efficacy of a mindfulness mobile app for stress reduction in nurses

Objective: All Nurses experience work stress that can take their focus away from patient care. Healthcare organizations strive to identify successful, cost-effective stress reduction programs. Mindfulness Based Stress Reduction (MBSR) training is a validated approach to stress reduction, usually in a class format. However, financial and time constraints make it inaccessible to most practicing nurses. Alternatively, mobile mindfulness apps offer an approach to mindfulness that can reach large populations, are available 24/7, anonymous, and cost effective.Methods: This prospective, study evaluated the efficacy of a mindfulness mobile app for stress reduction in nurses utilizing Whil, a Mobile App that offers mindfulness training specifically geared towards health professionals. Eight hundred and fifty-two nurses were recruited from twelve sites (71 per site) within a large Health Care System in the Northeast United States.Results: Two scales were used to test results. Nurses Stress Scale (NSS) results indicated that nurses experienced a reduction in stress level with use and time spent in the app. Nurses in the 31-40 age range and nurses on 12-hour shifts experienced greater stress levels.Conclusions: Significant differences were seen in the Subscales Conflict with Physicians, Conflict with other Nurses, and Lack of Support. There was no change in the Mindfulness Attention Awareness Scale (MAAS) over time. Spearman’s correlation showed a significant and negative correlation between NSS and MAAS scores. The Whil Mobile App is effective for stress reduction in practicing nurses on all shifts and is cost effective.

Abstract 4118668: Understanding Medication Adherence to Oral Anticoagulants in Atrial Fibrillation Management: Patient and Provider Perspectives in a Mixed Methods Study

Background: Atrial fibrillation (AF) increases risk of stroke. National guidelines recommend oral anticoagulant (OAC) use to reduce this risk. Medication adherence is pivotal for effective AF management, necessitating an understanding of adherence barriers. Aims: This mixed-methods study aimed to identify patient and provider factors influencing OAC adherence. Methods: Patients with nonvalvular AF and OAC prescriptions were sampled from a large community health system serving seven states. A stratified random sample of 2,000 patients was surveyed by mail, achieving a 13.3% response rate (266 patients). Twenty-eight semi-structured interviews were conducted with a purposive sample of patient survey respondents. Eligible providers were surveyed by email, resulting in a 3% response rate (78 providers). Survey data were analyzed descriptively and using multivariable regression models. Interview data were analyzed thematically. Results: Of 213 patients with an adherence score available, 31.5% reported non-adherence. Nonadherent patients were more likely to report poor healthcare experiences, including discrimination in a healthcare setting and poorer mental/emotional health. Receiving information from providers was significantly associated with adherence, especially among younger patients and those with fewer chronic conditions. High self-efficacy score was associated with adherence, particularly among older and rural patients. Interview analyses showed provider communication about AF medications, perceptions of AF as a serious illness, and a sense that medication was helping manage AF were all important for medication adherence. In contrast, most providers (87%) perceived high non-adherence among AF patients, citing forgetfulness, cost, and side effects as the main reasons. Providers’ responses indicate a need for improving communication and support for addressing adherence barriers. Conclusion: Most patients reported adherence to their medications, with adherence being associated with positive healthcare experiences and better mental/emotional health. Providers perceived relatively low adherence among their patients but aligned with patients on the need for improved communication and shared decision making. Tools that encourage discussion with patients and open communication between providers and patients are pivotal for optimizing patient care and enhancing medication adherence and treatment outcomes.

Abstract 4119619: Congruency of Stated Aortic Stenosis Severity and Reported Doppler Exam Values in a Large Health Care System Based on ACC/ASE Guidelines

Background: Peak aortic valve Doppler velocity, mean aortic valve gradient, and Doppler estimated aortic valve area are key measures recommended for assessing aortic stenosis severity. Discordance often occurs among these measures, posing diagnostic challenges. Failure to accurately diagnose severe aortic stenosis is critical. This study focuses on the discordance between reported aortic valve area (AVA) and stenosis severity, comparing guidelines from the ASE and ACC seen in Table 1. Goals: Assess the incidence of discordance between reported AVA and severity of aortic stenosis and compare how the differing guideline recommendations of the ASE versus ACC for defining severe AS by AVA would affect reporting severity in a large clinical practice. Methods: A query of all transthoracic echocardiograms obtained in patients 18 years of age and above was performed in a large multi-hospital healthcare system between Jan 1 and Jun 30 of 2023. This group was then filtered to include only exams in which the reported aortic valve area was 1 cm2 or less, excluding all cases with prosthetic aortic valves. This group was then further filtered to contain only those patients in whom the physician did NOT report severe aortic stenosis. Results: Between Jan and June 2023, 16,675 subjects ages 18-95 (avg 74) underwent transthoracic echocardiograms in our healthcare system, with 190 (1.1%) reported as having an AVA ≤ 1 cm2. Of those exams, 63 (33% of those with AVA ≤ 1) were not reported as having severe aortic stenosis. Of those 63 exams, 35 (56%) had a reported SVI &lt; 35 ml/m2 compatible with a low flow state. However, only 1 of those 35 cases mentioned flow-status in labeling the stenosis severity in the report. Based on the ASE Doppler AVA definition of severe AS (&lt; 1.0 cm2 (≤ 0.94 )) vs ACC (≤ 1 cm2 (≤1.04), the AV Doppler and SVI data contained in the reports should have led to the grading of the aortic valve as being ”Severe” or “Moderate” aortic stenosis as described in Table 2. Conclusion: Discordance in labeling severe aortic stenosis relates to differing AVA criteria. Cases are often termed "moderate to severe" under ACC criteria. The majority (56%) of cases failing to describe severe AS despite meeting AVA criteria were in low flow states, suggesting this phenomenon is not well understood in clinical practice. Various factors contribute to these findings, warranting further investigation.