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Rehabilitation Dataset Directory: Dataset Profile

Dataset: Medicare Provider Analysis and Review (MEDPAR)

Basic Information
Dataset Full Name Medicare Provider Analysis and Review
Dataset Acronym MEDPAR

The MEDPAR file contains utilization of services and claims data for Medicare beneficiaries during their stay in Medicare-certified inpatient short-term hospitals, skilled nursing facilities, inpatient rehabilitation facilities, and long-term care hospitals. The data are available in two formats: a 5% format that contains a random selection of 5% of total Medicare beneficiaries, and a 100% sample.

These records are all from inpatient facilities (Part A) and do not have any information related to outpatient care (Part B). The claims data in MEDPAR are final after taking into account all adjustments. The dataset is useful for tracking patterns of inpatient care for patients with various medical conditions. It also contains information related to the medical and surgical procedures that patients underwent during their stays in inpatient facilities.

Key Terms Medicare, Utilization and Claims Record, Inpatient Procedure Code
Study Design Longitudinal
Data Type(s) Administrative
Sponsoring Agency/Entity

Department of Health and Human Services (HHS)

Center for Medicare and Medicaid Services (CMS)

Health Conditions/Disability Measures
Health Condition(s)

ICD-9/10 diagnostic codes

Disability Measures NA
Measures/Outcomes of Interest
Topics Medical condition, Comorbidity information, Inpatient utilization, Claims, Service charges, Surgical procedure code, Diagnosis Related Group (DRG) information
Sample Population Medicare beneficiaries (inpatient "stay" record)
Sample Size/Notes 15,000,000 (±) Medicare beneficiaries receiving inpatient care at various facilities
Unit of Observation Patient
Continent(s) North America

United States

Geographic Coverage National
Geographic Specificity Zip Code (of beneficiary’s mailing address)
Special Population(s)

Medicare beneficiaries 

Data Collection
Data Collection Mode Administrative
Years Collected 1991-present
Data Collection Frequency Annual
Strengths and Limitations
Strengths Provides a very large sample size. Contains cross-sectional and longitudinal components. Can be linked with enrollment and other clinical data. Useful for health policy research.
Limitations Clinical and intervention information is limited. Requires high computational, and data analytical capabilities. Provides a snapshot in time when records are pulled from the system, so data records may be incomplete and updated in subsequent years of data.
Data Details
Primary Website https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareFeeforSvcPartsAB/MEDPAR.html
Data Access

Research Identifiable Files (RIFs):


Limited Data Set (LDS):


Data Access Requirements Data Use agreement, $ Cost
Summary Tables/Reports https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareFeeforSvcPartsAB/MEDPAR.html
Data Components

Research Identifiable Files (RIFs)

Limited Data Set (LDS)

Selected Papers
Other Papers NA

Data Dictionary for RIFs:


Data Alerts and Layouts for LDS:


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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:

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.

For questions or comments please contact disabilitystatistics@cornell.edu