Abstract 308: iHeart Failure: Using Innovative Methodologies From Technology And Manufacturing Companies To Reduce Readmissions

Heart failure (HF) patients have high 30-day readmission rates with high costs and poor quality of life. This study investigated the impact of a framework blending Lean Sigma, design thinking, and Lean Startup on 30-day all-cause readmissions among HF patients. This was a prospective study in an academic hospital in Baltimore, Maryland. Thirty-day all-cause readmission was assessed using the hospital's electronic medical record. The baseline readmission rate for HF was 28.4% in 2010 with 690 discharges. The framework was developed and interventions implemented in the second half of 2011. The impact of the interventions was evaluated through 2012. The rate declined to 18.9% among 703 discharges (P < .01). There was no significant change for non-HF readmissions. This study concluded that methodologies from technology and manufacturing companies can reduce 30-day readmissions in HF, demonstrating the potential of this innovations framework to improve chronic disease care.

Reducing Heart Failure Readmissions: A Clinical Business Analytics Approach

Reduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort. We hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF. After establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF. The HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P < .0001). In an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates.

Reducing Readmissions for Heart Failure.

Transitions in Care From Acute Care Telemetry Unit to Home: An Evidence-Based Quality Improvement Project.

Background: Heart failure (HF) is a common disease which is one of the most common causes of hospitalization. Although mortality rates are decreasing, readmission rates are still quite high. Objectives: We aimed to investigate the risk factors for readmission and death in patients who were hospitalized due to HF. Design and Setting: Retrospective study, Hacettepe University, Ankara, Turkey Methods: Patients hospitalized between 1 January 2014 to 31 December 2018 with the primary diagnosis of HF were included. Outcome variables were risk factors for 30-day all- caused readmission, 30-day HF related readmission, mortality. Results: All-cause 30-day readmission rate was 34.8% and HF-related 30-day readmission rate was 21.2%. The factors associated with increased all-caused 30-day readmission were male gender, hyperlipidemia, chronic liver disease, malignancy. The factors associated with increased HF-related 30-day readmission were hyperlipidemia, chronic liver disease, inflammatory rheumatologic diseases, malignancy. Use of ACE-i was found to be protective against all-cause and HF-related 30-day readmission. Factors associated with mortality were ejection fraction &lt;30%, chronic liver disease, acute kidney injury, hypoalbuminemia at the time of admission. Conclusions: Nearly one third of patients in this cohort who were hospitalized with a primary diagnosis of HF were readmitted in the following 30 days. Having certain chronic diseases and conditions were associated with an increased risk for readmission and mortality. These findings point out to the special needs of HF patients, who require a proactive, integrated and multidisciplinary management strategy to control the risk factors and to improve the inpatient and transitional stages in the hospital.

The Centers for Medicare & Medicaid Services uses a new peer group-based payment system to compare hospital performance as part of its Hospital Readmissions Reduction Program, which classifies hospitals into quintiles based on their share of dual-eligible beneficiaries for Medicare and Medicaid. However, little is known about the association of a hospital's share of dual-eligible beneficiaries with the quality of care and outcomes for patients with heart failure (HF). To evaluate the association between a hospital's proportion of patients with dual eligibility for Medicare and Medicaid and HF quality of care and outcomes. This retrospective cohort study evaluated 436 196 patients hospitalized for HF using the Get With The Guidelines-Heart Failure registry from January 1, 2010, to December 31, 2017. The analysis included patients 65 years or older with available data on dual-eligibility status. Hospitals were divided into quintiles based on their share of dual-eligible patients. Quality and outcomes were analyzed using unadjusted and adjusted multivariable logistic regression models. Data analysis was performed from April 1, 2020, to January 1, 2021. The primary outcome was 30-day all-cause readmission. The secondary outcomes included in-hospital mortality, 30-day HF readmissions, 30-day all-cause mortality, and HF process of care measures. A total of 436 196 hospitalized HF patients 65 years or older from 535 hospital sites were identified, with 258 995 hospitalized patients (median age, 81 years; interquartile range, 74-87 years) at 455 sites meeting the study criteria and included in the primary analysis. A total of 258 995 HF hospitalizations from 455 sites were included in the primary analysis of the study. Hospitals in the highest dual-eligibility quintile (quintile 5) tended to care for patients who were younger, were more likely to be female, belonged to racial minority groups, or were located in rural areas compared with quintile 1 sites. After multivariable adjustment, hospitals with the highest quintile of dual eligibility were associated with lower rates of key process measures, including evidence-based β-blocker prescription, measure of left ventricular function, and anticoagulation for atrial fibrillation or atrial flutter. Differences in clinical outcomes were seen with higher 30-day all-cause (adjusted odds ratio, 1.24; 95% CI, 1.14-1.35) and HF (adjusted odds ratio, 1.14; 95% CI, 1.03-1.27) readmissions in higher dual-eligible quintile 5 sites compared with quintile 1 sites. Risk-adjusted in-hospital and 30-day mortality did not significantly differ in quintile 1 vs quintile 5 hospitals. In this cohort study, hospitals with a higher share of dual-eligible patients provided care with lower rates of some of the key HF quality of care process measures and with higher 30-day all-cause or HF readmissions compared with lower dual-eligibility quintile hospitals.

The Impact of Telemonitoring on 30-day Readmissions for Patients Hospitalized with Heart Failure: A Safety-Net Hospital's Experience

As we approach the end of the first decade of the 21st century, it is evident that the epidemic of heart failure has not abated, nor have concerns about the quality of care received by patients with this condition. Recent data from the American Heart Association1 and the Centers for Disease Control and Prevention2 reaffirm that the number of patients hospitalized with heart failure has grown steadily over the past 30 years. With aging of the US population, this trend will undoubtedly continue, but it is also noteworthy that the increase in heart failure hospitalizations over the past few decades is not limited to the Medicare population.2 Moreover, the outcome of heart failure patients after hospital discharge is not improving. Despite evidence-based guidelines, performance measures, quality improvement programs, and public reporting of hospital-level performance data, the number of patients dying or readmitted to hospitals within 30 days of hospital discharge has not declined3–6 and is equivalent in patients with depressed and those with preserved left ventricular systolic function.7 Hospital readmission is expensive and contributes to the increasing economic burden of heart failure, but this is often a preventable event. However, the characteristics of patients who will require rehospitalization have not been identified,5 and it is impossible to predict which patients will be readmitted. In many cases it is not the patient, but the healthcare system involving multiple providers and transitions of care, that makes the conditions ripe for high readmission rates. Article See p 29 The fundamental first step in identifying and correcting gaps in healthcare quality is setting standards of care through development of evidence-based clinical practice guidelines. From quality indicators emphasized in guidelines, clinical performance measures can be derived to evaluate the quality of care provided by hospitals and by individual practitioners. …

The Impact Of Acute Heart Failure Related Length Of Stay On The 30-day All-Cause Readmission Rate.

Introduction: Noncardiac organ failure is a frequent complication in non–acute myocardial infarction (non-AMI) cardiogenic shock (CS), which accounts for a substantial proportion of CS hospitalizations. Although overall outcomes in non-AMI CS have improved, the effect of noncardiac organ failure on post-discharge outcomes remains understudied. Methods: Using the Nationwide Readmissions Database (2016–2021) we included non-AMI CS index hospitalizations among patients ≥ 18 years. Index hospitalizations were defined as patients discharged alive and prior to December of that year. After identifying acute kidney injury (AKI), acute neurological failure, acute hematologic failure, acute respiratory failure (ARF), and acute liver failure (ALF) during the index hospitalization, admissions were stratified into: no organ failure, single-system organ failure (OF), and multi-system OF (≥2 systems). Primary outcomes were 30-day all-cause and heart failure (HF) readmission rates. Logistic regression was used to assess the association between OF and all-cause readmissions. Results: We identified 170,247 index non-AMI CS hospitalizations. The median age was 66 years (IQR 56–75); 63% were male, and 69% had chronic heart failure. AKI (59.96%) was the most common organ failure, followed by ARF (50.96%) (Fig 1A). In total, 48.22% developed multi-system OF, 35.96% had single-system OF, and 15.82% had no organ failure. The overall 30-day all-cause and heart failure readmission rates were 18% and 4.9%, respectively. AKI was associated with the highest all-cause (19.85%) and HF (5.89%) readmission rates, followed by ALF (19.04% and 5.18%, respectively) (Fig 1B). All-cause readmissions were highest in patients with multi-system OF (18.75%) compared to those with no organ failure (15.64%) (p &lt; 0.001), while HF readmissions were highest in single-system OF (5.27%) (p &lt; 0.001) (Fig 1C). After adjustment, both multi-system OF (aOR 1.11; 95% CI 1.04–1.18) and multi-system OF (aOR 1.09; 95% CI 1.02–1.15) were independently associated with increased odds of all-cause readmissions (Table 1). Conclusion: Noncardiac organ failure during index non-AMI CS hospitalizations is linked to higher 30-day all-cause and HF readmission rates, with AKI showing the highest readmission rates. Both single and multi-system organ failure were independent predictors of all-cause readmissions, underscoring the importance of early post-discharge follow-up and multidisciplinary care planning for these high-risk patients.

Public reporting of hospitals' 30-day risk-standardized readmission rates following heart failure hospitalization and the financial penalization of hospitals with higher rates have been associated with a reduction in 30-day readmissions but have raised concerns regarding the potential for unintended consequences. To examine the association of the Hospital Readmissions Reduction Program (HRRP) with readmission and mortality outcomes among patients hospitalized with heart failure within a prospective clinical registry that allows for detailed risk adjustment. Interrupted time-series and survival analyses of index heart failure hospitalizations were conducted from January 1, 2006, to December 31, 2014. This study included 115 245 fee-for-service Medicare beneficiaries across 416 US hospital sites participating in the American Heart Association Get With The Guidelines-Heart Failure registry. Data analysis took place from January 1, 2017, to June 8, 2017. Time intervals related to the HRRP were before the HRRP implementation (January 1, 2006, to March 31, 2010), during the HRRP implementation (April 1, 2010, to September 30, 2012), and after the HRRP penalties went into effect (October 1, 2012, to December 31, 2014). Risk-adjusted 30-day and 1-year all-cause readmission and mortality rates. The mean (SD) age of the study population (n = 115 245) was 80.5 (8.4) years, 62 927 (54.6%) were women, and 91 996 (81.3%) were white and 11 037 (9.7%) were black. The 30-day risk-adjusted readmission rate declined from 20.0% before the HRRP implementation to 18.4% in the HRRP penalties phase (hazard ratio (HR) after vs before the HRRP implementation, 0.91; 95% CI, 0.87-0.95; P < .001). In contrast, the 30-day risk-adjusted mortality rate increased from 7.2% before the HRRP implementation to 8.6% in the HRRP penalties phase (HR after vs before the HRRP implementation, 1.18; 95% CI, 1.10-1.27; P < .001). The 1-year risk-adjusted readmission and mortality rates followed a similar pattern as the 30-day outcomes. The 1-year risk-adjusted readmission rate declined from 57.2% to 56.3% (HR, 0.92; 95% CI, 0.89-0.96; P < .001), and the 1-year risk-adjusted mortality rate increased from 31.3% to 36.3% (HR, 1.10; 95% CI, 1.06-1.14; P < .001) after vs before the HRRP implementation. Among fee-for-service Medicare beneficiaries discharged after heart failure hospitalizations, implementation of the HRRP was temporally associated with a reduction in 30-day and 1-year readmissions but an increase in 30-day and 1-year mortality. If confirmed, this finding may require reconsideration of the HRRP in heart failure.

Key Findings•A total of 84% of VPExam QI patients required moderate-significance modification of clinical care based on VPExam data, including volume status assessment with jugular venous pressure (JVP)/edema (47.6%), cardiopulmonary auscultation (33.3%), electrocardiogram detection of arrhythmias (14.2%), and structured data transmission of vitals, medication reconciliation, and labs (95.2%).•Nursing satisfaction (4.8/5) and compliance with follow-up (100%) was high. No significant technical errors were detected with deployment on over 550 devices.•VPExam QI intervention was associated with a 30-day hospital readmission rate of 9.52% from a baseline readmission rate of 15.9%, with a relative risk reduction 40.16%. Readmission rates fell precipitously, to 0%, for the last 4 consecutive months of VPExam QI. Thirty-day mortality rate was 4.76%.•VPExam QI provides supporting quality and feasibility data for increasing adoption of virtual care platforms with minimal barriers. Actionable physical exam and structured data optimize quality of transitional care, allowing for significant reductions in readmissions and mortality. Heart failure (HF) is a leading cause of morbidity and mortality in nearly 5.7 million Americans. With improved survival and an aging patient population, the cost associated with HF management is expected to reach close to $70 billion by 2030.1Ponikowski P. Voors A.A. Anker S.D. et al.ESC Scientific Document Group2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.Eur Heart J. 2016; 37 ([Erratum in: Eur Heart J 2016;PMID: 27206819]): 2129-2200Crossref PubMed Scopus (8578) Google Scholar Skilled nursing facilities (SNFs) are progressively used to care for older patients with significant comorbidities, including HF.2Orr N.M. Forman D.E. De Matteis G. Gambassi G. Heart failure among older adults in skilled nursing facilities: more of a dilemma than many now realize.Curr Geriatr Rep. 2015; 4: 318-326Crossref PubMed Scopus (15) Google Scholar However, morbidity and mortality rates are high for hospitalized HF patients discharged to SNFs, with 30-day readmission rates between 27% and 43%, owing to errors in transitions, inadequate discharge planning, and lack of appropriate follow-up with health care providers.3Jurgens C.Y. Goodlin S. Dolansky M. et al.American Heart Association Council on Quality of Care and Outcomes Research and the Heart Failure Society of America. Heart failure management in skilled nursing facilities: a scientific statement from the American Heart Association and the Heart Failure Society of America.Circ Heart Fail. 2015; 8: 655-687Crossref PubMed Scopus (34) Google Scholar Optimizing HF management in SNF populations who are underserved yet at high risk for hospitalization is a critical area of focus for value-based care. The COVID-19 pandemic has increased utilization of telemedicine for HF management. Telehealth is a growing field of importance owing to value-based care and population management incentives, as well as increased acceptance in the general and medical population since the COVID crisis. Numerous meta-analyses have demonstrated that compared to conventional care, the addition of telemedicine in HF management leads to reduced hospitalization and mortality.4Zhu Y. Gu X. Xu C. Effectiveness of telemedicine systems for adults with heart failure: a meta-analysis of randomized controlled trials.Heart Fail Rev. 2020; 25: 231-243Crossref PubMed Scopus (55) Google Scholar,5Gingele A.J. Ramaekers B. Brunner-La Rocca H.P. et al.Effects of tailored telemonitoring on functional status and health-related quality of life in patients with heart failure.Neth Heart J. 2019; 27: 565-574Crossref PubMed Scopus (10) Google Scholar However, there is a paucity of data regarding the use of virtual care in SNF and home care patients with HF. Telemedicine platforms have traditionally presented numerous obstacles, including affordability of equipment, difficult-to-use technology requiring extensive training, lack of standardization, and patient privacy issues. These barriers are especially challenging to SNF and home care populations, who often have significant comorbidities, functional impairments, and barriers to transportation and access.6Woo K. Dowding D. Factors affecting the acceptance of telehealth services by heart failure patients: an integrative review.Telemed J E Health. 2018; 24: 292-300Crossref PubMed Scopus (23) Google Scholar Telemedicine can offer these populations improved access, but this is typically at the expense of clinical data such as a comprehensive physical examination and other structured data including vitals, medication reconciliation, and laboratory results. Many of the most common and expensive cardiopulmonary diseases rely on physical examination for detection of decompensation or exacerbation (Figure 1). VPExam is a Health Insurance Portability and Accountability Act (HIPAA)–compliant medical device data system that assists in overcoming barriers to traditional telemedicine using a combination of augmented reality–based guidance leading a minimally trained nurse or medical assistant (MA) through appropriate camera positioning for video capture based on anatomical landmarks. VPExam augmented reality–guided overlays, combined with sample videos as well as text and audio instructions, teach users how to obtain optimal video recordings of clinical findings such as evaluation of jugular venous distention and degree of lower-extremity edema in a reproducible manner. VPExam is integrated with Bluetooth-enabled stethoscopes, including the Eko Duo with single-lead electrocardiogram (ECG), to capture a full heart and lung exam with active user instruction (Figure 2). Virtual physical examination components are customizable by providers to optimize an efficient clinical workflow. The type of physical exam can be customized by medical specialty. Default physical exam components for VPExam QI included evaluation with (1) head-to-toe pan, (2) oral mucosa, (3) jugular venous assessment, (4) lower-extremity edema evaluation, (5) cardiac auscultation with single-lead ECG at the right upper sternal border and right lower sternal border, and (6) pulmonary auscultation at 2 sites. VPExam app data acquisition creates a partnership between minimally trained users (nurses or MAs) and physicians to efficiently gather and transmit relevant medical data to the Physician Portal. On average, nurses/MAs required a single 20-minute training session before becoming clinically proficient to use the platform in clinical practice. VPExam nurse assessment was completed in under 5 minutes on average. VPExam data were reviewed by the managing physician using the Physician Portal in under 2 minutes on average prior to proceeding to real-time video encounter with the patient and saved in the electronic medical record (EMR). Time-efficient review of asynchronous, clinically relevant patient management data has proven critical for ongoing physician engagement when using virtual care telemedicine. In addition to physical exam data, VPExam allows nurses to transmit structured data including manual transmission of vitals, voice recognition–based virtual history and review of systems, medication reconciliation with photographs of pill bottles, and a document scanner to transmit laboratory results, orders, logs, ECGs, etc (Figure 3). Providers reviewed VPExam Physician Portal asynchronous data prior to initiating a real-time HIPAA-compliant video conference with the patient. The Eko stethoscope with ECG can also be used by the provider during real-time synchronous video conferencing in coordination with the nurse/MA user with the patient (Figure 3). VPExam routinely transmits digital physical exam data for comprehensive assessment of volume status, cardiopulmonary auscultation, ECG detection of arrhythmias, and structured data including vitals, medication reconciliation, and labs. VPExam Partner Portal allows physicians to collaborate with partners at SNFs and home care to schedule VPExam follow-up post discharge. VPExam Partner Portal sends automated e-mail notifications to administrators or directors of nursing at partner facilities. SNFs and home care have HIPAA-compliant access to coordination-of-care data as well as prepopulated electronic home care or SNF orders sent by physician users (Figure 3). VPExam also offers additional remote patient monitoring (RPM) using Apple Health and Google Fit Health Kit data transmission to the VPExam Provider Portal, including blood glucose, body mass index, cardio fitness, blood pressure, ECG, heart rate, oxygen saturation, walking heart rate, and weight (Figure 4). VPExam RPM allows for transmitting alerts such as tachycardia with arrhythmia from a smartwatch, revealing a patient going into atrial fibrillation, or greater than 3-pound weight gain from a Bluetooth scale, revealing a patient suffering from decompensation of HF. Abnormal RPM data can trigger deployment of personnel to perform VPExam in a patient's home or facility for earlier intervention by a physician. For VPExam QI, supplemental health kit–based RPM hardware was not routinely deployed as part of the workflow. The focus was placed on the core VPExam technology of a tablet paired with Bluetooth stethoscope to transmit physical exam data as the primary intervention in order to avoid additional confounders. VPExam QI was designed as a single-arm prospective comparative community case study recruiting patients admitted for HF at University Hospitals Regional Medical Centers with anticipated discharge to SNFs or home care as well as those requiring urgent cardiology consultation with VPExam intervention. Primary outcomes included the degree to which unique VPExam digital physical exam data to assess volume status, cardiopulmonary auscultation, ECG detection of arrhythmias, and structured data of vitals, medication reconciliation, and labs would impact individual transition-of-care management as well as VPExam impact 30-day all-cause rehospitalization rates. The design also sought to assess feasibility of transition-of-care workflows within SNFs and home care, compliance with follow-up, technical stability of the platform, nursing staff satisfaction with the platform, and financial analysis of virtual care networks. The study design attempted to overcome common barriers to traditional telemedicine. Training barriers were reduced using user-friendly VPExam augmented reality–guided overlays to guide new nurse and MA users through how to obtain optimal video recordings of clinical findings such as jugular venous distention and degree of lower-extremity edema. The VPExam platform was cost-effective, requiring only a tablet and Bluetooth stethoscope at each sending site, allowing for rapid scaling within the health system. VPExam was approved by a thorough institutional Information Technology (IT) Architecture review process by University Hospitals of Cleveland IT Department to ensure HIPAA and HITECH compliance. The goal of VPExam QI was to explore potential clinical benefits to active physician-directed disease management using the VPExam technology and workflows for patients undergoing transitions of care. VPExam QI focused on gathering data on patients being discharged to SNFs or home care from a community-based hospital. VPExam QI evaluated whether a comprehensive virtual care platform can enhance the quality of transitions of care. Three SNF and 1 home care partners were selected with the highest numbers of HF patients being discharged to their care from the 2 medical centers before initiation of the pilot to increase data acquisition. University Hospitals of Cleveland's IT department successfully deployed VPExam on over 550 hospital-owned devices and University Hospitals home care. Prior to deployment of VPExam QI, baseline hospital HF outcome data were collected. From June 2020 to June 2021, patients discharged from University Hospitals of Cleveland Regional Medical Centers received guideline-directed HF therapy in accordance with the American College of Cardiology expert consensus guidelines for HF management and follow-up. The same cardiologist and advanced practice providers managed the control population from June 2020 to June 2021, as well as the study population from July 2021 to December 2021, with VPExam acting as the primary intervention. The prospective single-arm community case study of VPExam QI used convenience sampling to recruit patients admitted with HF who met eligibility criteria from University Hospitals of Cleveland Regional Medical Centers. Twenty-one patients with 25 independent patient encounters received virtual care intervention using VPExam technology and established workflows. VPExam QI focused on patients who were being discharged to 3 partner SNFs or follow-up from University Hospitals home care. VPExam QI recruited patients for the 6-month period from July 2021 through December 2021. All patients approached agreed to participate. Once patients were enrolled in the VPExam QI, encounters were completed as routine follow-up appointments within 1–3 weeks of discharge. Partners were also permitted to enroll urgent encounters as part of VPExam QI in coordination with the research team to allow for urgent cardiology consultation. The intervention of VPExam was compared to historical community matched controls. The control group data were derived from historical community patient data from the hospital system in the months and year immediately prior to VPExam intervention. The control group received guideline-directed HF therapy without routine follow-up at their SNF or home care visits with cardiology-led telemedicine with physical exam data from the same cardiology team as the intervention group. To identify potential patients, a daily automated list was generated from the EMR of patients admitted to the 2 hospitals for HF, known as the "Currently Admitted Patients with Heart Failure" from Allscripts Sunrise EMR. HF patients were identified based on brain natriuretic peptide (BNP) of greater than 300 or left ventricular ejection fraction (LVEF) of less than 40%. The team emphasized enrollment of patients who had been readmitted to the hospital within the last 30 or 90 days, as well as patients' being active on cardiology consultation services. The team enrolled patients being discharged to 1 of 3 SNF partners or eligible for University Hospital Home Health with VPExam capabilities. Exclusion criteria included the following: (1) those not meeting the definition of HF patients used in VPExam QI; (2) severe dementia preventing the patient's participation; (3) patients who are unable or unlikely to comply with telemedicine encounters. VPExam QI's primary objective was to determine the degree of significance of physician-directed disease management based on unique VPExam data including volume status assessment, cardiopulmonary auscultation, ECG arrhythmia detection, and structured data transmission, as well as the downstream impact on 30-day all-cause rehospitalization rates. Outcome measures included the following: (1) identifying the degree of significance of disease-altering care (minor, moderate, or major) based on VPExam virtual care data (Table 1); (2) 30-day all-cause hospitalization rates; (3) compliance with follow-up visits post discharge; (4) nursing staff satisfaction with using the VPExam platform using a 6-point Likert scale (0 being completely dissatisfied to 5 being completely satisfied); (5) evaluation for technical errors of software using a 6-point Likert scale (0 meaning no technical error and 5 meaning unable to complete telemedicine encounter owing to technical error); and (6) 30- and 90-day all-cause mortality rates.Table 1Significance criteria for modification of clinical careModification of clinical careExampleMinor or no change in medical careStable patients with benign VPExam data without need to alter planned therapyModerate changes in medical careVPExam data result in diuretic and vasoactive medication adjustments or detecting errors in medication reconciliationEmergent changes in medical careVPExam used emergently to make decisions whether to triage a patient to the emergency room Open table in a new tab With each encounter, demographic data points were collected including sex, body mass index, tobacco abuse history, Charlson Comorbidity Index, NYHA classification, days since previous hospitalization, relevant echocardiogram data with LVEF, degree of valvular disease and pulmonary hypertension, and laboratory data including BNP and glomerular filtration rate trends (Table 2).Table 2Demographic and clinical characteristics of patient participantsCharacteristicsVPExam intervention groupAge, mean (SD), mean Comorbidity Index, mean based on mean based on mean on discharge from based on mean of modification to clinical care, reconciliation data to modification of care, status evaluation and ECG data medical of guideline-directed satisfaction mean error mean atrial body mass BNP brain natriuretic ECG glomerular filtration jugular venous left ventricular ejection based on Open table in a new tab atrial body mass BNP brain natriuretic ECG glomerular filtration jugular venous left ventricular ejection and functional characteristics were using were as mean or as The intervention was as a virtual care practice change to the quality of care. were collected and using a data in a on a All received the of the agreed to before and were to from the study at was from the or from of for the of or data included in this The community case study recruited patient with 25 independent encounters for patients discharged to SNFs or home care from 2 community University Hospitals of Cleveland Regional Centers. patients were on average (Table of the population was and only were active The average body mass of the study population was BNP on was on average The average glomerular filtration rate on discharge for patients was The mean was those patients, had HF with reduced and had HF with were to be in NYHA or NYHA The mean Charlson Comorbidity was a with significant and an satisfaction of using this platform was for 25 of nursing staff that were also regarding technical with the use of the platform on a 6-point Likert scale, with meaning no technical error and 5 meaning unable to use the platform owing to technical For nursing staff in the the was No technical errors were detected partners at 3 SNFs and deployment on over 550 home medical VPExam QI patients received clinical follow-up for and data for 3 months VPExam were no patients who and there were no All patients and 25 encounters at partner SNFs and home care nurses were with VPExam visits by VPExam Partner Portal. total of of study encounters with home care services and of encounters at the 6-month of hospitalized HF patients at University Hospitals of Cleveland Regional Hospitals were enrolled into VPExam QI. Cardiology management was based on guideline-directed of patients were on total of of patients were on and of patients were on on follow-up visits during the The most common for or was acute by including by of patient encounters required only or less significant clinical modification of care, 84% of patient encounters required moderate-significance changes in clinical modification based VPExam follow-up (Table The most common moderate-significance changes diuretic adjustments of and vasoactive medication adjustments of moderate-significance clinical included of medication reconciliation errors in of encounters and of in of encounters. modification encounters were by volume status assessment (47.6%), cardiopulmonary auscultation (33.3%), ECG detection of (14.2%), and structured data transmission of vitals, medication reconciliation, and satisfaction with the platform was high. Prior to deployment of VPExam QI, baseline hospital HF outcome data were collected. From June 2020 to June 2021, patients discharged from University Hospitals of Cleveland Regional Medical Centers received guideline-directed HF therapy in accordance with the American College of Cardiology expert consensus guidelines management and follow-up. The same cardiologist and advanced practice providers managed the control population from June 2020 to June 2021, as well as the intervention population from July 2021 to December 2021, with VPExam acting as the primary intervention. The historical control population of HF patients discharged from University Hospitals of Cleveland Regional Medical Centers from June 2020 to June 2021 had an average all-cause readmission rate of with total encounters. intervention with VPExam QI 30-day hospital rates to for the HF population, with total encounters. the encounters of VPExam QI, the all-cause 30-day hospital rate was VPExam was associated with a relative risk reduction for 30-day readmission compared to historical community matched than of VPExam QI patients were recruited in months 1 and 2 of the pilot when the readmission rate for the hospital system to historical However, by months of the pilot more than of VPExam patients were recruited and readmission rates fell to for 4 months (Figure HF patients with HF mortality rate hospitalization is at 30 days, at 1 and at 5 et the American Heart Association and Heart disease and a from the American Heart PubMed Scopus Google Scholar Thirty-day mortality in VPExam QI was lower at 30 days, at with a relative risk reduction of compared to historical controls. 90-day mortality of the study population was at a patient population that was high risk significant comorbidities, as by an average Charlson Comorbidity of The VPExam QI used the ongoing for a providers to the patients and quality for transitions of care. also hospital systems overcome to new technology workflows. barriers identified in review preventing of quality the following: (1) the of HF can be and make to (2) guideline-directed are owing to on their and (3) SNF providers can be managing HF owing to and at (4) there is and lack of between SNF patients, and their (5) there are in of and access to for care of and (6) there are and N.M. Forman D.E. De Matteis G. Gambassi G. Heart failure among older adults in skilled nursing facilities: more of a dilemma than many now realize.Curr Geriatr Rep. 2015; 4: 318-326Crossref PubMed Scopus (15) Google K. Dowding D. Factors affecting the acceptance of telehealth services by heart failure patients: an integrative review.Telemed J E Health. 2018; 24: 292-300Crossref PubMed Scopus (23) Google M. C. emergency department from skilled nursing facilities through an emergency physician telemedicine J 2020; PubMed Google et failure management in nursing a J 2018; PubMed Scopus (10) Google Scholar VPExam QI that unique VPExam overcome many of these barriers a virtual care between hospital systems and partner SNFs and home care. VPExam deployment to SNFs and home care is and VPExam virtual care high compliance with follow-up, with high satisfaction from nursing for patient management structured data transmission of the platform to modification in clinical management over of encounters. data transmission of improved detection of hypertension, and often in traditional telemedicine. data transmission of medication reconciliation often identified transition-of-care data transmission of labs including BNP and blood stability improved the quality of medical VPExam is also to clinical decisions with unique physical exam status including jugular venous distention and lower-extremity edema modification management in of encounters. and asynchronous stethoscope auscultation of the heart to and as well as auscultation of the to and modification management in over of encounters. and asynchronous single-lead ECG transmission for arrhythmia detection including atrial modification management in over of encounters. The that virtual care in transitions of care to SNFs and home care offer a to quality of care and risk of errors for cardiopulmonary VPExam was associated with improved adjustments in medication based on physical exam data, errors in medication reconciliation, and of medication compliance. a the reduction in 30-day hospitalization and mortality the that unique VPExam virtual care data care providers to optimize transitional quality of care traditional guideline-directed quality adoption of virtual care, there are also financial for of SNF to emergency is to increased morbidity and as well as significant M. C. emergency department from skilled nursing facilities through an emergency physician telemedicine J 2020; PubMed Google Scholar The average cost of readmission is VPExam clinical modification is associated with a significant reduction in 30-day there are over readmissions for HF, readmissions for cardiac arrhythmias, and readmissions with from VPExam cardiopulmonary and volume status evaluation chronic pulmonary disease with with and failure with readmissions of with and Hospital by 2021 for Research and Scholar VPExam the cost of patients to and including cost and nursing Virtual care also the risk of diseases such as COVID the health care as well as the to the including potential need for In addition to the financial to using virtual care to the cost of the platform can also increase individual provider VPExam data were to a of encounter on average, increasing to with an average increase in of and relative VPExam services are eligible for RPM using and with VPExam QI received RPM from including and the VPExam QI is a quality and feasibility study during an COVID crisis. that of virtual care in other hospital systems at a scale as a quality be to outcomes the of care. Virtual care offers earlier detection of clinical decompensation to underserved and decisions by in reduced risk of hospital and mortality on discharge. In improved transitional care assists health care systems in care and more efficient utilization of and care significant of the VPExam QI is the scale of a owing to the to and allow for data in a a case study with the most appropriate for The were deployed from community-based hospitals in that not be to more or health care be by access to especially for health for VPExam technology deployment was with 3 SNFs and a home care the has to SNFs in of patients home with home care services. Patients discharged to SNFs and home care on average have greater and barriers to care than patients discharged to home without such services. the intervention of VPExam QI was focused on the HF The research team included 2 2 2 cardiology nurse and 1 with training were during VPExam QI encounters to of data analysis and guideline-directed VPExam QI encounters were completed as routine follow-up appointments or urgent of the for a lack of significance modification of clinical management in the VPExam QI was that the of the study team to access to cardiology care for urgent an VPExam was not established with partner SNFs and home care. The of VPExam technology in these of encounters can be in the by the platform with a clinical team supporting these of encounters and offers greater potential virtual care to transitions of care in the underserved SNF and home care patient populations a to high readmission rates in HF VPExam offers to efficiently transmit virtual physical exam cardiopulmonary and volume status with supplemental structured data, including vitals, medication reconciliation, and labs critical to HF management. platforms offer to optimize quality of care, readmission and mortality rates. Virtual care in HF management is a intervention with quality for SNF and home care patients, who traditionally have access to care. not only focus on how virtual care can be integrated to quality of care, but also the of health care the growing of health care and hospital for digital research allows physicians the to the be used in workflows to optimize and quality of care. The the and University Hospitals of Cleveland Heart and for financial and institutional for digital physician and clinical The VPExam QI study was by University Hospitals of Cleveland Heart and The is the of the and not the of University Hospitals of

Readmission after hospitalization for heart failure (HF) has received increasing attention due to the significant burden it places on patients and payers.[1][1]–[2][2] Among Medicare beneficiaries, readmission within 30 days following heart failure hospitalization approaches 25%.[2][2] Even after

Introduction: Volume-outcome relationship data is limited for acute myocardial infarction-cardiogenic shock (AMI-CS). Revascularization is mainstay of therapy in AMI-CS. Objectives: Authors sought to examine relation between hospital inpatient percutaneous coronary intervention (PCI) volume and 30-day readmissions after an AMI-CS admission. Methods: Nationwide Readmissions Database (NRD) 2016-2019 was analyzed. Hospitals were categorized into quartiles (Q1, lowest to Q4, highest) based on annual inpatient PCI volume. Primary outcome of interest was 30-day unplanned all-cause readmissions. Secondary outcomes included cardiac, non-cardiac and heart-failure (HF) readmissions at 30-days. Results: A total of 49,558 index AMI-CS admissions were present in 3,954 PCI performing hospitals. Median hospital PCI volume was 174 (inter-quartile range 70-316). 59% admissions for AMI-CS were present in quartile Q4. Overall, 30-day readmission rate was 18.5% (n=9,179); of which 56.2% were cardiac, 43.8% were non-cardiac and 25.8% were related to HF. We did not find any difference in 30-day all-cause readmissions (Q1 = 17.6% vs. Q2 = 18.4% vs. Q3 = 18.2% vs. Q4 = 18.7%, p=0.55). Similarly, cardiac (Q1 = 10.9% vs. Q2 = 11.0% vs. Q3 = 10.6% vs. Q4 = 10.2%, p=0.29), and HF (Q1 = 5.0% vs. Q2 = 4.8% vs. Q3 = 4.8% vs. Q4 = 4.8%, p=0.99) readmissions were not different across quartiles. Non-cardiac readmissions were more-commonly observed in higher quartiles (Q1 = 6.7% vs. Q2 = 7.4% vs. Q3 = 7.7% vs. Q4 = 8.5%, p=0.001). However, no significance was noted with any outcome after multivariable adjustment. Similarly, no relationship was observed between hospital PCI volume as continuous variable and all-cause or cause-specific readmissions on restricted cubic spline analysis. Conclusions: In a national representative sample of 3,954 hospitals with 49,558 AMI-CS admissions, we found lack of an association between hospital PCI volume and 30-day all cause or cause-specific readmissions.