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Excess Readmission vs Excess Penalties: Maximum Readmission Penalties as a Function of Socioeconomics and Geography.

ABSTRACT: BACKGROUND:The Hospital Readmission Reduction Program (HRRP) penalizes hospitals with "excess" readmissions up to 3% of Medicare reimbursement. Approximately 75% of eligible hospitals received penalties, worth an estimated $428 million, in fiscal year 2015. OBJECTIVE:To identify demographic and socioeconomic disparities between matched and localized maximum-penalty and no-penalty hospitals. DESIGN:A case-control study in which cases included were hospitals to receive the maximum 3% penalty under the HRRP during the 2015 fiscal year. Controls were drawn from no-penalty hospitals and matched to cases by hospital characteristics (primary analysis) or geographic proximity (secondary analysis). SETTING:A selectiion of 3383 US hospitals eligible for HRRP. PARTICIPANTS:Thirty-nine case and 39 control hospitals from the HRRP cohort. MEASUREMENTS:Socioeconomic status variables were collected by the American Community Survey. Hospital and health system characteristics were drawn from Centers for Medicare and Medicaid Services, American Hospital Association, and Dartmouth Atlas of Health Care. The statistical analysis was conducted using Student t tests. RESULTS:Thirty-nine hospitals received a maximum penalty. Relative to controls, maximum-penalty hospitals in counties with lower SES profiles are defined by increased poverty rates (19.1% vs 15.5%, ? = 0.015) and lower rates of high school graduation (82.2% vs 87.5%, ? = 0.001). County level age, sex, and ethnicity distributions were similar between cohorts. CONCLUSIONS:Cases were more likely than controls to be in counties with low socioeconomic status; highlighting potential unintended consequences of national benchmarks for phenomena underpinned by environmental factors; specifically, whether maximum penalties under the HRRP are a consequence of underperforming hospitals or a manifestation of underserved communities.

SUBMITTER: Caracciolo C

PROVIDER: S-EPMC6091554 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Excess Readmission vs Excess Penalties: Maximum Readmission Penalties as a Function of Socioeconomics and Geography.

Caracciolo Chris C Parker Devin D Marshall Emily E Brown Jeremiah J

Journal of hospital medicine 20170801 8

<h4>Background</h4>The Hospital Readmission Reduction Program (HRRP) penalizes hospitals with "excess" readmissions up to 3% of Medicare reimbursement. Approximately 75% of eligible hospitals received penalties, worth an estimated $428 million, in fiscal year 2015.<h4>Objective</h4>To identify demographic and socioeconomic disparities between matched and localized maximum-penalty and no-penalty hospitals.<h4>Design</h4>A case-control study in which cases included were hospitals to receive the ma ...[more]

PMID: 28786426

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Project description:ImportanceThe Hospital Readmissions Reduction Program (HRRP) is a Centers for Medicare and Medicaid Services policy that levies hospital reimbursement penalties based on excess readmissions of patients with 4 medical conditions and 3 surgical procedures. A greater understanding of factors associated with the 3 surgical reimbursement penalties is needed for clinicians in surgical practice.ObjectiveTo investigate the first year of HRRP readmission penalties applied to 2 surgical procedures-elective total hip arthroplasty (THA) and total knee arthroplasty (TKA)-in the context of hospital and patient characteristics.Design, setting, and participantsFiscal year 2015 HRRP penalization data from Hospital Compare were linked with the American Hospital Association Annual Survey and with the Healthcare Cost and Utilization Project State Inpatient Database for hospitals in the state of Florida. By using a case-control framework, those hospitals were separated based on HRRP penalty severity, as measured with the HRRP THA and TKA excess readmission ratio, and compared according to orthopedic volume as well as hospital-level and patient-level characteristics. The first year of HRRP readmission penalties applied to surgery in Florida Medicare subsection (d) hospitals was examined, identifying 60 663 Medicare patients who underwent elective THA or TKA in 143 Florida hospitals. The data analysis was conducted from February 2016 to January 2017.ExposuresAnnual hospital THA and TKA volume, other hospital-level characteristics, and patient factors used in HRRP risk adjustment.Main outcomes and measuresThe HRRP penalties with HRRP excess readmission ratios were measured, and their association with annual THA and TKA volume, a common measure of surgical quality, was evaluated. The HRRP penalties for surgical care according to hospital and readmitted patient characteristics were then examined.ResultsAmong 143 Florida hospitals, 2991 of 60 663 Medicare patients (4.9%) who underwent THA or TKA were readmitted within 30 days. Annual hospital arthroplasty volume seemed to follow an inverse association with both unadjusted readmission rates (r = -0.16, P = .06) and HRRP risk-adjusted readmission penalties (r = -0.12, P = .14), but these associations were not statistically significant. Other hospital characteristics and readmitted patient characteristics were similar across HRRP orthopedic penalty severity.Conclusions and relevanceThis study's findings suggest that higher-volume hospitals had less severe, but not significantly different, rates of readmission and HRRP penalties, without systematic differences across readmitted patients.

Project description:BackgroundContemporary species distribution, genetic diversity and evolutionary history in many taxa are shaped by both historical and current climate as well as topography. The Himalayas show a huge variation in topography and climatic conditions across its entire range, and have experienced major climatic fluctuations in the past. However, very little is known regarding how this heterogenous landscape has moulded the distribution of Himalayan fauna. A recent study examined the effect of these historical events on the genetic diversity of the Himalayan langurs in Nepal Himalaya. However, this study did not include the samples from the Indian Himalayan region (IHR). Therefore, here we revisit the questions addressed in the previous study with a near complete sampling from the IHR, along with the samples from the Nepal Himalaya. We used the mitochondrial Cytochrome-b (Cyt-b, 746 bp) region combined with multiple phylogeographic analyses and palaeodistribution modelling.ResultsOur dataset contained 144 sequences from the IHR as well as the Nepal Himalaya. Phylogenetic analysis showed a low divergent western clade nested within high divergent group of eastern lineages and in the network analysis we identified 22 haplotypes over the entire distribution range of the Himalayan langurs. Samples from the Nepal Himalaya showed geographically structured haplotypes corresponding to different river barriers, whereas samples from IHR showed star-like topology with no structure. Our statistical phylogeography analysis using diyABC supported the model of east to west colonisation of these langurs with founder event during colonisation. Analysis of demographic history showed that the effective population size of the Himalayan langurs decreased at the onset of last glacial maximum (LGM) and started increasing post LGM. The palaeodistribution modelling showed that the extent of suitable habitat shifted from low elevation central Nepal, and adjoining parts of north India, during LGM to the western Himalaya at present.ConclusionThe current genetic diversity and distribution of Himalayan langurs in the Nepal Himalaya has been shaped by river barriers, whereas the rivers in the IHR had relatively less time to act as a strong genetic barrier after the recent colonisation event. Further, the post LGM expansion could have had confounding effect on Himalayan langur population structure in both Nepal Himalaya and IHR.

Project description:AimsThis study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.Methods and resultsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full-text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time-point comparisons, ML-derived c-indices were higher than CSM-derived c-indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in-hospital mortality to 1 year survival; of these nine, seven reported higher c-indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c-indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c-indices 0.913 vs. 0.835).ConclusionsML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML-based studies of prediction modelling. We suggest that ML-based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867.

Project description:Uninsured and insured but out-of-network emergency department (ED) patients are often billed hospital chargemaster prices, which exceed amounts typically paid by insurers.To examine the variation in excess charges for services provided by emergency medicine and internal medicine physicians.Retrospective analysis was conducted of professional fee payment claims made by the Centers for Medicare & Medicaid Services for all services provided to Medicare Part B fee-for-service beneficiaries in calendar year 2013. Data analysis was conducted from January 1 to July 31, 2016.Markup ratios for ED and internal medicine professional services, defined as the charges submitted by the hospital divided by the Medicare allowable amount.Our analysis included 12?337 emergency medicine physicians from 2707 hospitals and 57?607 internal medicine physicians from 3669 hospitals in all 50 states. Services provided by emergency medicine physicians had an overall markup ratio of 4.4 (340% excess charges), which was greater than the markup ratio of 2.1 (110% excess charges) for all services performed by internal medicine physicians. Markup ratios for all ED services ranged by hospital from 1.0 to 12.6 (median, 4.2; interquartile range [IQR], 3.3-5.8); markup ratios for all internal medicine services ranged by hospital from 1.0 to 14.1 (median, 2.0; IQR, 1.7-2.5). The median markup ratio by hospital for ED evaluation and management procedure codes varied between 4.0 and 5.0. Among the most common ED services, laceration repair had the highest median markup ratio (7.0); emergency medicine physician review of a head computed tomographic scan had the greatest interhospital variation (range, 1.6-27.7). Across hospitals, markups in the ED were often substantially higher than those in the internal medicine department for the same services. Higher ED markup ratios were associated with hospital for-profit ownership (median, 5.7; IQR, 4.0-7.1), a greater percentage of uninsured patients seen (median, 5.0; IQR, 3.5-6.7 for ?20% uninsured), and location (median, 5.3; IQR, 3.8-6.8 for the southeastern United States).Across hospitals, there is wide variation in excess charges on ED services, which are often priced higher than internal medicine services. Our results inform policy efforts to protect uninsured and out-of-network patients from highly variable pricing.

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Excess Readmission vs Excess Penalties: Maximum Readmission Penalties as a Function of Socioeconomics and Geography.

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Excess Readmission vs Excess Penalties: Maximum Readmission Penalties as a Function of Socioeconomics and Geography.

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