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Rehabilitation Dataset Directory: Dataset Profile


Dataset: Health care Cost & Utilization Project (HCUP): Nationwide Inpatient Sample (NIS) (HCUP-NIS)

Basic Information
Dataset full name: Health care Cost & Utilization Project (HCUP): Nationwide Inpatient Sample (NIS)
Dataset acronym HCUP-NIS
Summary The NIS is the largest all-payer database. It contains information related to inpatient stay, including both clinical and administrative (charge) information. The 2008 NIS lists all discharge data from 1,056 hospitals located in 42 States. The NIS's large sample size enables analyses of rare conditions, such as some congenital anomalies (e.g., spina bifida), and special patient populations. The data files are adjusted for severity of medical conditions using tools developed by AHRQ.
Key Terms Utilization and Cost of Hospital Services, Health Care Cost Inflation, Comparative Effectiveness Research, Access and Quality of Care
Study Design Longitudinal
Data Type(s) Administrative
Sponsoring Agency/Entity Department of Health and Human Services (HHS): Agency for Health care Research and Quality (AHRQ)
Health conditions/Disability measures
Health condition(s) Any/All
Disability Measures Any/All
Measures/outcomes of interest
Topics Primary and secondary diagnoses, Primary and secondary procedures, Admission and discharge status, Patient demographics (e.g., gender, age, race, median income for ZIP Code), Expected payment source, Total charges, Length of stay, Hospital characteristics (e.g., ownership, size, teaching status).
Sample
Sample Population Hospitals
Sample Size/Notes 8,000,000 (±) hospital inpatient stays
Unit of Observation Hospital & Patient
Geographic Coverage National* (Year 1988 has data from 8 states, while 2008 has data from 42 states)
Geographic specificity Hospital Zip Code
Data Collection
Data Collection Mode Administrative
Years Collected 1988-2008
Data Collection Frequency Annual
Strengths and limitations
Strengths Only national hospital database containing charge information on all patients, regardless of payer, and the uninsured. Data is weighted to determine national estimates. Patient severity adjustment is available. Comprehensive documentation and training available through AHRQ. Data can be linked with other datasets like American Hospital Association (AHA) survey, and Area Resource File (ARF). Trend analysis can be conducted.
Limitations Information is limited to inpatient stays. Clinical details are limited (e.g., intensity of rehabilitation intervention).
Data details
Primary Website https://www.hcup-us.ahrq.gov/nisoverview.jsp
Data Access http://www.hcup-us.ahrq.gov/tech_assist/centdist.jsp
Data Access Requirements Data Use agreement, $ Cost
Summary Tables/reports NIS Related Reports: https://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp
Dataset components (where applicable) Kids' Inpatient Database (KID)
Nationwide Emergency Department Sample (NEDS)
State Inpatient Databases (SID)
State Ambulatory Surgery Databases (SASD)
State Emergency Department Databases (SEDD)
Selected papers
Technical NIS Database Documentation: HCUP NIS Database Documentation
Other Papers



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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).

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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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