FIND Disability Statistics
American Community Survey (ACS)
- Employment Rate
- Not Working but Actively Looking for Work
- Full-Time / Full-Year Employment
- Annual Earnings
- Annual Household Income
- Supplemental Security Income (SSI)
- Educational Attainment
- Veterans Service-Connected Disability
- Health Insurance Coverage (and Type)
Current Population Survey (CPS)
EEOC Charge Data
Rehabilitation Dataset Directory: Dataset Profile
Dataset: Inpatient Rehabilitation Facility Compare (IRF Compare)
|Dataset Full Name
|Inpatient Rehabilitation Facility Compare
|The Inpatient Rehabilitation Facility Compare (IRF Compare) is a facility level database developed and monitored by the Centers for Medicare and Medicaid Services (CMS). Its purpose is to provide quality of care measures for inpatient rehabilitation facilities. Data are collected from Medicare claims and Inpatient Rehabilitation Facility-Patient Assessment Instrument (IRF-PAI) file as well as the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) tool.
Note that patient level data are not available. All data are aggregated at the national, state and specific Inpatient Rehabilitation Facility level.
|Inpatient Rehabilitation Facility, Healthcare-acquired infections, Functional Outcomes, Unplanned Hospital Readmission, Quality of Care Outcomes
|Centers for Medicare and Medicaid Services (CMS), Department of Health and Human Services (HHS)
|Health Conditions/Disability Measures
|Measures/Outcomes of Interest
|State and regional variations in IRF quality outcomes.
Quality outcomes, healthcare associated conditions and infections, unplanned readmissions.
Hospital readmission, urinary tract infection, pressure ulcers
Change in functional outcomes, mobility and self-care and incidence of falls*
*These outcomes are currently being collected but are not yet available (February 2017)
Medicare participating Inpatient Rehabilitating Facilities nationally.
|1,238 (December 2016) Medicare participating IRFs.
|Unit of Observation
|Individual Inpatient Rehabilitation Facilities
|Data Collection Mode
|Administrative claims data
National Healthcare Safety Network (NHSN) tracking system
|January 2015 - present
|Data Collection Frequency
Annually (hospital readmission rates only)
|Strengths and Limitations
|Data ideal for performing research on regional and state variation on quality outcomes.
Data can be linked with other Medicare and CMS datasets.
Potential for longitudinal analysis as additional time periods are incorporated.
|Patient-level data not available.
Data based on Medicare participating agencies only, not necessarily representative of all inpatient rehabilitation facilities.
As of February 2017 several quality outcomes not yet available, therefore research use is currently limited.
|Data Access Requirements
|Public Use Dataset
The IRF-Compare data is comprised of 4 csv files:
IRF Quality Public Reporting (CMS):
|IRF Quality Reporting Measures Information:
IRF-PAI and IRF QRP Manual:
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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).
Other CLDR supported resources and collaborative opportunities:
- Archive of Data on Disability to Enable Policy and research (ADDEP)
- Data Sharing & Archiving at CLDR
- Pilot Project Program
- Visiting Scholars Program
Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.
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