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 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.

scholarly journals The Medicare Current Beneficiary Survey (MCBS): Providing Unique Access to Data on the Medicare Population

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 908-908
Author(s):  
Hildie Cohen ◽  
Sarah Hoyt ◽  
Sara Navin ◽  
Jennifer Titus ◽  
Mia Ibrahim
Keyword(s):  
Health Care ◽  
Health Care Expenditures ◽  
The United States ◽  
Special Topic ◽  
Care Utilization ◽  
Medicare Beneficiaries ◽  
Data Set ◽  
Medicare Current Beneficiary Survey ◽  
Medicare Population ◽  
The Impact

Abstract The Medicare Current Beneficiary Survey (MCBS) is a continuous, multipurpose survey of a nationally representative sample of the Medicare population, conducted by the Centers for Medicare & Medicaid Services (CMS). It collects data on demographics, health insurance, health status, health care expenditures, satisfaction with care, and access to care for Medicare beneficiaries. The MCBS provides a unique source of information regarding beneficiaries aged 65 and over and beneficiaries aged 64 and below with disabilities residing in the United States that cannot be obtained solely through CMS administrative sources. For researchers interested in issues of health care utilization and cost, CMS releases two Limited Data Set (LDS) files for each data year and a Public Use File (PUF) freely available for download and use. Also, special topic based PUFs have been released on the impact of COVID-19 on Medicare beneficiaries. This presentation will demonstrate the importance of the MCBS for research on the Medicare population, discuss how researchers can access the data, where researchers can find published MCBS estimates, what content areas have recently been added, such as food insecurity, limited English proficiency, and COVID-19 vaccination uptake and what new content is on the horizon. The presentation will also discuss the operational challenges posed by the COVID-19 pandemic, and the content enhancement opportunities created by the public health emergency. It will conclude with a review of the suite of materials and documentation available for data users to enhance their research and utilize more timely data.

scholarly journals Timing of Home Health Care Initiation and 30-Day Rehospitalizations among Medicare Beneficiaries with Diabetes by Race and Ethnicity

2021 ◽  
Vol 18 (11) ◽  
pp. 5623
Author(s):  
Jamie M. Smith ◽  
Haiqun Lin ◽  
Charlotte Thomas-Hawkins ◽  
Jennifer Tsui ◽  
Olga F. Jarrín
Keyword(s):  
Health Care ◽  
Home Health Care ◽  
Race And Ethnicity ◽  
Health Care Services ◽  
Propensity Scores ◽  
The United States ◽  
Home Health ◽  
Medicare Beneficiaries ◽  
Timely Initiation ◽  
Care Services

Older adults with diabetes are at elevated risk of complications following hospitalization. Home health care services mitigate the risk of adverse events and facilitate a safe transition home. In the United States, when home health care services are prescribed, federal guidelines require they begin within two days of hospital discharge. This study examined the association between timing of home health care initiation and 30-day rehospitalization outcomes in a cohort of 786,734 Medicare beneficiaries following a diabetes-related index hospitalization admission during 2015. Of these patients, 26.6% were discharged to home health care. To evaluate the association between timing of home health care initiation and 30-day rehospitalizations, multivariate logistic regression models including patient demographics, clinical and geographic variables, and neighborhood socioeconomic variables were used. Inverse probability-weighted propensity scores were incorporated into the analysis to account for potential confounding between the timing of home health care initiation and the outcome in the cohort. Compared to the patients who received home health care within the recommended first two days, the patients who received delayed services (3–7 days after discharge) had higher odds of rehospitalization (OR, 1.28; 95% CI, 1.25–1.32). Among the patients who received late services (8–14 days after discharge), the odds of rehospitalization were four times greater than among the patients receiving services within two days (OR, 4.12; 95% CI, 3.97–4.28). Timely initiation of home health care following diabetes-related hospitalizations is one strategy to improve outcomes.

scholarly journals Patient Suffering in Chronic Digestive Diseases: Will Primary Care-Specialist Collaboration With Effective Interactive Communication and Integrative Medicine in the Plan of Care Improve Quality of Life?

2020 ◽  
Vol 7 (6) ◽  
pp. 989-993
Author(s):  
Andrew Thomas ◽  
Annie Thomas
Keyword(s):  
Quality Of Life ◽  
Primary Care ◽  
Health Care ◽  
Health Care Providers ◽  
The United States ◽  
Care Providers ◽  
Physician Visits ◽  
Digestive Diseases ◽  
Care Specialist

Acute and chronic digestive diseases are causing increased burden to patients and are increasing the United States health care spending. The purpose of this case report was to present how nonconfirmatory and conflicting diagnoses led to increased burden and suffering for a patient thus affecting quality of life. There were many physician visits and multiple tests performed on the patient. However, the primary care physician and specialists could not reach a confirmatory diagnosis. The treatment plans did not offer relief of symptoms, and the patient continues to experience digestive symptoms, enduring this burden for over 2 years. The central theme of this paper is to inform health care providers the importance of utilizing evidence-based primary care specialist collaboration models for better digestive disease outcomes. Consistent with patient’s experience, the authors propose to pilot/adopt the integrative health care approaches that are proven effective for treating digestive diseases.

Access to Medical Care for Children and Adolescents in the United States

PEDIATRICS ◽  
1990 ◽  
Vol 86 (5) ◽  
pp. 666-673 ◽  
Author(s):  
David L. Wood ◽  
Rodney A. Hayward ◽  
Christopher R. Corey ◽  
Howard E. Freeman ◽  
Martin F. Shapiro
Keyword(s):  
Health Care ◽  
Medical Care ◽  
Children And Adolescents ◽  
The United States ◽  
Emergency Rooms ◽  
Community Clinics ◽  
Hospital Outpatient ◽  
Regular Source ◽  
Outpatient Departments ◽  
Source Of Care

To evaluate access to health care for American children and adolescents, a telephone survey of a national random sample of households was conducted in which 2182 children 17 years or younger were studied. Approximately 10% had no medical insurance; 10% had no regular source of care; and 18% identified emergency rooms, community clinics, or hospital outpatient departments as their usual site of medical care. Children who were uninsured, poor, or nonwhite were less likely to have seen a physician in the past year (P < .001), and uninsured children were less likely to have up-to-date immunizations. Logistic regression analyses revealed that poor, uninsured, or nonwhite children less frequently had a regular source of care; more frequently used emergency rooms, community clinics, and hospital outpatient departments as their regular providers; and more frequently encountered financial barriers to health care. Low-income or nonwhite children had much less access to care compared with children from more affluent or white families, independent of insurance status or health status.

scholarly journals Impact of Travel Time on Health Care Costs and Resource Use by Phase of Care for Older Patients With Cancer

2019 ◽  
Vol 37 (22) ◽  
pp. 1935-1945 ◽  
Author(s):  
Gabrielle B. Rocque ◽  
Courtney P. Williams ◽  
Harold D. Miller ◽  
Andres Azuero ◽  
Stephanie B. Wheeler ◽  
...  
Keyword(s):  
Health Care ◽  
Travel Time ◽  
Health Care Costs ◽  
Resource Use ◽  
Cancer Care ◽  
Medicare Beneficiaries ◽  
Patient Cost ◽  
Hospitalization Rates ◽  
Care Costs ◽  
Medicare Spending

PURPOSEMany community cancer clinics closed between 2008 and 2016, with additional closings potentially expected. Limited data exist on the impact of travel time on health care costs and resource use.METHODSThis retrospective cohort study (2012 to 2015) evaluated travel time to cancer care site for Medicare beneficiaries age 65 years or older in the southeastern United States. The primary outcome was Medicare spending by phase of care (ie, initial, survivorship, end of life). Secondary outcomes included patient cost responsibility and resource use measured by hospitalization rates, intensive care unit admissions, and chemotherapy-related hospitalization rates. Hierarchical linear models with patients clustered within cancer care site (CCS) were used to determine the effects of travel time on average monthly phase-specific Medicare spending and patient cost responsibility.RESULTSMedian travel time was 32 (interquartile range, 18-59) minutes for the 23,382 included Medicare beneficiaries, with 24% of patients traveling longer than 1 hour to their CCS. During the initial phase of care, Medicare spending was 14% higher and patient cost responsibility was 10% higher for patients traveling longer than 1 hour than those traveling 30 minutes or less. Hospitalization rates were 4% to 13% higher for patients traveling longer than 1 hour versus 30 minutes or less in the initial (61 v 54), survivorship (27 v 26), and end-of-life (310 v 286) phases of care (all P < .05). Most patients traveling longer than 1 hour were hospitalized at a local hospital rather than at their CCS, whereas the converse was true for patients traveling 30 minutes or less.CONCLUSIONAs health care locations close, patients living farther from treatment sites may experience more limited access to care, and health care spending could increase for patients and Medicare.

scholarly journals Association Between Medicare Star Ratings for Patient Experience and Medicare Spending per Beneficiary for US Hospitals

2017 ◽  
Vol 4 (1) ◽  
pp. 17-21 ◽  
Author(s):  
Stephen Trzeciak ◽  
John P Gaughan ◽  
Joshua Bosire ◽  
Mark Angelo ◽  
Adam S Holzberg ◽  
...  
Keyword(s):  
Patient Experience ◽  
Hospital Patient ◽  
Medicare Beneficiaries ◽  
Hospital Level ◽  
Data Set ◽  
Case Mix Index ◽  
Episode Of Care ◽  
Lower Health ◽  
Hospital Compare ◽  
Medicare Spending

Objective: To test the association between patient experience and Centers for Medicare and Medicaid Services (CMS) spending at the hospital level. Methods: Using CMS Hospital Compare data set, we analyzed 2014 data for CMS patient experience star ratings and the hospital Medicare Spending per Beneficiary (MSPB) Measure, which assesses price-standardized, risk-adjusted payments for services provided to Medicare beneficiaries for an episode of care from 3 days before hospital admission to 30 days following discharge. We tested the association using linear regression, adjusting for complexity of care using hospital Case Mix Index (CMI) and for socioeconomic status of the hospital patient population using Disproportionate Share Hospital (DSH) status. Results: The MSPB decreased with increasing hospital patient experience ratings. After adjustment for CMI and DSH, better hospital patient experience was associated with lower spending per episode (5.6% decrease from the lowest to highest patient experience star rating). Conclusion: We found that better hospital patient experience was associated with lower health-care spending. Further research is needed to define what specific elements and phases of the episode of care are driving the association.

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 Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach (Preprint)

2020 ◽  
Author(s):  
Sina Rashidian ◽  
Kayley Abell-Hart ◽  
Janos Hajagos ◽  
Richard Moffitt ◽  
Veena Lingam ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Health Care Providers ◽  
Demographic Data ◽  
The United States ◽  
Heterogeneous Data ◽  
Diabetes Diagnosis ◽  
Care Providers ◽  
Data Set ◽  
Laboratory Test Results

BACKGROUND Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the “gold standard” reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. OBJECTIVE This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. METHODS We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. RESULTS When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve–receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. CONCLUSIONS This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

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