Providing Cardiology Care in Rural Areas Through Visiting Consultant Clinics

Pediatric subspecialty outreach clinics: reach and impact on access to care

BACKGROUND

Recent research highlighting a shortage of pediatric subspecialists in the United States has shown wide variations in the distance from children to the nearest subspecialists but has not accounted for subspecialty outreach clinics, in which specialists may improve access in rural areas by periodically staffing clinics there. This study aimed to determine the impact of pediatric subspecialty outreach clinics on the driving times to the nearest pediatric subspecialists for children in Maine.

METHODS

This cross-sectional study utilized administrative data on the schedule and location of pediatric subspecialty clinics in Maine in 2022 to estimate the driving time from each ZIP-code tabulation area to the nearest subspecialist, with and without the inclusion of outreach clinics. Using 2020 census data, we calculated the median and interquartile ranges of driving times for the state's overall child population, as well as for children living in urban and rural areas.

RESULTS

Of 207,409 individuals under 20 years old in Maine, 68% were located closer to an outreach location than to a clinical hub. Across the seven subspecialties offering outreach clinics, outreach clinics decreased median driving times to the nearest pediatric subspecialist by 5 to 26 minutes among all children, and by 16 to 46 minutes among rural children.

CONCLUSIONS

Pediatric subspecialty outreach clinics can substantially reduce the driving time to the nearest pediatric subspecialist , especially for children living in rural areas. The use of outreach clinics should be accounted for in research describing the geographic access or barriers to care. Expanding the number of outreach clinics should be considered by policymakers hoping to improve access.

Telemedicine in Cardiology: Enhancing Access to Care and Improving Patient Outcomes

Telemedicine has gained significant recognition, particularly since the COVID-19 pandemic. However, its roots date back to its significant role during major epidemic outbreaks such as severe acute respiratory syndrome (SARS), H1N1 and H7N9 influenza, and Middle East respiratory syndrome (MERS), where alternate means of accessing healthcare were adopted to combat the outbreak while limiting the spread of the virus. In Sub-Saharan Africa, telemedicine has supported healthcare delivery, patient and professional health education, disease prevention, and surveillance, starting with its first adoption in Ethiopia in 1980. In the United States, telemedicine has significantly impacted cardiology, particularly at-home monitoring programs, which have proven highly effective for patients with abnormal heart rhythms. Devices such as Holter monitors, blood pressure monitors, and implantable cardioverter-defibrillators have reduced mortality rates and hospital readmissions while improving healthcare efficiency by saving healthcare costs. However, the COVID-19 pandemic accelerated the adoption of telemedicine, as evidenced by a dramatic increase in telemedicine visits at institutions like New York University (NYU) Langone Health during and post-COVID-19 pandemic. In addition, telemedicine has also facilitated cardiac rehabilitation and improved access to specialized cardiology care in rural and underserved areas, reducing disparities in cardiovascular health outcomes. As technology advances, telemedicine is poised to play an increasingly significant role in cardiology and healthcare at large, enhancing patient management, healthcare efficiency, and cost reduction. This review underscores the significance of telemedicine in cardiology, its challenges, and future directions.

Analysis of the Weekend Effect on Mortality, Diagnostic Coronary Angiography, and Percutaneous Coronary Intervention in Acute Myocardial Infarction Across Rural US Hospitals

BACKGROUND

Rural hospitals face several unique challenges in delivering healthcare to an underserved population. Achieving time-sensitive goals in a resource-scarce facility is often a difficult task without the right team at hand. Resources are further depleted on the weekends, exposing understaffed hospitals to poorer outcomes. Acute myocardial infarction (AMI) mortality depends on timely diagnosis and intervention. It is unknown to what extent resource shortages impact rural hospitals during weekends and how they affect AMI mortality.

METHODS

This cross-sectional study was performed on patients admitted on weekends with AMI using the National Inpatient Sample (NIS) 2019. Patients with type II non-ST-elevation myocardial infarction (NSTEMI) and missing information were excluded. The rates and timing of in-hospital diagnostic coronary angiograms, PCIs (percutaneous coronary interventions), and in-hospital mortality were studied. Regression models were used for data analyses.

RESULTS

A total of 161,625 patients met the inclusion criteria (58,690 females (36%), 114,830 Caucasians (71%), 17,910 African American (11%), 13,920 Hispanic (8.6%); mean (SD) age, 66.5 (0.5) years), including 47,665 (29.5%) ST-elevation myocardial infarction (STEMI) and 113,960 (70.5%) NSTEMI. Patients admitted to rural hospitals were less likely to undergo diagnostic coronary angiogram (adjusted odds ratio (aOR), 0.69; CI, 0.57-0.83; p<0.001) and PCI (aOR, 0.83; CI, 0.72-0.96; p 0.012). Rural hospitals had lesser odds of early diagnostic angiograms (aOR, 0.79; CI, 0.67-0.95; p<0.05) and PCI (aOR, 0.78; CI, 0.66-0.92; p<0.05) within 24 hours. The mortality difference between rural and urban hospitals was not significant (aOR, 1.08; CI, 0.85-1.4; p 0.52).

CONCLUSIONS

Diagnostic coronary angiograms and PCI are performed at a lesser rate in rural hospitals during weekends. This trend did not affect rural AMI mortality.

Configuration and Delivery of Primary Care in Rural and Urban Settings

BACKGROUND

There are concerns about the capacity of rural primary care due to potential workforce shortages and patients with disproportionately more clinical and socioeconomic risks. Little research examines the configuration and delivery of primary care along the spectrum of rurality.

OBJECTIVE

Compare structure, capabilities, and payment reform participation of isolated, small town, micropolitan, and metropolitan physician practices, and the characteristics and utilization of their Medicare beneficiaries.

DESIGN

Observational study of practices defined using IQVIA OneKey, 2017 Medicare claims, and, for a subset, the National Survey of Healthcare Organizations and Systems (response rate=47%).

PARTICIPANTS

A total of 27,716,967 beneficiaries with qualifying visits who were assigned to practices.

MAIN MEASURES

We characterized practices' structure, capabilities, and payment reform participation and measured beneficiary utilization by rurality.

KEY RESULTS

Rural practices were smaller, more primary care dominant, and system-owned, and had more beneficiaries per practice. Beneficiaries in rural practices were more likely to be from high-poverty areas and disabled. There were few differences in patterns of outpatient utilization and practices' care delivery capabilities. Isolated and micropolitan practices reported less engagement in quality-focused payment programs than metropolitan practices. Beneficiaries cared for in more rural settings received fewer recommended mammograms and had higher overall and condition-specific readmissions. Fewer beneficiaries with diabetes in rural practices had an eye exam. Most isolated rural beneficiaries traveled to more urban communities for care.

CONCLUSIONS

While most isolated Medicare beneficiaries traveled to more urban practices for outpatient care, those receiving care in rural practices had similar outpatient and inpatient utilization to urban counterparts except for readmissions and quality metrics that rely on services outside of primary care. Rural practices reported similar care capabilities to urban practices, suggesting that despite differences in workforce and demographics, rural patterns of primary care delivery are comparable to urban.

Pre-left Ventricular Assist Device Cognition: A Comparison of Rural and Urban Implant Recipients

Mild cognitive impairment (MCI) is common in patients before left ventricular assist device (LVAD) placement. The consequences of these deficits may differentially impact groups of patients with limited access to postimplantation resources, such as those in rural areas. However, to date, no studies have examined preimplantation cognition in rural and urban patients. Therefore, the purpose of this study was to compare cognition in rural and urban patients before LVAD implantation. This observational cohort study is a secondary analysis of 265 patients undergoing LVAD implantation between July 2004 and June 2019. Preimplantation cognitive function was assessed. Rural-Urban Commuting Area Codes designated rural and urban. Independent-samples Mann-Whitney U tests were used to compare rural and urban cohorts. Subjects were 75.8% (201/265) male, 75.4% (200/265) urban, and 56.6% (±13.2) years old. Rural subjects scored significantly lower on the Mini Mental Status Exam (MMSE) (27 ± 2.20) and Wide Range Achievement Test (WRAT) (32 ± 22.9) compared with urban counterparts (MMSE, 27.6 ± 2.7) (p = 0.009); WRAT (44.4 ± 26.2) (p = 0.02) pre-LVAD. These findings suggest rural patients may have greater MCI before LVAD implantation. Rural LVAD recipients with MCI may require additional intensive education and tailored resource identification before discharge.

An Initiative to Improve 30-Day Readmission Rates Using a Transitions-of-Care Clinic Among a Mixed Urban and Rural Veteran Population

BACKGROUND/OBJECTIVE

Hospital readmissions in the United States, especially in patients at high-risk, cost more than $17 billion annually. Although care transitions is an important area of research, data are limited regarding its efficacy, especially among rural patients. In this study, we describe a novel transitions-of-care clinic (TOCC) to reduce 30-day readmissions in a Veterans Health Administration setting that serves a high proportion of rural veterans.

METHODS

In this quality improvement initiative we conducted a pre-post study evaluating clinical outcomes in adult patients at high risk for 30-day readmission (Care Assessment Needs score > 85) discharged from the Iowa City Veterans Affairs (ICVA) Health Care System from 2017 to 2020. The ICVA serves 184,000 veterans across 50 counties in eastern Iowa, western Illinois, and northern Missouri, with more than 60% of these patients residing in rural areas. We implemented a multidisciplinary TOCC to provide in-person or virtual follow-up to high-risk veterans after hospital discharge. The main purpose of this study was to assess how TOCC follow-up impacted the monthly 30-day patient readmission rate.

RESULTS

The TOCC resulted in a 19.2% relative reduction in 30-day readmission rates in the 12-month postimplementation period compared to the preimplementation period (9.2% vs 11.4%, P = .04). Virtual visits were more popular than in-person visits among both urban and rural veterans. There was no difference in outcomes between these two follow-up options, and both groups had reduced readmission rates compared to non-TOCC follow-up.

CONCLUSIONS

A multidisciplinary TOCC within the ICVA featuring both virtual and in-person visits reduced the 30-day readmission rate. This reduction was particularly notable among patients with congestive heart failure.

The application of deep learning in electrocardiogram: Where we came from and where we should go?

Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versus time traces over a period. Deep learning technology, a robust artificial intelligence algorithm, can imitate the data processing patterns of the human brain, and it has experienced remarkable success in disease screening, diagnosis, and prediction. Compared with traditional machine learning, deep learning algorithms possess more powerful learning capabilities and can automatically extract features without extensive data pre-processing or hand-crafted feature extraction, which makes it a suitable tool to analyze complex structures of high-dimensional data. With the advances in computing power and digitized data availability, deep learning provides us an opportunity to improve ECG data interpretation with higher efficacy and accuracy and, more importantly, expand the original functions of ECG. The application of deep learning has led us to stand at the edge of ECG innovation and will potentially change the current clinical monitoring and management strategies. In this review, we introduce deep learning technology and summarize its advantages compared with traditional machine learning algorithms. Moreover, we provide an overview on the current application of deep learning in ECGs, with a focus on arrhythmia (especially atrial fibrillation during normal sinus rhythm), cardiac dysfunction, electrolyte imbalance, and sleep apnea. Last but not least, we discuss the current challenges and prospect directions for the following studies.

Multiple unplanned readmissions after discharge for an admission with percutaneous coronary intervention

OBJECTIVE

This study aims to describe temporal trends, characteristics, and clinical outcomes of patients with more than one unplanned readmission within 30 and 180 days after admission with percutaneous coronary intervention (PCI).

BACKGROUND

There is limited understanding of multiple readmissions after PCI.

METHODS

Patients undergoing PCI between 2010 and 2014 in the U.S. Nationwide Readmission Database were evaluated for unplanned readmissions at 30 and 180 days after discharge. Trends in multiple readmissions, characteristics of patients, and causes of first readmissions are described.

RESULTS

A total of 2,324,194 patients were included in the analysis of 30-day unplanned readmissions and 1,327,799 patients in the analysis of 180-day unplanned readmission. The proportions of patients with a single readmission and multiple readmissions within 30 days were 8.5 and 1.0% and at 180 days were 15.4 and 9.1%, respectively. Common reasons for first readmission among patients with multiple readmissions were coronary artery disease, including angina, heart failure, and acute myocardial infarction. Factors associated with multiple readmissions were discharge against medical advice, discharge to care home, renal failure, and liver failure. The total cost of multiple readmissions is significant, with an increase from ~$20,000 for no readmission to over $60,000 at 30-day follow up and $86,000 at 180-day follow up.

CONCLUSIONS

Multiple readmissions are rare within 30 days after PCI but increase to nearly 1 in 10 patients at 180 days, and 20-25% of patients who have multiple readmissions are readmitted for the same cause as for the first and second readmissions.

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.

Chest Pain on the Ward: A Simulation Scenario for Rural Family and Emergency Medicine Trainees

Chest pain is a frequently encountered emergency room presentation, of which about 15% of cases are due to acute coronary syndromes. Cardiogenic shock is a relatively uncommon complication with associated high morbidity and mortality. Emergency medicine practitioners frequently encounter critically ill patients that require quick, definitive treatment to optimize patient outcomes. These high acuity presentations often are of relatively low occurrence which makes training residents and learners challenging. Simulation-based medical education has been shown to enhance patient outcomes by teaching these high acuity low occurrence (HALO) presentations in a safe environment. Herein we describe a simulation scenario of a patient with cardiogenic shock secondary to acute coronary syndrome. It consists of a step-wise, detailed summary of the case, along with modifiers to adjust the case for repeated use, learning objectives, and a suggested evaluation.

Diversity, Inclusion, and Equity: Evolution of Race and Ethnicity Considerations for the Cardiology Workforce in the United States of America From 1969 to 2019

Since 1969, racial and ethnic preferences have existed throughout the American medical academy. The primary purpose has been to increase the number of blacks and Hispanics within the physician workforce as they were deemed to be "underrepresented in medicine." To this day, the goal continues to be population parity or proportional representation. These affirmative action programs were traditionally voluntary, created and implemented at the state or institutional level, limited to the premedical and medical school stages, and intended to be temporary. Despite these efforts, numerical targets for underrepresented minorities set by the Association of American Medical Colleges have consistently fallen short. Failures have largely been attributable to the limited qualified applicant pool and legal challenges to the use of race and ethnicity in admissions to institutions of higher education. In response, programs under the appellation of diversity, inclusion, and equity have recently been created to increase the number of blacks and Hispanics as medical school students, internal medicine trainees, cardiovascular disease trainees, and cardiovascular disease faculty. These new diversity programs are mandatory, created and implemented at the national level, imposed throughout all stages of academic medicine and cardiology, and intended to be permanent. The purpose of this white paper is to provide an overview of policies that have been created to impact the racial and ethnic composition of the cardiology workforce, to consider the evolution of racial and ethnic preferences in legal and medical spheres, to critically assess current paradigms, and to consider potential solutions to anticipated challenges.

Call to Action: Rural Health: A Presidential Advisory From the American Heart Association and American Stroke Association

Understanding and addressing the unique health needs of people residing in rural America is critical to the American Heart Association's pursuit of a world with longer, healthier lives. Improving the health of rural populations is consistent with the American Heart Association's commitment to health equity and its focus on social determinants of health to reduce and ideally to eliminate health disparities. This presidential advisory serves as a call to action for the American Heart Association and other stakeholders to make rural populations a priority in programming, research, and policy. This advisory first summarizes existing data on rural populations, communities, and health outcomes; explores 3 major groups of factors underlying urban-rural disparities in health outcomes, including individual factors, social determinants of health, and health delivery system factors; and then proposes a set of solutions spanning health system innovation, policy, and research aimed at improving rural health.

Access to specialty healthcare in urban versus rural US populations: a systematic literature review

Background

Access to healthcare is a poorly defined construct, with insufficient understanding of differences in facilitators and barriers between US urban versus rural specialty care. We summarize recent literature and expand upon a prior conceptual access framework, adapted here specifically to urban and rural specialty care.

Methods

A systematic review was conducted of literature within the CINAHL, Medline, PubMed, PsycInfo, and ProQuest Social Sciences databases published between January 2013 and August 2018. Search terms targeted peer-reviewed academic publications pertinent to access to US urban or rural specialty healthcare. Exclusion criteria produced 67 articles. Findings were organized into an existing ten-dimension care access conceptual framework where possible, with additional topics grouped thematically into supplemental dimensions.

Results

Despite geographic and demographic differences, many access facilitators and barriers were common to both populations; only three dimensions did not contain literature addressing both urban and rural populations. The most commonly represented dimensions were availability and accommodation, appropriateness, and ability to perceive. Four new identified dimensions were: government and insurance policy, health organization and operations influence, stigma, and primary care and specialist influence.

Conclusions

While findings generally align with a preexisting framework, they also suggest several additional themes important to urban versus rural specialty care access.

Renin-angiotensin system blockade after transcatheter aortic valve replacement (TAVR) improves intermediate survival

Introduction: Hypertension is common in patients with severe aortic stenosis undertaking transcatheter aortic valve replacement (TAVR). Renin–angiotensin system (RAS) blockade therapy with angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) has recently been associated with improved outcomes after surgical aortic valve replacement and TAVR, but it is unknown if these findings apply to a more rural patient population.

Methods: A retrospective cohort study of 169 patients with at least 1 year of post-TAVR follow-up at a single predominantly rural US center was performed to determine if RAS blockade after TAVR affects short- and long-term outcomes. Seventy-one patients were on an ACEI or ARB at the time of TAVR and at 1 year post-TAVR follow-up. Fisher’s exact test was used for categorical data and t-test/ANOVA was used to determine the statistical significance of continuous variables.

Results: In a well-matched cohort, RAS blockade therapy post-TAVR was associated with significantly improved overall survival at 2 years (95% vs. 79%, P = 0.042). RAS blockade was also associated with a trend towards decreased heart failure exacerbations in the first year after TAVR, which was statistically significant in the 30 days to 6 months timeframe after TAVR (8% vs. 21%, P = 0.032).

Conclusion: In a rural patient population, RAS blockade after TAVR is associated with improved overall survival and a trend towards decreased heart failure exacerbations. This study builds upon previous studies and suggests that TAVR should be considered a compelling indication for these agents.

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram

Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found^1-4. An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.