Abstract 5921: Medicare Beneficiaries with Mild to Severe Heart Failure See 15–23 Different Providers Annually

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Robert L Page ◽  
Christopher Hogan ◽  
Kara Strongin ◽  
Roger Mills ◽  
JoAnn Lindenfeld
Keyword(s):  
Heart Failure ◽  
Severe Heart Failure ◽  
The United States ◽  
Outpatient Setting ◽  
Fiscal Year ◽  
Hospital Inpatient ◽  
Medicare Beneficiaries ◽  
Improvement Program ◽  
Data Set ◽  
Mild Disease

In fiscal year 2003, Medicare beneficiaries with heart failure (HF) accounted for 37% of all Medicare spending and nearly 50% of all hospital inpatient costs. On average, each beneficiary had 10.3 outpatient and 2 inpatient visits specifically for HF. Despite significant improvements in medical care for HF, mortality and hospital admissions remain high. No data exist regarding the number of providers ordering and providing care for this population. An analysis of fiscal year 2005 Medicare claims was conducted, using a 5% sample standard analytic and denominator file, limited data set version to extrapolate the 34,150,200 Medicare beneficiaries. Three cohorts were defined according to mild, moderate, severe HF employing the Centers for Medicare and Medicaid Services Hierarchical Condition Categories Model and Chronic Care Improvement Program definitions. HMO enrollees, persons without Part A and Part B coverage, and those outside the United States were excluded. We identified physicians by using the unique physician identification number of performing physicians. Based on inclusion criteria, 173,863 beneficiaries were identified. The average number of providers providing care in all sites were 15.9, 18.6, 23.1 for beneficiaries with mild, moderate, and severe HF, respectively; and 10.1, 11.5, and 12.1 in the outpatient setting, respectively. The average number of providers ordering care in all sites consisted of 8.3, 9.6, and 11.2 for beneficiaries with mild, moderate, and severe HF, respectively; and 6.5,7.3, and 7.8 in the outpatient setting, respectively. For beneficiaries with mild disease, only 10% of all office visits were specifically for HF, while those with moderate or severe disease, only 20% were specifically for HF. Medicare beneficiaries with HF, even those with mild disease, have a large number of providers ordering and providing care. These data highlight the importance for developing systems and processes of coordinated care for this population.

Abstract 2707: The Magnitude of Health Care Resource Utilization by Medicare Beneficiaries with Heart Failure

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Robert L Page ◽  
Kara B Strongin ◽  
Roger M Mills ◽  
Christopher Hogan ◽  
JoAnn Lindenfeld
Keyword(s):  
Heart Failure ◽  
Health Care ◽  
The United States ◽  
Hospital Inpatient ◽  
Medicare Beneficiaries ◽  
Data Set ◽  
Physician Visits ◽  
Hospital Outpatient ◽  
Co Morbidity ◽  
Medicare Spending

Introduction: By 2010, the number of individuals ≥ 65 years with a heart failure (HF) diagnosis should increase by an additional 700,000. As the financial burden of HF is expected to substantially increase, we examined health care expenditures of Medicare beneficiaries with HF to estimate the current healthcare costs and resource allocation. Methods: An analysis of 2005 Medicare claims was conducted, using a 5% sample standard analytic and denominator file, limited data set version to extrapolate the 34,150,200 Medicare beneficiaries. The cohort was defined by the Centers for Medicare and Medicaid Services Hierarchical Condition Categories Model which requires one HF diagnosis from a physician or hospital outpatient department/inpatient bill. HMO enrollees, persons without Part A and Part B coverage, and those outside the United States were excluded. Results: Based on inclusion criteria, 260,076 beneficiaries were identified. Beneficiaries with HF accounted for 13% of the total beneficiary population and 37% of all Medicare spending. Reimbursement for hospital inpatient admissions, physician visits, and hospital outpatient visits accounted for $12,556; $5,875; and $2,753 per-capita, respectively. In one year, 22% of all beneficiaries required hospitalization compared to 59% of beneficiaries with HF. Thirty-one percent of beneficiaries with HF had ≥ 2 inpatient admissions. Twenty-four percent of all hospital discharges were for HF, either as a principal diagnosis or co-morbidity, accounting for $30.4 billion. On average, 8.3 different outpatient and inpatient providers ordered services for a single beneficiary. Beneficiaries with at least two prior HF hospitalizations within the index period had on average 3.04 physician visits every three months. Only 26% of these visits were conducted by a cardiologist. Conclusion : Medicare beneficiaries with HF impose a tremendous burden on Medicare, consisting of over one-third of Medicare spending. It will be important to determine how much of this burden is due to HF and how much to comorbid conditions. Development of specialized Medicare HF Management Programs, also providing comprehensive care for co-morbidities, could curtail these admissions and potentially reduce costs.

Abstract 388: Quality Improvement in the Outpatient Setting: Observations from the PINNACLE Registry® 2009 Q4-2013 Q1

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Katie Kehoe ◽  
Sherry Shultz ◽  
Fran Fiocchi ◽  
Qiong Li ◽  
Thomas Shields ◽  
...  
Keyword(s):  
South Carolina ◽  
Quality Improvement ◽  
Office Visit ◽  
The United States ◽  
Outpatient Setting ◽  
Quality Improvement Initiative ◽  
The South ◽  
Improvement Program ◽  
Heart Center ◽  
Over Time

Title: Quality Improvement in the Outpatient Setting: Observations from the PINNACLE Registry® 2009 Q4-2013 Q1 Authors: Katie Kehoe BSN, MS 1 ; Sherry Shultz RN, BSN, CIO 2 ; Fran Fiocchi MPH 1 ; Qiong Li PhD 1 ; Thomas Shields 1 ; Charlie Devlin MD FACC, FACP, FASNC 2 ; Nathan T Glusenkamp, MA 1 ; J. Brendan Mullen 1 ; Angelo Ponirakis, PhD 1 ; 1 American College of Cardiology, Washington, DC 2 South Carolina Heart Center, Columbia SC Background: The PINNACLE Registry® at the American College of Cardiology is the first outpatient practice-based quality improvement program in the United States. Begun as a pilot program in 2007, the registry systematically collects and reports on adherence to clinical guidelines in the care of patients with coronary artery disease, hypertension, atrial fibrillation and heart failure. Over time, these reports offer a unique opportunity for Quality Improvement (QI) in the outpatient setting. The current study aimed to assess the effect of QI in the outpatient setting using PINNACLE Registry data. Methods: The South Carolina Heart Center is a cardiovascular practice in Columbia, South Carolina. There are 19 providers, 5 office locations and NextGen EMR. The practice’s Quality Committee and Board meet monthly to review PINNACLE reports and identify areas for QI. This Clinical Quality Improvement Initiative began 10 years ago and consists of physicians, nurses, administrators, medical assistants, a medical record analyst and information systems staff. During this review, providers’ data was not blinded to others. QI Interventions implemented included physician and staff education, improving documentation during the office visit, addition of necessary fields to capture missing data and routine planned internal audits. Between October 1, 2009 and March 31, 2013 a total of 161,873 patient encounters were submitted to the registry. A two-tailed z test was performed to assess the significance in percentage changes between 2009 to 2013. Results: The following table showed significant percentage changes in six performance measures indicating interventions implemented by the practice demonstrate significant quality improvement over time from 2009-2013. Conclusions: Utilizing their PINNACLE Registry reports, the South Carolina Heart Center identified several areas for QI. Implementing multiple interventions, this practice was able to significantly improve their PINNACLE Reports and the quality of care provided.

scholarly journals Incorporating Point-of-Care Bacterial Fluorescence into a Wound Clinic Antimicrobial Stewardship Program

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1010
Author(s):  
Thomas E. Serena
Keyword(s):  
Fluorescence Imaging ◽  
Antimicrobial Stewardship ◽  
Chronic Wounds ◽  
The United States ◽  
Outpatient Setting ◽  
Joint Commission ◽  
World Health ◽  
Evidence Based ◽  
Hospital Inpatient ◽  
Imaging Device

Background: In 2014 the World Health Organization (WHO) warned of an emerging world-wide crisis of antibiotic-resistant microorganisms. In response, government and professional organizations recommended that health care systems adopt antimicrobial stewardship programs (ASPs). In the United States, the Centers for Medicare Services (CMS) mandated antimicrobial stewardship in the hospital inpatient setting. Effective 1 January 2020, the Joint Commission required ambulatory centers that prescribe antibiotics, such as wound centers, to institute an ASP. Chronic wounds often remain open for months, during which time patients may receive multiple courses of antibiotics and numerous antimicrobial topical treatments. The wound clinician plays an integral role in reducing antimicrobial resistance in the outpatient setting: antibiotics prescribed for skin and soft tissue infections are among the most common in an outpatient setting. One of the most challenging aspects of antimicrobial stewardship in treating chronic wounds is the inaccuracy of bacterial and infection diagnosis. Methods: Joint Commission lists five elements of performance (EP): (1) identifying an antimicrobial stewardship leader; (2) establishing an annual antimicrobial stewardship goal; (3) implementing evidence-based practice guidelines related to the antimicrobial stewardship goal; (4) providing clinical staff with educational resources related to the antimicrobial stewardship goal; and (5) collecting, analyzing, and reporting data related to the antimicrobial stewardship goal. This article focuses on choosing and implementing an evidence-based ASP goal for 2020. Discussion: Clinical trials have demonstrated the ability of fluorescence imaging (MLiX) to detect clinically significant levels of bacteria in chronic wounds. Combined with clinical examination of signs and symptoms of infection, the MLiX procedure improves the clinician’s ability to diagnose infection and can guide antimicrobial use. In order to satisfy the elements of performance, the MLiX procedure was incorporated into the annual ASP goal for several wound care centers. Clinicians were educated on the fluorescence imaging device and guidelines were instituted. Collection of antimicrobial utilization data is underway.

scholarly journals A Canadian perception of the nursing practice, research, and theoretical implications associated with Autologous Cell Transplantation

2021 ◽  
Author(s):  
Suzanne Fredericks ◽  
Monica Da Silva
Keyword(s):  
Heart Failure ◽  
Cell Transplantation ◽  
Nursing Practice ◽  
Severe Heart Failure ◽  
The United States ◽  
Practice Research ◽  
Innovative Therapy ◽  
Autologous Cell ◽  
Injured Myocardium ◽  
Autologous Cell Transplantation

Heart failure is a progressive disorder. An estimated 400,000 Canadians are diagnosed annually with heart failure, and a quarter experience severe heart failure that is unresponsive to medical therapy. Autologous cell transplantation (ACT) has been proposed as a new approach for cardiac repair, and holds enormous potential for the regeneration of injured myocardium cells. Currently, ACT is under investigation in Canada. The use of ACT as a treatment alternative for heart failure patients has been established over the past 5 years across Europe and the United States. This paper will present a Canadian perception of the nursing practice, research, and theoretical implications associated with this new and innovative therapy.

scholarly journals Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset

10.2196/19892 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e19892
Author(s):  
Patrick Essay ◽  
Baran Balkan ◽  
Vignesh Subbian
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Risk Factor ◽  
Random Forest ◽  
Acute Heart Failure ◽  
Vital Signs ◽  
The United States ◽  
Data Set ◽  
Failure Risk ◽  
Timeline Analysis

Background Heart failure is a leading cause of mortality and morbidity worldwide. Acute heart failure, broadly defined as rapid onset of new or worsening signs and symptoms of heart failure, often requires hospitalization and admission to the intensive care unit (ICU). This acute condition is highly heterogeneous and less well-understood as compared to chronic heart failure. The ICU, through detailed and continuously monitored patient data, provides an opportunity to retrospectively analyze decompensation and heart failure to evaluate physiological states and patient outcomes. Objective The goal of this study is to examine the prevalence of cardiovascular risk factors among those admitted to ICUs and to evaluate combinations of clinical features that are predictive of decompensation events, such as the onset of acute heart failure, using machine learning techniques. To accomplish this objective, we leveraged tele-ICU data from over 200 hospitals across the United States. Methods We evaluated the feasibility of predicting decompensation soon after ICU admission for 26,534 patients admitted without a history of heart failure with specific heart failure risk factors (ie, coronary artery disease, hypertension, and myocardial infarction) and 96,350 patients admitted without risk factors using remotely monitored laboratory, vital signs, and discrete physiological measurements. Multivariate logistic regression and random forest models were applied to predict decompensation and highlight important features from combinations of model inputs from dissimilar data. Results The most prevalent risk factor in our data set was hypertension, although most patients diagnosed with heart failure were admitted to the ICU without a risk factor. The highest heart failure prediction accuracy was 0.951, and the highest area under the receiver operating characteristic curve was 0.9503 with random forest and combined vital signs, laboratory values, and discrete physiological measurements. Random forest feature importance also highlighted combinations of several discrete physiological features and laboratory measures as most indicative of decompensation. Timeline analysis of aggregate vital signs revealed a point of diminishing returns where additional vital signs data did not continue to improve results. Conclusions Heart failure risk factors are common in tele-ICU data, although most patients that are diagnosed with heart failure later in an ICU stay presented without risk factors making a prediction of decompensation critical. Decompensation was predicted with reasonable accuracy using tele-ICU data, and optimal data extraction for time series vital signs data was identified near a 200-minute window size. Overall, results suggest combinations of laboratory measurements and vital signs are viable for early and continuous prediction of patient decompensation.

scholarly journals Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset (Preprint)

2020 ◽  
Author(s):  
Patrick Essay ◽  
Baran Balkan ◽  
Vignesh Subbian
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Risk Factor ◽  
Random Forest ◽  
Acute Heart Failure ◽  
Vital Signs ◽  
The United States ◽  
Data Set ◽  
Failure Risk ◽  
Timeline Analysis

BACKGROUND Heart failure is a leading cause of mortality and morbidity worldwide. Acute heart failure, broadly defined as rapid onset of new or worsening signs and symptoms of heart failure, often requires hospitalization and admission to the intensive care unit (ICU). This acute condition is highly heterogeneous and less well-understood as compared to chronic heart failure. The ICU, through detailed and continuously monitored patient data, provides an opportunity to retrospectively analyze decompensation and heart failure to evaluate physiological states and patient outcomes. OBJECTIVE The goal of this study is to examine the prevalence of cardiovascular risk factors among those admitted to ICUs and to evaluate combinations of clinical features that are predictive of decompensation events, such as the onset of acute heart failure, using machine learning techniques. To accomplish this objective, we leveraged tele-ICU data from over 200 hospitals across the United States. METHODS We evaluated the feasibility of predicting decompensation soon after ICU admission for 26,534 patients admitted without a history of heart failure with specific heart failure risk factors (ie, coronary artery disease, hypertension, and myocardial infarction) and 96,350 patients admitted without risk factors using remotely monitored laboratory, vital signs, and discrete physiological measurements. Multivariate logistic regression and random forest models were applied to predict decompensation and highlight important features from combinations of model inputs from dissimilar data. RESULTS The most prevalent risk factor in our data set was hypertension, although most patients diagnosed with heart failure were admitted to the ICU without a risk factor. The highest heart failure prediction accuracy was 0.951, and the highest area under the receiver operating characteristic curve was 0.9503 with random forest and combined vital signs, laboratory values, and discrete physiological measurements. Random forest feature importance also highlighted combinations of several discrete physiological features and laboratory measures as most indicative of decompensation. Timeline analysis of aggregate vital signs revealed a point of diminishing returns where additional vital signs data did not continue to improve results. CONCLUSIONS Heart failure risk factors are common in tele-ICU data, although most patients that are diagnosed with heart failure later in an ICU stay presented without risk factors making a prediction of decompensation critical. Decompensation was predicted with reasonable accuracy using tele-ICU data, and optimal data extraction for time series vital signs data was identified near a 200-minute window size. Overall, results suggest combinations of laboratory measurements and vital signs are viable for early and continuous prediction of patient decompensation.

scholarly journals Diagnosis-based risk adjustment and Australian health system policy

2006 ◽  
Vol 30 (1) ◽  
pp. 83 ◽  
Author(s):  
Ronald Donato ◽  
Jeffrey Richardson
Keyword(s):  
Health System ◽  
Risk Adjustment ◽  
The United States ◽  
Parameter Estimates ◽  
System Reform ◽  
Data Sets ◽  
Hospital Inpatient ◽  
Data Set ◽  
Health System Reform ◽  
Individual Level

Diagnosis-based risk adjustment is increasingly seen as an important tool for establishing capitation payments and evaluating appropriateness and efficiency of services provided and has become an important area of research for many countries contemplating health system reform. This paper examines the application of a risk-adjustment method, extensively validated in the United States, known as diagnostic cost groups (DCG), to a large Australian hospital inpatient data set. The data set encompassed hospital inpatient diagnoses and inpatient expenditure for the entire metropolitan population residing in the state of New South Wales. The DCG model was able to explain 34% of individual-level variation in concurrent expenditure and 5.2% in subsequent year expenditure, which is comparable to US studies using inpatient-only data. The degree of stability and internal consistency of the parameter estimates for both the concurrent and prospective models indicate the DCG methodology has face validity in its application to NSW health data sets. Modelling and simulations were conducted which demonstrate the policy applications and significance of risk adjustment model(s) in the Australian context. This study demonstrates the feasibility of using large individual-level data sets for diagnosis-based risk adjustment research in Australia. The results suggest that a research agenda should be established to broaden the options for health system reform.

Abstract P387: Length of Stay Among Medicare Beneficiaries Hospitalized with Heart Failure

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Emily B Levitan ◽  
Melissa K Van Dyke ◽  
Ligong Chen ◽  
Meredith L Kilgore ◽  
Todd M Brown ◽  
...  
Keyword(s):  
Heart Failure ◽  
Length Of Stay ◽  
Systolic Dysfunction ◽  
Skilled Nursing Facility ◽  
The United States ◽  
Demographic Characteristics ◽  
Hospital Length Of Stay ◽  
Medicare Beneficiaries ◽  
Nursing Facility ◽  
Skilled Nursing

Background: Heart failure (HF) is among the most common reasons for hospitalization in the United States. Hospital length of stay (LOS) is a driver of cost and disease burden. Objectives: To examine factors associated with LOS of HF hospitalizations. Methods: Medicare beneficiaries with fee-for-service and pharmacy coverage who had HF hospitalizations (inpatient claims with ≥1 overnight stay/2 hospital days with HF as the primary discharge diagnosis, discharged alive) between 2007 and 2011 were identified in the Medicare national 5% sample. The median and interquartile range (IQR) LOS was calculated by demographic characteristics, comorbidities, and discharge status based on Medicare claims data with the Kruskal-Wallis test to compare distributions in the overall population with HF (n = 45,584) and in the subpopulation with documented systolic dysfunction (n = 10,256). Results: The median LOS was 5 days (range 2-255, IQR 4-8 days) in the overall HF population and 5 days (range 2-204, IQR 4-8 days) in those with systolic dysfunction. Across most demographic characteristics and comorbidities, the median LOS was 5 days but was higher among nursing home residents and individuals with malnutrition in both groups and with chronic kidney disease in those with systolic dysfunction ( Figure ). All comorbidities were associated with a shift in the distribution toward longer LOS in the population with systolic dysfunction and all but coronary heart disease in the overall population (p < 0.001). HF patients discharged to a skilled nursing facility had longer LOS (median 7 days, IQR 5-10 days) versus other discharge statuses (median 5 days, IQR 3-7 days, p < 0.001) in both populations. Conclusions: In patients hospitalized for HF, the median LOS was 5 days across most comorbidities and other characteristics, but comorbidities were associated with a shift in the upper tail of the distribution toward longer LOS. Worse functional status (nursing residence or discharge to a skilled nursing facility) was associated with a higher median LOS.

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