This dataset contains the statewide number and (unadjusted) rate for all-cause, unplanned, 30-day inpatient readmissions in California hospitals. Data are categorized by age, sex, race/ethnicity, expected payer and county.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses.

The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals.

Restricted access data files are available with a data use agreement and brief online security training.

This statistic displays the rate of 30-day acute-care hospital readmissions in the United States from 2015 to 2017, by disease. In 2017, some 21.7 percent of those with heart failure were readmitted to the hospital in the United States within 30 days.

In FY2022, from the 3,046 hospitals Medicare assessed for hospital readmissions, 2,273 (or 42 percent) were penalized for readmission rates exceeding 30-day risk-standardized readmission rates. The Hospital Readmissions Reduction Program (HRRP) was created as part of the Affordable Care Act's (ACA's) payment and delivery system reform, to focus on quality rather than quantity of care. Preventable rehospitalization costs Medicare hundreds of millions of dollars each year and can be avoided through better care and more attention paid to the patients during discharge and transition. Thus Medicare is reducing its payments to these 2,273 hospitals with an average penalty of 0.43 percent reduction in payment for each Medicare patient for FY2023. Penalties are capped at three percent. This statistic presents the number of hospitals in the United States that Medicare punished in FY 2023 for high readmission based on patients discharged in the past three years.

The Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all patients regardless of the expected payer for the hospital stay. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in healthcare data - the lack of nationally representative information on hospital readmissions for all ages.

Datasets for the study of factors predicting hospital readmission among Thais with post myocardial infarction

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

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This statistic displays variations in 30-day-readmission rates among for-profit hospitals compared to not-for-profit hospitals in the U.S. between 2011 and 2015, by selected condition. In the given period, 30-day readmission rates for heart attacks were 0.35 percent higher in for-profit hospitals. Generally, readmission rates are higher in for-profit hospitals.

Background

The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain.

Objective To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR).

Research Design We assessed 71 community factors in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital.

Subjects Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n= 6,790,723). Patients were linked to community factors using their 5-digit zip code of residence.

Methods We used a random forest algorithm to rank factors for their importance in predicting hospital HWR scores. Factors were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Factors with with P-values <0.10 were retained for a final model, after eliminating any that were collinear.

Results Among 71 community factors, 19 were retained in the 6 domain models and the final model. Domains which explained the most to least variance in HWR were: physical environment (R2=15%); clinical care (12%); demographics (11%); social and economic environment (7%); health behaviors (9%); and social capital (8%). In the final model, the 19 factors explained more than a quarter of the variance in readmission rate (R2=27%).

Conclusions Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital.

ObjectivesIdentifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.MethodsOur objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients.ResultsA risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates.ConclusionsThe risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.

Importance Excess 30-day readmissions have declined substantially in hospitals initially penalized for high readmission rates under the Medicare Hospital Readmissions Reduction Program (HRRP). Although a possible explanation is that the policy incentivized penalized hospitals to improve care processes, another is regression to the mean (RTM), a statistical phenomenon that predicts entities farther from the mean in one period are likely to fall closer to the mean in subsequent (or preceding) periods owing to random chance. Objective To quantify the contribution of RTM to declining readmission rates at hospitals initially penalized under the HRRP. Design, setting, and participants This study analyzed data from Medicare Provider and Analysis Review files to assess changes in readmissions going forward and backward in time at hospitals with high and low readmission rates during the measurement window for the first year of the HRRP (fiscal year [FY] 2013) and for a measurement window that predated the FY 2013 measurement window for the HRRP among hospitals participating in the HRRP. Hospital characteristics are based on the 2012 survey by the American Hospital Association. The analysis included fee-for-service Medicare beneficiaries 65 years or older with an index hospitalization for 1 of the 3 target conditions of heart failure, acute myocardial infarction, or pneumonia or chronic obstructive pulmonary disease and who were discharged alive from February 1, 2006, through June 30, 2014, with follow-up completed by July 30, 2014. Data were analyzed from January 23, 2018, through March 29, 2019. Exposures Hospital Readmission Reduction Program penalties. Main outcome and measures The excess readmission ratio (ERR), calculated as the ratio of a hospital's readmissions to the readmissions that would be expected based on an average hospital with similar patients. Hospitals with ERRs of greater than 1.0 were penalized. Results A total of 3258 hospitals were included in the study. For the 3 target conditions, hospitals with ERRs of greater than 1.0 during the FY 2013 measurement window exhibited decreases in ERRs in the subsequent 3 years, whereas hospitals with ERRs of no greater than 1.0 exhibited increases. For example, for patients with heart failure, mean ERRs declined from 1.086 to 1.038 (-0.048; 95% CI, -0.053 to -0.043; P < .001) at hospitals with ERRs of greater than 1.0 and increased from 0.917 to 0.957 (0.040; 95% CI, 0.036-0.044; P < .001) at hospitals with ERRs of no greater than 1.0. The same results, with ERR changes of similar magnitude, were found when the analyses were repeated using an alternate measurement window that predated the HRRP and followed up hospitals for 3 years (for patients with heart failure, mean ERRs declined from 1.089 to 1.044 [-0.045; 95% CI, -0.050 to -0.040; P < .001] at hospitals with below-mean performance and increased from 0.915 to 0.948 [0.033; 95% CI, 0.029 to 0.037; P < .001] at hospitals with above-mean performance). By comparing actual changes in ERRs with expected changes due to RTM, 74.3% to 86.5% of the improvement in ERRs for penalized hospitals was explained by RTM. Conclusions and relevance Most of the decline in readmission rates in hospitals with high rates during the measurement window for the first year of the HRRP appeared to be due to RTM. These findings seem to call into question the notion of an HRRP policy effect on readmissions.

This map service depicts the percent of inpatient readmissions within 30 days of an acute hospital stay during 2008 by Hospital Referral Region (HRR) in the United States. The data source is the Centers for Medicare Medicaid Services (CMS) Chronic Conditions Warehouse (CCW). It contains 100 percent of Medicare claims for beneficiaries who are enrolled in the fee-for-service (FFS) program as well as enrollment and eligibility data. The study group consists of Medicare Fee For Service (FFS) beneficiaries age 65 and older who were enrolled in Parts A and B for the entire year or who were enrolled in Parts A and B until their death date (comprising 54% of the total Medicare population).Here is a link to the complete raw data.To learn more about the national CMS Medicare and Medicaid research data: Chronic Condition Data Warehouse_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

The hospital readmission rate PUF presents nation-wide information about inpatient hospital stays that occurred within 30 days of a previous inpatient hospital stay (readmissions) for Medicare fee-for-service beneficiaries. The readmission rate equals the number of inpatient hospital stays classified as readmissions divided by the number of index stays for a given month. Index stays include all inpatient hospital stays except those where the primary diagnosis was cancer treatment or rehabilitation. Readmissions include stays where a beneficiary was admitted as an inpatient within 30 days of the discharge date following a previous index stay, except cases where a stay is considered always planned or potentially planned. Planned readmissions include admissions for organ transplant surgery, maintenance chemotherapy-immunotherapy, and rehabilitation.

Background Predictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized. Objective We aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM). Design This cross-sectional study employed a multi-source mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach. Participants Nurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night. Main measures We performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP. Key results We collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a low-medium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home. Conclusions Our study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion.

The indicators presented measure the percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital over the period 2013/14 to 2022/23. There are 4 datasets that include breakdowns by the following geographies: region, Office for National Statistics (ONS) area classifications, NHS England regions, local authority of residence, NHS and private hospital providers, sub-Integrated Care Boards (sub-ICB) and Integrated Care Boards (ICB). Breakdowns are also published by Index of Multiple Deprivation (IMD) Quintiles. (1) Emergency readmissions to hospital within 30 days of discharge (I02040 & I00712) Also broken down by: (a) age bands: All, <16 years, 16+ years, 16-74 years; 75+ years (b) sex: male only, female only and persons. (2) Emergency readmissions to hospital within 30 days of discharge by diagnosis for all ages (I02041) Diagnoses included are: (a) Fractured proximal femur broken down by sex: male only, female only and persons (b) Stroke broken down by sex: male only, female only and persons. (3) Emergency readmissions to hospital within 30 days of discharge by procedure for all ages (I02042) Procedures included are: (a) Primary hip replacement surgery broken down by sex: male only, female only and persons (b) Hysterectomy broken down by female only. (4) Reasons for Readmission contextual indicator (I02043)

This indicator measures the percentage of admissions of people who returned to hospital as an emergency within 30 days of the last time they left hospital after a stay. Admissions for cancer and obstetrics are excluded as they may be part of the patient’s care plan. This indicator aims to measure the success of the NHS in helping people to recover effectively from illnesses or injuries. If a person does not recover well, it is more likely that they will require hospital treatment again within the 30 days following their previous admission. Thus, readmissions are widely used as an indicator of the success of healthcare in helping people to recover. There is an ongoing review by NHS Digital of Emergency Readmissions which includes indicators across the frameworks. Phase Two of the review has been on hold during the pandemic although we hope to re-start this work during 2021. During the review this indicator is designated as an experimental statistic. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Please see the methodological change paper and specification, linked below, for more details. Legacy unique identifier: P01758

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique additionally powerful database designed to support various types of analyses of national readmission charges since all payers and aforementioned uninsured. One NRD includes discharges for sufferers with plus no replay hospital visits in a year real those anyone can died in the hospital. Repeat girdle allow or may not be related. One criteria at determine the relation between hospital admissions is left to the analyst after the NRD. This database address one large gap in good customer data - the lack on nationally representative information on hospital readmissions for any ages. Outcomes of support include national readmission fare, reasons for returning to aforementioned hospital for taking, and the hospitalized charges for discharges with and out readmissions. Unweighted, the NRD contains data after approximately 30 million discharges each year. Weighted, it estimates roughness 60 gazillion discharges. Developed through a Federal-State-Industry partnership sponsored on one Agency for Healthcare Research and Quality, HCUP data inform decision making at the nationals, State, also community levels. One NRD the drawn from HCUP State Inpatient Databases (SID) including verified patient linkage numbers which can must often to track a person over dispensaries on a Assert, as adhering to strict our guidelines. The NRD is not designed for technical regional, State-, or hospital-specific readmission analyses. The NRD in more than 319 clinical and non-clinical data elements provided in adenine hospital discharge abstract. Data tree include but are not limited to: diagnoses, procedures, patient demographics (e.g., getting, age), expected source of payer, regardless of likely payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, with those billed because ‘no charge, discharge month, quarter, and date, total charges, length away stay, and data elements essential to readmission analyses. The NIS excludes data elements that would directly or indirectly id individuals. Restricted access data files are available with ampere data use agreement plus brief virtual security education.