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.

The 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.

Characteristics and comorbidities of patients discharged alive after an index hospitalization for pneumonia between 2013–14 in the National Readmissions Database sample, overall and by age- group.

Abstract

Inpatient discharge data linked within states within a given year.

Usage

IRB approvals and Researchers will need a DUA. There is no cost to reuse.

Collaboration Notes

I am open to new collaborations, but I am not open to supporting a doctoral student.

Start and End Dates of Data

2010-2020

All numbers are percentages with SE in parenthesis unless specified otherwise.

IntroductionCardiac sarcoidosis (CS) is an increasingly recognized cause of cardiac disease. Because the clinical presentation of CS is non-specific, the diagnosis is often delayed. Early detection is essential to initiate treatments that reduce the risk of heart failure (HF) and arrhythmic death. We therefore aimed to describe the features of CS hospitalizations during which the initial diagnosis of CS is made.MethodsWe performed a retrospective analysis of hospitalizations from 2016 to 2019 in the Nationwide Readmission Database (NRD). Hospitalizations with a primary diagnosis suggestive of CS (HF/cardiomyopathy, cardiac arrest, arrhythmias, or heart block) were categorized into cases with and without CS as a secondary diagnosis (CS+ and CS−, respectively). One-to-one propensity score matching (PSM) was performed.ResultsThe CS+ cohort comprised 1,146 hospitalizations and the CS− cohort 3,250,696 hospitalizations. The CS+ cohort included patients who were younger and more often male. PSM resulted in highly matched cohorts (absolute standardized mean difference

Percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission: indirectly standardised by age, sex, method of admission and diagnosis/procedure. The indicator is broken down into the following demographic groups for reporting: ● All years and female only, male only and both male and female (persons). ● <16 years and female only, male only and both male and female (persons). ● 16+ years and female only, male only and both male and female (persons) ● 16-74 years and female only, male only and both male and female (persons) ● 75+ years and female only, male only and both male and female (persons) Results for each of these groups are also split by the following geographical and demographic breakdowns: ● Local authority of residence. ● Region. ● Area classification. ● NHS and private providers. ● NHS England regions. ● Deprivation (Index of Multiple Deprivation (IMD) Quintiles, 2019). ● Sustainability and Transformation Partnerships (STP) & Integrated Care Boards (ICB) from 2016/17. ● Clinical Commissioning Groups (CCG) & sub-Integrated Care Boards (sub-ICB). All annual trends are indirectly standardised against 2013/14.

BackgroundMedical management is recommended for uncomplicated type B aortic dissection (TBAD). However, data focused on sex differences in outcomes in TBAD patients managed medically are limited.MethodsHospitalizations of adults with TBAD were identified using the 2016–2019 Nationwide Readmissions Database. TBAD diagnosis was deduced by inclusion of thoracic or thoracoabdominal aorta dissection and exclusion of presumed type A aortic dissection. Hospitalizations associated with intervention were excluded. Multivariable logistic regression modeling was used to investigate the association of sex with in-hospital mortality. A Cox proportional hazards model was used to assess the association between sex and readmission rates.ResultsThere were 52,269 TBAD hospitalizations (58% male). Compared to men, women were older (72 vs. 65 years), had higher in-hospital mortality (11.5% vs. 8.5%), shorter median length of stay (3.95 vs. 4.23 days), and lower rates of elective admissions (6.4% vs. 8.2%) (all p 

HCUPnet is an on-line query system that provides free, instant access to the largest set of all-payer health care databases that are publicly available. Using HCUPnet's easy step-by-step query system, you can generate tables and graphs on statistics and trends for acute care hospitals in the U.S. HCUPnet provides:  National and regional estimates for inpatient stays and emergency department visits;  State counts of inpatient stays and emergency department visits for those states that agreed to participate;  National estimates on readmissions and readmission rates;  County-level statistics on hospital use and potentially preventable admissions, based on the AHRQ Quality Indicators (QIs)* For most queries, detailed information is available for conditions and procedures (by ICD-9-CM codes and Clinical Classification Software), and for diagnosis related groups (DRGs). HCUPnet allows easy access to information from datasets that are part of the Healthcare Cost and Utilization Project (HCUP); details on obtaining these datasets are also available in www.healthdata.gov

A recent study found that approximately 1 in every 6 patients hospitalized for the 1st episode of syncope had an underlying pulmonary embolism (PE). As current guidelines do not strongly emphasize evaluation for PE in the workup of syncope, we hypothesize that there might be a higher rate of 30-day readmission due to untreated venous thromboembolism (VTE). The objective of this study is to measure the 30-day readmission rate due to VTE and identify predictors of 30-day readmission with VTE among syncope patients. We identified patients admitted with syncope with ICD9 diagnoses code 780.2 in the Nationwide Readmission Database (NRD-2013), Healthcare Cost and Utilization Project (HCUP). The 30-day readmission rate was calculated using methods described by HCUP. Logistic-regression was used to identify predictors of 30-day readmission with VTE. Discharge weights provided by HCUP were used to generate national estimates. In 2013, NRD included 207,339 eligible patients admitted with syncope. The prevalence rates of PE and DVT were 1.1% and 1.4%, respectively. At least one syncope associated condition was present in 60.9% of the patients. Among the patients who were not diagnosed with VTE during index admission for syncope (N = 188,015), 30-day readmission rate with VTE was 0.5% (0.2% with PE and 0.4% with DVT). In conclusion, low prevalence of VTE in patients with syncope and extremely low 30-day readmission rate with VTE argues against missed diagnoses of VTE in index admission for syncope. These results warrant further studies to determine clinical impact of work up for PE in syncope patients without risk factors.

Most statistically significant differences in readmitted and non-readmitted patients.

Data source: Declaration data of medical service points of insurance medical service organizationNumerator: Number of non-planned readmission cases within 14 days after discharge from denominator cases.Denominator: Number of inpatient cases for childbirth at the same hospital in the same season.Calculation formula: (Numerator / Denominator) x 100%

● Region.

Studies with two patient populations collapsed into one. CI = 95% Confidence Interval.*Statistically significant difference present between all single hospital, multicenter, and national database, p-value = 0.0198.Thirty-Day Readmission Rates Stratified by Data Source.

This indicator compares the odds of an emergency readmission within seven days for patients discharged on a particular day of the week to the odds of an emergency readmission within seven days for patients discharged on a Wednesday. Discharges with both emergency and non-emergency admission methods are included in the indicator. Results including only discharges where the patient was admitted in an emergency are also presented as contextual information. The results are presented as odds ratios, alongside the number of discharges, emergency readmissions and the crude readmission rate for each trust. The number of emergency readmissions is broken down into those where the readmission was to the same provider that the patient was discharged from and those where the readmission was to a different provider. From April 2020, the Department of Health and Social Care (DHSC) is no longer commissioning NHS Digital to produce these indicators. Therefore, no further publications in this series are planned. Notes: 1. There is a shortfall in the number of records for Tameside and Glossop Integrated Care NHS Foundation Trust (trust code RMP) and University College London Hospitals NHS Foundation Trust (trust code RRV) meaning that results for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. From this publication onwards, the adjustment for deprivation uses the 2019 version of the Index of Multiple Deprivation (IMD). Previous releases of these indicators used the 2015 version. Further information is available in the statement of methodological changes (see Resources). 3. The following mergers took place on 1st October 2019: Cumbria Partnership NHS Foundation Trust (trust code RNN) merged with North Cumbria University Hospitals NHS Trust (trust code RNL). The new trust is called North Cumbria Integrated Care NHS Foundation Trust (trust code RNN). Aintree University Hospital NHS Foundation Trust (trust code REM) merged with Royal Liverpool and Broadgreen University Hospitals NHS Trust (trust code RQ6). The new trust is called Liverpool University Hospitals NHS Foundation Trust (trust code REM). Results are presented to reflect the updated organisational structure from this publication onwards. 4. Further information on data quality can be found in the Seven-day Services background quality report, which can be downloaded from the ‘Resources’ section of the publication page. Further guidance on the interpretation of the indicators is also available to download from that page.

Data source: Declaration data of medical service points by insurance medical service institutionsDenominator: Number of discharged cases of inpatient DRGs cases.Numerator: Number of cases that are readmitted within 0 to 14 days after discharge from the denominator.Calculation formula: (numerator / denominator) x 100%

Data source: Declaration data of medical service points of insurance medical service organizationsNumerator: Number of cases in which the same person, same day, same disease (first three digits of the main diagnosis are the same), and same medical institution have sought medical treatment 2 times or more.Denominator: Outpatient visits of the same person, same day, and same medical institution.Calculation formula: (Numerator/Denominator) x 100%

Analysis of ‘Hospital ratings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/center-for-medicare-and-medicaid/hospital-ratings on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This are the official datasets used on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at over 4,000 Medicare-certified hospitals across the country.

Content

Dataset fields:

• Provider ID
• Hospital Name
• Address
• City
• State
• ZIP Code
• County Name
• Phone Number
• Hospital Type
• Hospital Ownership
• Emergency Services
• Meets criteria for meaningful use of EHRs
• Hospital overall rating
• Hospital overall rating footnote
• Mortality national comparison
• Mortality national comparison footnote
• Safety of care national comparison
• Safety of care national comparison footnote
• Readmission national comparison
• Readmission national comparison footnote
• Patient experience national comparison
• Patient experience national comparison footnote
• Effectiveness of care national comparison
• Effectiveness of care national comparison footnote
• Timeliness of care national comparison
• Timeliness of care national comparison footnote
• Efficient use of medical imaging national comparison
• Efficient use of medical imaging national comparison

Acknowledgements

Dataset was downloaded from [https://data.medicare.gov/data/hospital-compare]

Inspiration

If you just broke your leg, you might need to use this dataset to find the best Hospital to get that fixed!

--- Original source retains full ownership of the source dataset ---

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

Introduction: Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC's Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC's Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.

Methods: A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1^st of January 2018 and the 31^st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X² goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.

Results: At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676-0.708), 0.703 (95% CI 0.687-0.719) and 0.705 (95% CI 0.690-0.720). The Hosmer-Lemeshow X² tests had values of p<0.001.

Conclusion: In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability - model updating is warranted.