Large Integrated Health System Research Articles

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. Retrospective, cohort study. Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum. These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Despite mandated primary series, health care personnel still hesitant about COVID-19 vaccine and immunizing children

ImportanceHealthcare personnel (HCP) are important messengers for promoting vaccines, for both adults and children. Our investigation describes perceptions of fully vaccinated HCP about COVID-19 vaccine for themselves and primary series for their children. ObjectiveTo determine associations between sociodemographic, employment characteristics and perceptions of COVID-19 vaccines among HCP overall and the subset of HCP with children, who were all mandated to receive a COVID-19 vaccine, in a large US metropolitan region. DesignCross-sectional survey of fully vaccinated HCP from a large integrated health system. SettingParticipants were electronically enrolled within a multi-site NYS healthcare system from December 21, 2021, to January 21, 2022. ParticipantsOf 78,000 employees, approximately one-third accessed promotional emails; 6,537 employees started surveys and 4165 completed them. Immunocompromised HCP (self-reported) were excluded. Exposure(s) (for observational studies)We conducted a survey with measures including demographic variables, employment history, booster status, child vaccination status; vaccine recommendation, confidence, and knowledge. Main outcome(s) and measuresThe primary outcome was COVID-19 vaccine hesitancy for all dose types − primary series or booster doses − among HCP. ResultsFindings from 4,165 completed surveys indicated that almost 17.2 % of all HCP, including administrative and clinical staff, were hesitant or unsure about receiving a COVID-19 vaccine booster, despite the NYS recommendation to do so. Depending on age group, between 20 % and 40 % of HCP were hesitant about having their children vaccinated for COVID-19, regardless of clinical versus non-clinical duties. In multivariable regression analyses, lack of booster dose, unvaccinated children, females, income less than $50,000, and residence in Manhattan remained significantly associated with vaccine hesitancy. Conclusions and relevanceDespite mandated COVID-19 vaccination, a substantial proportion of HCP remained vaccine hesitant towards adult booster doses and pediatric COVID-19 vaccination. While provider recommendation has been the mainstay of combatting COVID-19 vaccine hesitancy, a gap exists between HCP—despite clinical or administrative status—and the ability to communicate the need for vaccination in a healthcare setting. While previous studies describe the HCP vaccine mandate as a positive force to overcome vaccine hesitancy, we have found that despite a mandate, there is still substantial COVID-19 vaccine hesitancy, misinformation, and reluctance to vaccinate children.

Abstract 4968: Discovery of cancer-associated factors in 293.5 million diagnosis records using statistical machine learning analysis

Abstract Cancer, a leading global public health issue, ranks as the second leading cause of death in the United States, with projections indicating 1,958,310 new cases and 609,820 deaths in 2023. Accurately quantifying specific pre-disease diagnosis factors associated with cancer, as well as predicting cancer through disease trajectories and imaging modalities, presents a complex and formidable challenge. A comprehensive temporal trajectory analysis of cancer diseases, using disease trajectories as an insight within large cohorts in the USA, has not yet been developed. Our analysis used a comprehensive EHR dataset stretching back to 1999, encompassing 293,501,891 records and involving 3,019,978 patients within Ochsner Health, a large integrated health system across the State of Louisiana. According to our investigation, those diagnosed at the ICD chapter level with diseases of the genitourinary system (genitourinary diseases, ICD-10 Chapter 14, relative risk (RR=1.65)) or endocrine nutritional and metabolic diseases (obesity, ICD-10 Chapter 4, RR=1.35) have an elevated risk of being diagnosed with cancer in the subsequent five years. The top four individual diagnosis ICD-10 codes associated with increased risk are N60 (Benign Mammary dysplasia, RR=7.80), M34 (Systemic sclerosis, RR=7.72), R43 (Disturbances of smell and taste, RR=6.93), and R92 (Abnormal and inconclusive findings on diagnostic imaging of the breast, RR=6.59). Furthermore, in our investigation of specific types of cancer risk across 17 cancer types (Breast, Skin, Prostate, Lung, Pancreatic, etc.), we observed that the most common diseases associated with breast and skin cancer involved immune mechanisms (RR=8.35 and 8.68), genitourinary issues (RR=4.13 and 2.48), obesity (RR=3.42 , 3.35), and cardiovascular conditions (ICD-10 Chapter 9, RR=1.73 and 2.19). For prostate cancer, associations were found with obesity (RR=3.50), human immunodeficiency virus (HIV, ICD-10 Chapter 1, RR=2.39). Across lung cancer, involvement with chronic viral hepatitis (ICD-10 Chapter 1, RR=4.00), cardiovascular issues (RR=3.52), genitourinary problems (RR=4.13), and obesity (RR=3.42) were indicated. Our comprehensive analysis has the potential to contribute to the early detection, diagnosis trajectories, and improved understanding of the pathological processes underlying cancer in patients worldwide. Citation Format: Md Ashad Alam, Nick Duesbery, Daniel Daniel Fort. Discovery of cancer-associated factors in 293.5 million diagnosis records using statistical machine learning analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4968.

Implementation of NCCARE360, a Digital Statewide Closed-Loop Referral Platform to Improve Health and Social Care Coordination: Evidence from the North Carolina COVID-19 Support Services Program

Introduction Efforts to improve population health by being responsive to patients’ social and economic conditions will benefit from care models and technologies that assess and address unmet social needs. In 2019, NCCARE360 launched in North Carolina as the first state­wide digital care coordination network to “close the loop” on referrals between community-based organizations (CBOs), health service providers, and social service agencies. The platform creates a shared network for sending and receiving electronic referrals and track client outcomes. Methods As a case study, we compare NCCARE360 resolution rates for community resource referrals originating from a large integrated health system primarily in Durham County from September 1, 2020, through February 28, 2021. In the first year, COVID-19 Support Ser­vices Program (COVID-SSP) funding was available to reimburse associated CBOs for providing food assistance and case management services. We compared this with the same period the following year after funds had been exhausted. We present frontline implementation experiences and highlight opportunities, challenges, and recommendations for NCCARE360 implementation. Results Multi-level considerations for individual end users, organizations adopting the platform, and policymakers are presented. Addi­tionally, we find that when COVID-SSP funding was available, more referrals were placed (3,220 cases) and referrals were more likely to be resolved (88% resolution rate) when compared to the same time frame when funds were no longer available (860 cases; 30% resolution rate). These results underscore the importance of reimbursement mechanisms and funding. Limitations The examination of referral rates is observational and may not generalize to other contexts. Conclusion The shift to value-based care is an opportunity to embrace structural solutions to health and social care fragmentation. There is also an opportunity to realize the potential of NCCARE360 and efforts like it to contain costs and improve health outcomes and equity.

An interpretable predictive deep learning platform for pediatric metabolic diseases.

Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. The cohort included 49517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16). Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.

Perspective: Leveraging Electronic Health Record Data Within Food Is Medicine Program Evaluation: Considerations and Potential Paths Forward

Government, health care systems and payers, philanthropic entities, advocacy groups, nonprofit organizations, community groups, and for-profit companies are presently making the case for Food is Medicine (FIM) nutrition programs to become reimbursable within health care services. FIM researchers are working urgently to build evidence for FIM programs’ cost-effectiveness by showing improvements in health outcomes and health care utilization. However, primary collection of this data is costly, difficult to implement, and burdensome to participants. Electronic health records (EHRs) offer a promising alternative to primary data collection because they provide already-collected information from existing clinical care. A few FIM studies have leveraged EHRs to demonstrate positive impacts on biomarkers or health care utilization, but many FIM studies run into insurmountable difficulties in their attempts to use EHRs. The authors of this commentary serve as evaluators and/or technical assistance providers with the United States Department of Agriculture’s Gus Schumacher Nutrition Incentive Program National Training, Technical Assistance, Evaluation, and Information Center. They work closely with over 100 Gus Schumacher Nutrition Incentive Program Produce Prescription FIM projects, which, as of 2023, span 34 US states and territories. In this commentary, we describe recurring challenges related to using EHRs in FIM evaluation, particularly in relation to biomarkers and health care utilization. We also outline potential opportunities and reasonable expectations for what can be learned from EHR data and describe other (non-EHR) data sources to consider for evaluation of long-term health outcomes and health care utilization. Large integrated health systems may be best positioned to use their own data to examine outcomes of interest to the broader field.

Access to telehealth and changes in diabetes care patterns during the pandemic: evidence from a large integrated health system in the Southeast USA

IntroductionTo examine the role of telehealth in diabetes care and management during versus pre-COVID-19 pandemic.Research design and methodsWe included adults (≥18 years) with prevalent diabetes as of January 1, 2018,...

Abnormal uterine bleeding diagnoses and care following COVID-19 vaccination

BackgroundThere is evidence suggesting that COVID-19 vaccination may be associated with small, transitory effects on uterine bleeding, possibly including menstrual timing, flow, and duration, in some individuals. However, changes in health care seeking, diagnosis, and workup for abnormal uterine bleeding in the COVID-19 vaccine era are less clear. ObjectivesTo assess the impact of COVID-19 vaccination on incident abnormal uterine bleeding diagnosis and diagnostic evaluation in a large integrated health system. Study DesignUsing segmented regression, we assessed whether the availability of COVID-19 vaccines was associated with changes in monthly, population-based rates of incident abnormal uterine bleeding diagnoses compared to the pre-pandemic period in health system members ages 16-44 years who were not menopausal. We also compared clinical and demographic characteristics of patients diagnosed with incident abnormal uterine bleeding between December 2020 through October 13, 2021 by vaccination status (never vaccinated, vaccinated in the 60 days prior to diagnosis, vaccinated more than 60 days prior to diagnosis) and conducted detailed chart review of patients diagnosed with abnormal uterine bleeding within 1-60 days of COVID-19 vaccination in the same time period. ResultsIn monthly populations ranging from 79,000 to 85,000 female health system members, incidence of abnormal uterine bleeding diagnosis per 100,000 person-days ranged from 8.97 to 19.19. There was no significant change in the level or trend in the incidence of abnormal uterine bleeding diagnoses between the pre-pandemic (January 2019-January 2020) and post-COVID-19 vaccine (December 2020-December 2021) periods. A comparison of clinical characteristics of 2,717 abnormal uterine bleeding cases by vaccination status suggested that abnormal bleeding among recently vaccinated patients was similar or less severe than abnormal bleeding among patients who had never been vaccinated patients or those vaccinated more than 60 days prior. There were also significant differences in age and race of patients with incident abnormal uterine bleeding diagnoses by vaccination status: never vaccinated patients were the youngest and those vaccinated more than 60 days prior were the oldest; the proportion of patients who were Black/African American was highest among never vaccinated patients, and the proportion of Asian patients was higher among vaccinated patients. Chart review of 114 confirmed post-vaccination abnormal uterine bleeding cases diagnosed from December 2020 through October 13, 2021 found that the most common symptoms reported were changes in timing, duration, and volume of bleeding. Approximately one-third of cases received no diagnostic workup; 57% had no etiology for the bleeding documented in the electronic health record. In 12% of cases, the patient mentioned or asked about a possible link between their bleeding and their recent COVID-19 vaccine. ConclusionsThe availability of COVID-19 vaccination was not associated with a change in incidence of medically attended abnormal uterine bleeding in our population of over 79,000 female patients of reproductive age. Additionally, among 2,717 patients with abnormal uterine bleeding diagnoses in the period following COVID-19 vaccine availability, receipt of the vaccine was not associated with greater bleeding severity.

Knowledge and Behavior of Primary Care Physicians Regarding Utilization of Standardized Tools in Screening and Assessment of Anxiety, Depression, and Mood Disorders at a Large Integrated Health System.

Standardized screening, objective evaluation, and management of behavioral health conditions are major challenges in primary care. The Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and Mood Disorder Questionnaire (MDQ) provide standardized screening and symptom management tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), and Mood Disorders (MD), respectively. This study explores family physicians' knowledge, attitudes, and practices regarding the utilization of GAD-7, PHQ-9, and MDQ in outpatient primary care offices. The study method was a cross-sectional electronic and paper survey utilizing a self-administered questionnaire that assessed primary care physicians' demographics, knowledge, attitudes, and practices in rural and urban outpatient clinical settings regarding GAD-7, PHQ-9, and MDQ. Statistical software SAS 9.4 was used for descriptive and Chi-Square statistics. Out of 320 total participants,145 responded (45.3%). Responding family physicians demonstrated a high level of familiarity with the GAD-7 (97.9%), PHQ-9 (97.9%), and MDQ (81.3%) assessment tools. However, the reported utilization rates were relatively lower than knowledge, with 62.7%, 73.1%, and 31.9% extremely likely or likely to utilize the GAD-7, PHQ-9, and MDQ as screening and monitoring tools, respectively. Less than a quarter of the total respondents use the objective score for the future management of GAD, with significantly more residents utilizing the score for GAD-7 compared to attendings (P < .05). There was no statistical significance difference between residents and attendings for the objective evaluation of Major Depressive Disorder (P = .26) and Mood Disorders (P = .05). Despite being knowledgeable of the utility of GAD-7, PHQ-9, and MDQ, the primary care physicians in a large integrated health system in Central Pennsylvania and Northern Maryland report inconsistent utilization in their practice. Further studies are needed to determine the underlying factors contributing to the suboptimal usage of these screening tools and ways to increase it.

Screening for Social Determinants of Health During Primary Care and Emergency Department Encounters

This cross-sectional study explores characteristics of patients who are screened and who screen positive for social determinants of health (SDOH) needs in different clinical settings within a large integrated health system.

Hospital Readmissions Among Patients Experiencing Homelessness: An Electronic Health Record Data Study.

Population-level tracking of hospital use patterns with integrated care organizations in patients experiencing homelessness has been difficult. A California law implemented in 2019 (Senate Bill 1152) aimed to ensure safety for this population after discharge from the hospital by requiring additional documentation for patients experiencing homelessness, which provides an opportunity to evaluate hospital use by this population. In a large integrated health system in California, patients experiencing homelessness were identified through documentation change requirements associated with this law and compared with a matched group from the general population. Patients experiencing homelessness had increased rates of hospital readmission after discharge compared to the general population matched on demographics and medical comorbidity in 2019 and 2020. Any address change in the prior year for patients was associated with increased odds of emergency department readmission. Patients experiencing homelessness, both enrolled in an integrated delivery system and not, were successfully identified as having higher readmission rates compared with their housed counterparts. Documentation of housing status following Senate Bill 1152 has enabled improved study of hospital use among those with housing instability. Understanding patterns of hospital use in this vulnerable group will help practitioners identify timely points of intervention for further social and health care support.

Clinical impacts of an integrated electronic health record-based smoking cessation intervention during hospitalisation

ObjectiveTo assess the effects of an electronic health record (EHR) intervention that prompts the clinician to prescribe nicotine replacement therapy (NRT) at hospital admission and discharge in a large integrated...

Fracture Risk and Association With TDF Use Among People With HIV in Large Integrated Health Systems.

Greater decline in bone health among people with HIV (PWH) has been documented but fracture risk and the impact of specific antiretroviral therapy (ART) regimens remain unclear. Retrospective analyses of electronic health record data from 3 US integrated health care systems. Fracture incidence was compared between PWH aged 40 years or older without prior fracture and demographically matched people without HIV (PWoH), stratified by age, sex, and race/ethnicity. Multivariable Cox proportional hazards models were used to estimate fracture risk associated with HIV infection. The association of tenofovir disoproxil fumarate (TDF) use and fracture risk was evaluated in a subset of PWH initiating ART. Incidence of fracture was higher in PWH [13.6/1000 person-years, 95% confidence interval (CI): 13.0 to 14.3, n = 24,308] compared with PWoH (9.5, 95% CI: 9.4 to 9.7, n = 247,313). Compared with PWoH, the adjusted hazard ratio (aHR) for fracture among PWH was 1.24 (95% CI: 1.18 to 1.31). The association between HIV infection and fracture risk increased with age, with the lowest aHR (1.17, 95% CI: 1.10 to 1.25) among those aged 40-49 years and the highest aHR (1.89, 95% CI: 1.30 to 2.76) among those aged 70 years or older. Among PWH initiating ART (n = 6504), TDF was not associated with significant increase in fracture risk compared with non-TDF regimens (aHR: 1.18, 95% CI: 0.89 to 1.58). Among people aged 40 years or older, HIV infection is associated with increased risk of fractures. Bone health screening from the age of 40 years may be beneficial for PWH. Large cohort studies with longer follow-up are needed to evaluate TDF effect and the potential benefit of early screening.

Survey of Pulmonary Embolism Risk Stratification Methods in the Emergency Department and Barriers to Electronic Health Record Documentation

Introduction Pulmonary Embolism (PE) is a major cause of morbidity and mortality worldwide. Diagnosis is essential as untreated PE has a 30% mortality rate. Currently, computer tomography pulmonary angiogram (CTPA) is the standard of care to diagnose PE. However, this modality is expensive, exposes patients to radiation and increases risk of contrast induced nephropathy. Furthermore, only 5% of CTPAs performed are positive for PE signifying that many unnecessary tests are being performed. To address this gap, Pre-Test Probability (PTP) tools have been developed to safely exclude patients with low risk of PE and limit iatrogenic harm from over testing. Despite the tools being developed over 20 years ago, they are not widely available in electronic health record (EHR) systems and even when they are built in, uptake remains low. Methods Three large integrated health systems using Epic EHR systems across five states (MA, WI. MN, AZ, FL) built electronic PTP assessments into their EHR systems to safely exclude patients with low risk of PE and limit over testing. Two sites made the PTP optional, and one site made it a forced function (providers were forced to complete PTP prior to ordering CTPA unless a D-dimer result was present in the prior 48 hours). To assess the usability of these EHR tools, the team conducted a survey with ED clinicians across the three test sites. The survey was created to identify which PE risk stratification methods were employed by ED clinicians (EHR embedded PTP tool, apps on their phone, web applications like MDcalc, calculations from memory, or if they relied on clinical gestalt), and to identify barriers to using the EHR embedded PTP. Results Site 1 had 47 respondents, Site 2 had 94 respondents, and Site 3 had 16 respondents. Two key findings emerged from the survey (Figure 1). First, clinicians used a variety of risk stratification approaches and few of them documented their risk assessments in structured fields in the medical record. Second, clinical gestalt was the most used PE risk stratification approach, and this was true across all three test sites. In addition, the health system with a PTP prompt before clinicians could proceed with CTPA orders (Site 3) did not have a higher percentage of clinicians who reported using the EHR embedded tool than sites that did not prompt clinicians to fill in the PTP. Clinicians cited a variety of different barriers to using the EHR embedded PTP tool (Table 1). Conclusion While many clinicians indicated intermittent use of PTP criteria to aid in the diagnosis of PE, risk factors and scores from phone apps, web tools, and calculations from memory are unlikely to be documented in the patient's medical record, which means that they are not accessible to all members of the ED care team and cannot be used for electronic clinical quality measure. The biggest hurdles to increasing the use of structured PTP assessments in the ED setting to safely exclude PE and limit iatrogenic harm from over testing are (1) obtaining buy-in from ED clinicians that the PTP tools provide value over clinical gestalt, at least for a subset of patients who could most benefit, and (2) identifying best practices for incorporating PTP tools into EHR systems that make the tools easier to access and use 3) developing and implementing effective education and awareness programs around the PTP tools. This survey adds to the body of literature on PTP assessments in the ED setting and can help inform further research that is needed to continue to improve the diagnosis of PE. Funding: This project is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF10778 to the American Society of Hematology.

A Comparative Outcomes Analysis: The Evolution and Growth of a Teledermatology Program to Accommodate High Patient Volumes

Background: Most teledermatology studies include reviews of programs on a limited scale, whereas there are fewer reports on how to expand a program to accommodate high patient volumes.&#x0D; Objective: To share our insights into the growth and maintenance of a teledermatology program that sees over 100,000 cases a year. &#x0D; Methods: Retrospective review of a teledermatology program between 2015 and 2022. Outcomes were compared between the initial phase from 2015-2017 and the maturation phase from 2018-2022.&#x0D; Results: In 2015, the teledermatology program was piloted in 3 hospital centers and expanded to 10 more centers by 2017. There was a total of 12,385 cases in 2015, which increased to 139,110 cases in 2022. Despite the dramatic increase in number of cases, our program adapted well: the initial phase of program development resulted in 69.2% of concerns being treated remotely, compared to 70.9% of submissions in the later years, p &lt;0.001.&#x0D; Limitations: Overall generalizability as this study was performed in a large integrated health system.&#x0D; Discussion: We maintain that the following can help scale a teledermatology program: 1. Communication with all interested parties; 2. Streamlined implementation of technological devices; 3. Training for dermatologists and primary providers; 4. Image guidelines to maintain photo quality.

Temporal shift in heart failure diagnoses among hospitalized patients within a large US integrated health system

Abstract Background Heart failure (HF) is a major cause of morbidity and mortality. Its rising prevalence and attributable cost have also made it a key focus in value-based care. Historically, most HF admissions among Medicare beneficiaries in the US have been disproportionately accounted for by a limited number of International Classification of Disease (ICD) diagnosis codes (I50.2, systolic heart failure; I50.3, diastolic heart failure; and I50.4, combined systolic and diastolic heart failure). In 2017, however, a significant shift in assigned codes was observed nationally, with hypertensive heart disease with heart failure (I11.0) and hypertensive heart disease with heart failure and chronic kidney disease (I13.0 and I13.2) accounting for a large majority of assigned ICD-10 codes (1). Purpose To better understand the pattern of assigned ICD-10 codes among patients hospitalized for HF since 2017. Methods We performed a retrospective cross-sectional analysis of patients admitted to a large integrated health system within the western US between 1/1/2018 and 10/1/2022 with a principal diagnosis of HF (defined by one of the above ICD-10 codes). More specifically, we sought to further understand the demographics, clinical characteristics, and treatment patterns of HF patients across all payers as part of a quality improvement initiative. Patient-level analyses were not performed; rather, all hospitalizations were considered independent events. Results Over the nearly 5-year period, 61,238 HF hospitalizations occurred, involving 43,234 patients, with 76% having more than 1 hospitalization. Hypertensive heart disease with HF and chronic kidney disease was the most common HF diagnosis, accounting for just over half of all admissions (Figure). Hypertensive heart disease with HF was the next most common HF diagnosis, accounting for just over one-third of admissions. Consistent with the previous observed shift in HF diagnoses among US Medicare beneficiaries in 2017, systolic HF, diastolic HF, and combined systolic and diastolic HF accounted for &amp;lt;10% of HF admissions in 2022. Conclusions Hypertensive heart disease with HF and chronic kidney disease and hypertensive heart disease with HF now constitute the predominant HF diagnoses for hospitalized patients across payers. This has important implications for quality improvement initiatives and health services research that utilize real-world data, as precise characterization of distinct phenotypes of HF may now be less reliant on coding than before.HF diagnoses (2018-2022)

Abstract 18357: Racial Disparities in Direct Oral Anticoagulant Orders in Patients With Nonvalvular Atrial Fibrillation Within A Large Integrated Health System

Introduction: Anticoagulant (OAC) use for stroke prevention in non-valvular atrial fibrillation (NVAF), specifically direct oral anticoagulants (DOAC) use, is suboptimal in racial/ethnic (RE) minorities. Hypothesis: We hypothesized differences in OAC and DOAC orders among RE groups over time in patients newly diagnosed with NVAF from 2012-2022 within a large US integrated healthcare system. Methods: We conducted a retrospective cohort study using Kaiser Permanente Southern California electronic medical record data on adults with NVAF between 2012-2022. We compared the proportion of OAC and DOAC orders within 60 days from the first NVAF diagnosis among those with a CHA 2 DS 2 -VASc score ≥2 across self-reported RE groups over time using chi-squared tests. Multivariable logistic regression was used to compare OAC and DOAC orders in RE groups compared with non-Hispanic White (NHW) adjusting for age, sex, and CHA 2 DS 2 -VASc score. Results: Of 59,340 OAC eligible patients from 2012-2022, 52% of patients received an OAC order and 33% received a DOAC order. DOAC orders across all RE groups increased over time, with DOAC orders comprising 95% of all OAC orders for new NVAF patients in 2022. The percentages of OAC and DOAC orders were highest among NHW patients in most years. In 2022, OACs were ordered at higher rates for NHW (67.1%) than Asian (67.0%) (OR=0.84, 95% CI=0.67-1.05), Hispanic (65.6%) (OR=0.63, 95% CI=0.54-0.74), or non-Hispanic Black (62.0%) (OR=0.54, 95% CI=0.43-0.68) patients. In 2022, DOACs were ordered at higher rates for NHW (65.3%) than Asian (64.2%) (OR=0.78, 95% CI=0.63-0.98), Hispanic (64.1%) (OR=0.64, 95% CI=0.55-0.75), or non-Hispanic Black (60.7%) (OR=0.55, 95% CI=0.44-0.69) patients ( Figures 1a/1b ). Conclusions: Although DOAC orders have been increasing over time across all RE groups, minority groups were still less likely to receive OAC or DOAC orders than NHW patients.

A comparative effectiveness study of carotid intervention for long-term stroke prevention in patients with severe asymptomatic stenosis from a large integrated health system: Presented in abstract form at the plenary session of the 2020 Vascular Annual Meeting of the Society for Vascular Surgery ONLINE, June 17-20, 2020.

A comparative effectiveness study of carotid intervention for long-term stroke prevention in patients with severe asymptomatic stenosis from a large integrated health system: Presented in abstract form at the plenary session of the 2020 Vascular Annual Meeting of the Society for Vascular Surgery ONLINE, June 17-20, 2020.

Role of Institutional Support for Evidence-Based Psychotherapy in Satisfaction and Burnout Among Veterans Affairs Therapists.

Burnout is widespread among psychotherapists and leads to negative mental and other health outcomes, absenteeism, and turnover. Job resources, including institutional support for evidence-based practices, can buffer against burnout and may improve satisfaction among therapists. The Veterans Health Administration (VHA) is the nation's largest integrated health system and employs 23,000 therapists, including psychologists, social workers, and counselors. The authors assessed associations between perceived institutional support for evidence-based treatment and satisfaction and burnout among VHA therapists. This analysis used data from the VHA's national 2018 Mental Health Provider Survey. Responding therapists (N=5,341) answered questions about the quality of mental health care and job satisfaction. Multilevel logistic regression models were used to predict burnout and satisfaction. The authors tested availability of evidence-based treatment and measurement-based care (MBC) as predictors; analyses were adjusted for therapist workload, demographic characteristics, and potential clustering by facility. VHA therapists had less burnout and more job satisfaction when they perceived receiving institutional support for evidence-based psychotherapy (EBP) and MBC, irrespective of whether the analyses were adjusted for workload. Less difficulty in scheduling EBP was significantly associated with decreased likelihood of burnout (OR=0.83, p<0.001) and increased satisfaction (OR=1.09, p=0.008). Less difficulty ending psychotherapy was significantly associated with decreased likelihood of burnout (OR=0.89, p=0.002) and increased satisfaction (OR=1.12, p=0.004). Support for evidence-based practices, including EBP and MBC, was closely linked to VHA therapists' satisfaction and burnout. Expanding support for therapists to provide evidence-based treatment may benefit therapists, patients, and the health care system.

Addressing Veteran Health-Related Social Needs: How Joint Commission Standards Accelerated Integration and Expansion of Tools and Services in the Veterans Health Administration

The Joint Commission recently named reduction of health care disparities and improvement of health care equity as quality and safety priorities (Leadership [LD] Standard LD.04.03.08 and National Patient Safety Goal [NPSG] Standard NPSG.16.01.01). As the largest integrated health system, the Veterans Health Administration (VHA) sought to leverage these new accreditation standards to further integrate and expand existing tools and initiatives to reduce health care disparities and address health-related social needs (HRSNs). A combination of existing data tools (for example, Primary Care Equity Dashboard), resource tools (for example, Assessing Circumstances and Offering Resources for Needs tool), and a care delivery approach (for example, Whole Health) are discussed as quality improvement opportunities to further integrate and expand how VHA addresses health care disparities and HRSNs. The authors detail the development timeline, building, limitations, and future plans for these tools and initiatives. Responding to new health care equity Joint Commission standards led to new implementation strategies and deeper partnerships across VHA that facilitated expanded dissemination, technical assistance activities, and additional resources for VHA facilities to meet new standards and improve health care equity for veterans. Health care systems may learn from VHA's experiences, which include building actionable data platforms, employing user-centered design for initiative development and iteration, designing wide-reaching dissemination strategies for tools, and recognizing the importance of providing technical assistance for stakeholders. VHA continues to expand implementation of a diverse set of tools and resources to reduce health care disparities and identify and address unmet individual veteran HRSNs more widely and effectively.