This data package contains information about Measures of Rehospitalization, Emergency Visit and Community Discharge for Medicare Beneficiaries. It also includes Nursing Home Compare information on Deficiencies, Fire Safety Deficiencies, MDS Quality Measures, Ownership information, Fines and Payment denial, Provider Information, State Averages and Survey Summary information about nursing homes.

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

Context

Every year, all U.S. hospitals that accept payments from Medicare and Medicaid must submit quality data to The Centers for Medicare and Medicaid Services (CMS). CMS' Hospital Compare program is a consumer-oriented website that provides information on "the quality of care hospitals are providing to their patients." CMS releases this quality data publicly in order to encourage hospitals to improve their quality and to help consumer make better decisions about which providers they visit.

"Hospital Compare provides data on over 4,000 Medicare-certified hospitals, including acute care hospitals, critical access hospitals (CAHs), children’s hospitals, Veterans Health Administration (VHA) Medical Centers, and hospital outpatient departments"

The Centers for Medicare & Medicaid Services (CMS) uses a five-star quality rating system to measure the experiences Medicare beneficiaries have with their health plan and health care system — the Star Rating Program. Health plans are rated on a scale of 1 to 5 stars, with 5 being the highest.

Content

• Includes the most recent Hospital General Information.csv data for each archive year found on CMS' archive site. Years: 2016-2020

| Column Name | Data Type | Description | | --- | --- | -- | | Facility ID | Char(6) | Facility Medicare ID | | Facility Name | Char(72) | Name of the facility | | Address | Char(51) | Facility street address | | City | Char(20) | Facility City | | State | Char(2) | Facility State | | ZIP Code | Num(8) | Facility ZIP Code | | County Name | Char(25) | Facility County | | Phone Number | Char(14) | Facility Phone Number | | Hospital Type | Char(34) | What type of facility is it? | | Hospital Ownership | Char(43) | What type of ownership does the facility have? | | Emergency Services | Char(3)) | Does the facility have emergency services Yes/No? | | Meets criteria for promoting interoperability of EHRs | Char(1) | Does facility meet government EHR standard Yes/No? | | Hospital overall rating | Char(13) | Hospital Overall Star Rating 1=Worst; 5=Best. Aggregate measure of all other measures | | Hospital overall rating footnote | Num(8) | | | Mortality national comparison | Char(28) | Facility overall performance on mortality measures compared to other facilities | | Mortality national comparison footnote | Num(8) | | | Safety of care national comparison | Char(28) | Facility overall performance on safety measures compared to other facilities | | Safety of care national comparison footnote | Num(8) | | | Readmission national comparison | Char(28) | Facility overall performance on readmission measures compared to other facilities | | Readmission national comparison footnote | Num(8) | | | Patient experience national comparison | Char(28) | Facility overall performance on pat. exp. measures compared to other facilities | | Patient experience national comparison footnote | Char(8) | | | Effectiveness of care national comparison | Char(28) | Facility overall performance on effect. of care measures compared to other facilities | | Effectiveness of care national comparison footnote | Char(8) | | | Timeliness of care national comparison | Char(28) | Facility overall performance on timeliness of care measures compared to other facilities | | Timeliness of care national comparison footnote| Char(8) | | | Efficient use of medical imaging national comparison | Char(28) | Facility overall performance on efficient use measures compared to other facilities | | Efficient use of medical imaging national comparison footnote | Char(8) | | | Year | Char(4) | cms data release year |

Acknowledgements

A similar dataset called Hospital General Information was previously uploaded to Kaggle. However, that dataset only includes data from one year (2017). I was inspired by this dataset to go a little further and try to add a time dimension. This dataset includes a union of Hospital General Information for the years 2016-2020. The python script used to collect and union all the datasets can be found on my [github[(https://github.com/abrambeyer/cms_hospital_general_info_file_downloader). Thanks to this dataset owner for the inspiration.

Thanks to CMS for releasing this dataset publicly to help consumers find better hospitals and make better-informed decisions.

***All Hospital Compare websites are publically accessible. As works of the U.S. government, Hospital Compare data are in the public domain and permission is not required to reuse them. An attribution to the agency as the source is appreciated. Your ...

The Physician Compare website was created by the Centers for Medicare & Medicaid Services (CMS) in December 2010 as required by the Affordable Care Act (ACA) of 2010 to help patients assess and find doctors and hospitals. This dataset contains the information supplied to patients via that website, including patient satisfaction surveys and performance scores across over 100 metrics.

Acknowledgements

This dataset was kindly released by the Centers for Medicare & Medicaid Services. You can find the original copy of the dataset here.

This data package contains information about the Ambulatory Surgical Center Quality Reporting (ASCQR) Program by facility as well as national and state level health care data. It also provides information regarding the hospital, national and state data for the Outpatient Imaging Efficiency Core Measures.

Consumer Assessment of Healthcare Providers and Systems (CAHPS) for PQRS measure performance rates reported by groups.

The dataset contains information on Physician Compare Clinician Utilization Data prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code and place of service.

Physician Quality Reporting System (PQRS) measure performance rates reported by groups.

Physician Quality Reporting System (PQRS) and non-PQRS Qualified Clinical Data Registry (QCDR) measure performance rates reported by clinicians.

Context

This dataset contains Hospital General Information from the U.S. Department of Health & Human Services. This is the BigQuery COVID-19 public dataset. This data contains a list of all hospitals that have been registered with Medicare. This list includes addresses, phone numbers, hospital types and quality of care information. The quality of care data is provided for over 4,000 Medicare-certified hospitals, including over 130 Veterans Administration (VA) medical centers, across the country. You can use this data to find hospitals and compare the quality of their care

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.cms_medicare.hospital_general_info.

Sample Query

How do the hospitals in Mountain View, CA compare to the average hospital in the US? With the hospital compare data you can quickly understand how hospitals in one geographic location compare to another location. In this example query we compare Google’s home in Mountain View, California, to the average hospital in the United States. You can also modify the query to learn how the hospitals in your city compare to the US national average.

“#standardSQL SELECT MTV_AVG_HOSPITAL_RATING, US_AVG_HOSPITAL_RATING FROM ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS MTV_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE city = 'MOUNTAIN VIEW' AND state = 'CA' AND hospital_overall_rating <> 'Not Available') MTV JOIN ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS US_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE hospital_overall_rating <> 'Not Available') ON 1 = 1”

What are the most common diseases treated at hospitals that do well in the category of patient readmissions? For hospitals that achieved “Above the national average” in the category of patient readmissions, it might be interesting to review the types of diagnoses that are treated at those inpatient facilities. While this query won’t provide the granular detail that went into the readmission calculation, it gives us a quick glimpse into the top disease related groups (DRG)
, or classification of inpatient stays that are found at those hospitals. By joining the general hospital information to the inpatient charge data, also provided by CMS, you could quickly identify DRGs that may warrant additional research. You can also modify the query to review the top diagnosis related groups for hospital metrics you might be interested in. “#standardSQL SELECT drg_definition, SUM(total_discharges) total_discharge_per_drg FROM bigquery-public-data.cms_medicare.hospital_general_info gi INNER JOIN bigquery-public-data.cms_medicare.inpatient_charges_2015 ic ON gi.provider_id = ic.provider_id WHERE readmission_national_comparison = 'Above the national average' GROUP BY drg_definition ORDER BY total_discharge_per_drg DESC LIMIT 10;”

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

The Veterans Health Administration (VHA) has now collaborated with the Centers for Medicare & Medicaid Services (CMS) to present information to consumers about the quality and safety of health care in VHA. VHA has approximately 50 percent of Veterans enrolled in the healthcare system who are eligible for Medicare and, therefore, have some choice in how and where they receive inpatient services. VHA has adopted healthcare transparency as a strategy to enhance public trust and to help Veterans make informed choices about their health care.VHA currently reports the following types of quality measures on Hospital Compare:Timely and effective care.Behavioral health.Readmissions and deaths.Patient safety.*Experience of care.

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

Data from: https://data.medicare.gov/Hospital-Compare/Payment-and-value-of-care-Hospital/c7us-v4mf More information coming soon!

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This data package contains information regarding different hospitals and their quality of surgical outcomes and structural measures. It includes datasets over facility, national and state-level data for Inpatient Psychiatric Hospital Facility Quality Reporting (IPFQR) and payment measures. It also provides Timely and Effective Care information by national and state-level data for measures of heart attack care, heart failure care, pneumonia care, surgical care and emergency department care.

This dataset contains a list of hospice agencies with data on their scores on the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey measures. It includes information about hospice agencies such as an address, phone number, CMS region data and different Centers for Medicare & Medicaid Services (CMS) Regions they belong to. This dataset also contains data regarding the corresponding scores against each of the measures for quality of patient care.

The Medicare Fee-For-Service Public Provider Enrollment dataset includes information on providers who are actively approved to bill Medicare or have completed the 855O at the time the data was pulled from the Provider Enrollment, Chain, and Ownership System (PECOS). The release of this provider enrollment data is not related to other provider information releases such as Physician Compare or Data Transparency.

Source

GitHub

Hospital Price transparency

The Centers for Medicare and Medicaid Services recently required hospitals under 45 CFR §180.50 to publish a list of prices on their websites. They specifically instruct hospitals to make these lists... - As a comprehensive machine-readable file with all items and services.
- In a display of shoppable services in a consumer-friendly format.

There is a lot of variation in adherence to these policies. Without strong guidance on formatting from CMS, it is no wonder hospitals are all over the map on formatting. Many hospitals have complied with the new rules but in ways that are not consumer friendly. 500 Megabytes of JSON data is not a strong start!

This repository cuts out pricing noise purposefully introduced by these hospital systems. You can easily search for a given CPT or HCPCS code and compare those prices across hospitals.

Supplied Data

If you don't have the proclivity to transform these data yourself with docker, there are CSV extracts available in ./volumes/data/extracts. They are broken down into four distinct groups.

• gross: this is often the top line item that the hospital never actually charges
• cash: this is the self-pay discounted price you would pay without insurance
• max: this is the maximum negotiated rate by an insurance company in the hospital network.
• min: this is the minimum negotiated rate by an insurance company in the hospital network

Ontology

We rely on the excellent work of the Athena vocabulary to define the ontology of healthcare procedures. This maps CPT and HCPCS codes into a common data model.

Coverage

Only North Carolina is covered right now because I happen to live there. Submit a PR if you have found data for other hospital systems.

Usage

Quickstart with docker-compose docker-compose up

Run the flyway migrations docker-compose run flyway

Run the ETL docker-compose run etl

Interactive PSQL client docker exec -it postgres psql -d postgres -U builder

What this is not

I sacrificed some scalabilty for the name of speed. There are some excellent examples how you could scrape your way through this to complete automation. I introduced a s manual step of downloading a file and naming it by the hospital ID. All other transformations are codified and reproducible in the container.

Contact

Submit an issue if you find anything inconsistent. Like all data products, we make assumptions and provide no warrantee.

The purpose of the project is to detect unreported Supplemental Security Income (SSI) recipient admissions to Title XIX institutions. A file containing SSN's of SSI recipients (all eligible individuals and members of eligible couples in current pay) will be matched against the Health Care Financing Administration's (HCFA) Minimum Data Set (MDS) database which contains admission, discharge, re-entry and assessment information about persons in Title XIX facilities for all 50 States and Washington, D.C. This database is updated monthly. The match will produce an output file containing MDS data pertinent to SSI eligibility on matched records. This data will be compared back to the SSR data to generate alerts to the Field Offices for their actions.

This dataset includes a list of hospice agencies with data on the quality of patient care measures shown on Hospice Compare. It includes information about hospice agencies such as address, phone number, ownership data and different Centers for Medicare & Medicaid Services (CMS) Regions they belong to. This dataset also contains data regarding the corresponding scores against each of the measures for quality of patient care.