The Skilled Nursing Facility (SNF) Cost Report dataset is a public use file that provides select measures from the skilled nursing facility annual cost report. This data includes provider information such as facility characteristics, utilization data, cost and charges by cost center (in total and for Medicare), Medicare settlement data, and financial statement data organized by CMS Certification Number.

The Skilled Nursing Facility (SNF) Cost Report dataset is a public use file that provides select measures from the skilled nursing facility annual cost report. This data includes provider information such as facility characteristics, utilization data, cost and charges by cost center (in total and for Medicare), Medicare settlement data, and financial statement data organized by CMS Certification Number.

This dataset provides information on services provided to Medicare beneficiaries residing in skilled nursing facilities. The Skilled Nursing Facility Public Use File contains information on utilization, payment (allowed amount, Medicare payment and standard payment), submitted charges, and beneficiary demographic and chronic condition indicators organized by Centers for Medicare and Medicaid Services (CMS) Certification Number, Resource Utilization Group (RUG), and state of service.

The Nursing Home COVID-19 Public File includes data reported by nursing homes to the CDC’s National Healthcare Safety Network (NHSN) Long Term Care Facility (LTCF) COVID-19 Module: Surveillance Reporting Pathways and COVID-19 Vaccinations.

On September 15, 2023, the Centers for Disease Control (CDC) updated its recommendation regarding what is required for individuals to be up to date with their COVID-19 vaccine. The COVID-19 vaccination rates now reflect the new definition of up to date. Initial findings should be interpreted with caution while providers are learning how to report COVID-19 vaccination status based on the new up to date definition. The data may initially show that few long-term care residents and staff have up to date vaccination status; these percentages will increase over time as residents and staff receive the updated COVID-19 vaccine.

BackgroundMedicare Advantage (MA) and Medicare fee-for-service (FFS) plans have different financial incentives. Medicare pays predetermined rates per beneficiary to MA plans for providing care throughout the year, while providers serving FFS patients are reimbursed per utilization event. It is unknown how these incentives affect post-acute care in skilled nursing facilities (SNFs). The objective of this study was to examine differences in rehabilitation service use, length of stay, and outcomes for patients following hip fracture between FFS and MA enrollees.Methods and findingsThis was a retrospective cohort study to examine differences in health service utilization and outcomes between FFS and MA patients in SNFs following hip fracture hospitalization during the period January 1, 2011, to June 30, 2015, and followed up until December 31, 2015. We linked the Master Beneficiary Summary File, Medicare Provider and Analysis Review data, Healthcare Effectiveness Data and Information Set data, the Minimum Data Set, and the American Community Survey. The 6 primary outcomes of interest in this study included 2 process measures and 4 patient-centered outcomes. Process measures included length of stay in the SNF and average rehabilitation therapy minutes (physical and occupational therapy) received per day. Patient-centered outcomes included 30-day hospital readmission, changes in functional status as measured by the 28-point late loss MDS-ADL scale, likelihood of becoming a long-term resident, and successful discharge to the community. Successful discharge from a SNF was defined as being discharged to the community within 100 days of SNF admission and remaining alive in the community without being institutionalized in any acute or post-acute setting for at least 30 days. We analyzed 211,296 FFS and 75,554 MA patients with hip fracture admitted directly to a SNF following an index hospitalization who had not been in a nursing facility or hospital in the preceding year. We used inverse probability of treatment weighting (IPTW) and nursing facility fixed effects regression models to compare treatments and outcomes between MA and FFS patients. MA patients were younger and less cognitively impaired upon SNF admission than FFS patients. After applying IPTW, demographic and clinical characteristics of MA patients were comparable with those of FFS patients. After adjusting for risk factors using IPTW-weighted fixed effects regression models, MA patients spent 5.1 (95% CI -5.4 to -4.8) fewer days in the SNF and received 463 (95% CI to -483.2 to -442.4) fewer minutes of total rehabilitation therapy during the first 40 days following SNF admission, i.e., 12.1 (95% CI -12.7 to -11.4) fewer minutes of rehabilitation therapy per day compared to FFS patients. In addition, MA patients had a 1.2 percentage point (95% CI -1.5 to -1.1) lower 30-day readmission rate, 0.6 percentage point (95% CI -0.8 to -0.3) lower rate of becoming a long-stay resident, and a 3.2 percentage point (95% CI 2.7 to 3.7) higher rate of successful discharge to the community compared to FFS patients. The major limitation of this study was that we only adjusted for observed differences to address selection bias between FFS and MA patients with hip fracture. Therefore, results may not be generalizable to other conditions requiring extensive rehabilitation.ConclusionsCompared to FFS patients, MA patients had a shorter course of rehabilitation but were more likely to be discharged to the community successfully and were less likely to experience a 30-day hospital readmission. Longer lengths of stay may not translate into better outcomes in the case of hip fracture patients in SNFs.

This data set contains the Skilled Nursing Facility PUF information on utilization, payment (allowed amount, Medicare payment and standard payment), and submitted charges organized by CMS Certification Number (6-digit provider identification number), Resource Utilization Group (RUG), and state of service. This PUF is based on information from CMS’s Chronic Conditions Data Warehouse (CCW) data files. The data in the Skilled Nursing Facility PUF covers calendar year 2013 and contains 100% final-action (i.e., all claim adjustments have been resolved) skilled nursing facility institutional claims for the Medicare fee-for-service (FFS) population.

Analysis of ‘Medicare Post-Acute Care & Hospice - by Provider and Service’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7cb60ef5-e2d5-400d-a74d-9ec779d9d4d6 on 11 February 2022.

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

The Medicare Post-Acute Care (PAC) & Hospice by Provider and Service dataset contains information on services provided to Medicare beneficiaries by home health agencies, hospices, skilled nursing facilities (SNFs), inpatient rehabilitation facilities (IRFs), and long-term care hospitals. It includes information on demographic and clinical characteristics of beneficiaries served, professional and paraprofessional service utilization, submitted charges, and payments at the provider, state, and national levels. There are three additional reports which include information at the Home Health Resource Group, Resource Utilization Group, and Case-Mix Group levels for home health agencies, SNFs and IRFs.

Please note:

The data included may not be representative of a physician’s entire practice, as the file only includes information on Medicare fee-for-service beneficiaries. The data are also not intended to indicate the quality of care provided and are not risk-adjusted to account for differences in underlying severity of disease of patient populations.

For the 2019 Medicare Post-Acute Care & Hospice by Provider and Service datasets only, we have modified the data for SNFs presented. Specifically, SNF data are reported in a separate file and only cover the period between January 1, 2019 and September 30, 2019. This dataset can be found as a downloadable report under ‘Resources.’ More information can be found in the Methodology and FAQ documents.

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

This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.

The Skilled Nursing Facility Utilization and Payment Public Use File (Skilled Nursing Facility PUF) provides information on services provided to Medicare beneficiaries residing in skilled nursing facilities. The Skilled Nursing Facility PUF contains information on utilization, payment (allowed amount, Medicare payment and standard payment), submitted charges and beneficiary demographic and chronic condition indicators organized by CMS Certification Number (6-digit provider identification number), Resource Utilization Group (RUG), and state of service.

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

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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

...

NCHS has linked data from various surveys with Medicare program enrollment and health care utilization and expenditure data from the Centers for Medicare & Medicaid Services (CMS). Linkage of the NCHS survey participants with the CMS Medicare data provides the opportunity to study changes in health status, health care utilization and costs, and prescription drug use among Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older, certain younger people with disabilities, and people with End-Stage Renal Disease.

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. The Institutional Population Component (IPC) is a survey of nursing and personal care homes and facilities for the mentally retarded and residents admitted to those facilities. Information was collected on facilities and their residents at several points during 1987. Use and expenditure estimates for institutionalized persons can be combined with those from the Household Component for composite estimates covering most of the civilian population. Information on facilities and residents was collected from facility administrators and caregivers, with additional information collected from next-of-kin or other knowledgeable respondents. These data were supplemented by Medicare claims information for covered sample persons. Research File 36 provides information from the Medicare Automated Data Retrieval System (MADRS) for a subset of persons from File 1 of NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: INSTITUTIONAL POPULATION COMPONENT, FACILITY USE AND EXPENDITURE DATA FOR NURSING AND PERSONAL CARE HOME RESIDENTS PUBLIC USE TAPE 17 and a subset of persons from File 1 of NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: INSTITUTIONAL POPULATION COMPONENT, FACILITY USE AND EXPENDITURE DATA FOR RESIDENTS OF FACILITIES FOR PERSONS WITH MENTAL RETARDATION RESEARCH FILE 22R. Six data files are provided for Research File 36R, all of which contain demographic data such as age, sex, and race. Other variables common to all parts are facility type, person number, sample person identifier, reimbursement amount by Medicare, and total charges reported by provider. Parts 1-6 cover, respectively, Part B Payment Records, Part B Outpatient Bill Records, Part B Home Health Bill Records, Part A Inpatient/Skilled Nursing Facilities Bill Records, Part A Home Health Bill Records, and Part A Hospice Bill Records.

BackgroundHealth care is believed to be suffered from a “cost disease,” in which a heavy reliance on labor limits opportunities for efficiencies stemming from technological improvement. Although recent evidence shows that U.S. hospitals have experienced a positive trend of productivity growth, skilled nursing facilities are relatively “low-tech” compared to hospitals, leading some to worry that productivity at skilled nursing facilities will lag behind the rest of the economy.ObjectiveTo assess productivity growth among skilled nursing facilities (SNFs) in the treatment of conditions which frequently involve substantial post-acute care after hospital discharge.MethodsWe constructed an analytic file with the records of Medicare beneficiaries that were discharged from acute-care hospitals to SNFs with stroke, hip fracture, or lower extremity joint replacement (LEJR) between 2006 and 2014. We populated each record for 90 days starting at the time of SNF admission, detailing for each day the treatment site and all associated costs. We used ordinary least square regression to estimate growth in SNF productivity, measured by the ratio of “high-quality SNF stays” to total treatment costs. The primary definition of a high-quality stay was a stay that ended with the return of the patient to the community within 90 days after SNF admission. We controlled for patient demographics and comorbidities in the regression analyses.ResultsOur sample included 1,076,066 patient stays at 14,394 SNFs with LEJR, 315,546 patient stays at 14,154 SNFs with stroke, and 739,608 patient stays at 14,588 SNFs with hip fracture. SNFs improved their productivity in the treatment of patients with LEJR, stroke, and hip fracture by 1.1%, 2.2%, and 2.0% per year, respectively. That pattern was robust to a number of alternative specifications. Regressions on year dummies showed that the productivity first decreased and then increased, with a lowest point in 2011. Over the study period, quality continued to rise, but dominated by higher costs at first. Costs then started to decrease, driving productivity to grow.ConclusionThere has been substantial productivity growth in recent years among SNFs in the U.S. in the treatment of post-acute-care-intensive conditions.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. This file lists the bed types and bed type capacities that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility beds file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. ELMS records healthcare service provider applications, issues licenses, generates license renewal notices, determines license fees, issues and tracks State enforcement actions, and generates management reports. This file lists the services that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility services file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

The data in these files were retrieved from the Nursing Home Compare (NHC) data repository, https://data.medicare.gov/data/nursing-home-compare, on April 26, 2019. The data were compiled from the NHC files ProviderInfo_Download.csv, QualityMsrMDS_Download.csv and QualityMsrClaims_Download.csv.

Abstract (en): This data collection contains two data files derived from information gathered in the initial screening interview and Rounds 1-4 of the Household Survey component of the 1987 NATIONAL MEDICAL EXPENDITURE SURVEY (NMES). The Person File supplies data on each sampled person who reported coverage by Medicare at any time in 1987 and who responded to all rounds of the Household Survey for which he or she was eligible to respond. Data in this file include age, sex, race, marital status, education, employment status, personal and family income, coverage under private health insurance and public programs such as Medicaid and CAMPUS/CAMPVA, and the total number and cost of all prescriptions purchased in 1987 while under Medicare coverage. In addition, there are indicators of general health and specific medical conditions: stroke, cancer, heart disease, gallbladder disease, high blood pressure, hardening of the arteries, rheumatism, emphysema, arthritis and diabetes. The Prescribed Medicines Event File presents data pertaining to every instance a prescribed medicine was purchased or otherwise obtained by these Medicare beneficiaries during 1987. For respondents who were covered by Medicare for part of the year, only prescribed medicines acquired during the Medicare coverage period are included. This file gives the trade and generic name of each prescribed medication and reports the cost of the prescription and the medical condition for which it was prescribed. Civilian noninstitutionalized population of the United States living in housing units, group quarters, and other noninstitutional (nongroup) quarters. Stratified multistage area probability sample of dwelling units. Dwelling units including blacks, Hispanics, the elderly, the functionally impaired, and the poor were oversampled. 2006-03-30 File CB9340.SUPP.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads. (1) The principal investigator was formerly known as the National Center for Health Services Research and Health Care Technology Assessment. (2) The age distribution for Part 1: 17 and under (N=8), 18-63 (N=444), 64 (N=246), 65-74 (N=3,246), 75-84 (N=1,685), 85+ (N=409). (3) Parts 1 and 2 are linked by common identification variables. (4) Hard copy supplementary materials to the machine-readable documentation in Part 3 are supplied for this collection. (5) Part 2 contains alphabetic variables. (6) NMES consists of several surveys including two household panel surveys: the Household Survey and the Survey of American Indians and Alaska Natives (SAIAN). The Household Survey, from which this data collection is derived, surveyed the United States noninstitutionalized population and was fielded over four rounds of personal and telephone interviews at four-month intervals, with a short telephone interview constituting the fifth final round. SAIAN, which was conducted over three rounds of personal interviews, surveyed all persons who were eligible for care through the Indian Health Service and were living on or near reservations. These household surveys were supplemented by additional surveys, most important of which are the Health Insurance Plans Survey of employers and insurers of consenting household survey respondents, and the Medical Provider Survey of physicians, osteopaths, and inpatient and outpatient facilities, including home health care agencies reported as providing services to any member of the noninstitutionalized population sample. NMES also surveyed persons resident in or admitted to long-term care facilities (nursing homes and facilities for the mentally retarded) at any time in 1987. Information on these individuals was obtained from the Survey of Institutions, which collected data from facility administrators and designated staff, and the Survey of Next-of-Kin, which collected data from the respondent's next-of-kin or other knowledgeable persons. Together, the major components of NMES provide measures of health status and estimates of insurance coverage and the use of services, expenditures, and sources of payment for the period from January 1 to December 31, 1987 for the civilian population of the United States. NMES continues a series of national health care expenditure surveys carried out in the past, particularly the 1980 National Medical Care Utiliza...

NOTE: Layer is depreciated because an updated layer is available. It can be found here: https://nmcdc.maps.arcgis.com/home/item.html?id=56213bc129004746a0cf7323c65243f5SOURCE - STANFORD OPEN DATA PROJECT - https://biglocalnews.org/#READMEIncluded here are files for hospital level data, nursing home data and Census populationestimates at the county level. The data was gathered and processed by Jacob Fenton withPublicAccountibility.org in collaboration with Big Local News. Assistance provided by ErinPetenko with VTDigger . More information on data processing and source files can be foundhere: https://github.com/jsfenfen/covid_hospitals_demographics/blob/master/README.mdThis project provides and joins datasets pertinent to the COVID-19 pandemic: CMS hospitallocation and number of beds by type, county-level population estimates by age, which can belinked to CMS (Centers for Medicare and Medicaid Services) hospital data, and nursing homelocation and capacity.DATA FILEShospital_data.csv - Hospital-level bed data - This file has basic hospital information and bedcounts with CMS. Data come from the most recently filed Medicare hospital cost report receivedin 2017 or later. Please note, cost reports are self-reported by the hospitals and could containerrors and omissions. The facilities that are included in the data file are short-term acute-carehospitals, critical access hospitals and children's hospitals. Military hospitals with an id ending inF and some children’s hospitals are missing bed counts. Psychiatric hospitals or rehabilitationfacilities are not included. Recently opened facilities that have not filed CMS reports yet alsoshow zero bed counts.Key data fields for bed counts in hospital_data.csv:● acute_beds - number of general adult/pediatric acute-care beds● icu_beds - number of general purpose intensive care beds● coronary_beds - number of coronary care beds● burn_beds - number of burn ICU beds● surg_icu_beds - number of surgical ICU beds● oth_spec_beds - other specialty care beds (can include neonatal beds)● subtotal_acute_beds - acute care beds, intensive care beds and other specialty beds.● all_beds - total beds hospital wide, including inpatient rehab, hospice, etc.hosp_geo.zip - Shapefile of all hospitals in the hospital_data.csv above. All of the data columnsfrom the csv can be found in this file, so you do not need to join these together. The shapefileleaves out one hospital in Puerto Rico. Includes FIPS codes for county and CBSA (which isessentially metro area).