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Hospital Compare Readmissions and Deaths

This data package contains information about hospital readmission and deaths as well as hospital excess readmission reduction program. It also includes data over hospital value based purchasing program for years 2017 and 2018. It comprises of datasets about readmission rates by age, gender, patient residence, payer, zip code and median income.
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About

## Overview

This data package contains the Readmissions and Deaths by hospital of unplanned readmission to any acute care hospital within 30 days of discharge from a hospitalization for any cause related to medical conditions, including heart attack (AMI), heart failure (HF), pneumonia, chronic obstructive pulmonary disease (COPD), and stroke; and surgical procedures, including hip/knee replacement and cornary artery bypass graft (CABG). The HRRP applied to all acute care hospitals except the psychiatric, rehabilitation, pediatric, cancer, and critical access hospitals. In the first two years, only readmissions for heart attack, heart failure, and pneumonia and also were added chronic obstructive pulmonary disease (COPD) and elective hip replacement and knee replacement. The Hospital Value-Based Purchasing (VBP) Program is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. National patient readmissions within 30 days by age provides nationally representative information on hospital readmissions for all ages who were readmitted to the hospital within 30 days of discharge. The dataset on Readmission rates provides information on nationwide hospital readmissions within 30 days of discharge by gender, patient residence, payer, zip code, median income and readmissions payment adjustment factors.

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## License Information

The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://aws.amazon.com/marketplace/pp/prodview-ou2apz7asjdai?sr=0-13&ref_=beagle&applicationId=AWSMPContessa#offers) on AWS. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes.

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## Metadata

Description | Value

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Data Package Complexity | Simple

Available Enrichments | ICD 10 and HCPCS codes and descriptions added as an enrichment

Keywords | Hospital Compare, Quality-of-Care, Quality Assurance, Hospital Survey, Hospital Experience, Healthcare Associated Infections, Readmissions and Deaths, Unplanned Hospital Visits, Unplanned Readmissions Measures, Readmissions Payment Adjustments

Other Titles | Payment and Value of Care, Hospital Compare Unplanned Hospital Visits, Healthcare Associated Infection (HAI), Catheter Associated Urinary Tract Infections, Readmissions Reduction Program Factors for Medicare Advantage

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## Included Datasets

1. Hospital Excess Readmissions Reduction Program

This dataset is about Medicare payments reductions for Inpatient Prospective Payment System hospitals with excess readmissions, which started in In October 2012. Excess readmissions are measured by a ratio, by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack, heart failure, and pneumonia by the number that would be “expected,” based on an average hospital with similar patients. A ratio greater than 1 indicates excess readmissions.

2. Hospital Value Based Purchasing Program Actual Payment Adjustment

The Hospital Value-Based Purchasing (VBP) Program is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. It contains the actual payment adjustment factors under the Hospital VBP Program for FY 2019 & FY 2020.

3. Hospital Value Based Purchasing Program

The Hospital Value-Based Purchasing (VBP) Program dataset is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. It contains updated proxy adjustment factors under the Hospital VBP Program that were calculated using historical baseline and performance periods.

4. Maryland Potentially Avoidable Utilization Savings Policy 2015

This dataset includes Maryland Health Services Cost Review Commission (HSCRC) which operates a Potentially Avoidable Utilization (PAU) savings policy as part of its portfolio of value-based payment policies. The PAU savings policy is intentionally designed to assure savings to payers.

5. Maryland Readmission Reduction Incentive Program for RY 2014-2015

This dataset comprises Maryland Readmissions Reduction Incentive Program (RRIP) incentivizes hospitals to reduce avoidable readmissions by linking rewards and penalties to improvements in readmissions rates. The RRIP began to impact hospital revenue starting in Rate Year 2016, with the first performance year of Calendar Year 2014.

6. Readmissions Payment Adjustment Factors

This dataset states the factors that are to be used for Readmissions Payment Adjustments based on the FY 2017 to FY 2022 and would be used for proposed payments for FY 2017 to FY 2021 discharges, as discussed in the FY 2021 IPPS/LTCHPPS proposed rule.

7. Readmissions and Deaths by Hospital

This dataset includes provider data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.

8. Readmissions and Deaths by National

This dataset includes national-level data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.

9. Readmissions and Deaths by State

This dataset includes state-level data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.

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## Data Engineering Overview

#### We deliver high-quality data

* Each dataset goes through 3 levels of quality review

* 2 Manual reviews are done by domain experts

* Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints

* Data is normalized into one unified type system

* All dates, unites, codes, currencies look the same

* All null values are normalized to the same value

* All dataset and field names are SQL and Hive compliant

* Data and Metadata

* Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters

* Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated

* Data Updates

* Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted

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#### Our data is curated and enriched by domain experts

Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:

* Field names, descriptions, and normalized values are chosen by people who actually understand their meaning

* Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset

* Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations

* The data is always kept up to date – even when the source requires manual effort to get updates

* Support for data subscribers is provided directly by the domain experts who curated the data sets

* Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.

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## Need Help?

* If you have questions about our products, contact us at <info@johnsnowlabs.com>.

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## About Us

[John Snow Labs](https://www.johnsnowlabs.com/), an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects.

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