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Dataset: American Hospital Association Annual Survey Database (AHA Annual Survey Database)

Basic Information
Dataset Full Name American Hospital Association Annual Survey Database
Dataset Acronym AHA Annual Survey Database
Summary The AHA annual survey of hospitals in the United States is administered online. The survey is comprised of questions related to the following domains: organization structure (e.g., ownership status, services provision, and national identifiers); facilities and services provided (by institutions themselves, through contracts, by other partnering facilities); total facility beds, utilization, finances, and staffing (beds and length of stay, source of payment, revenues and expenses, assets, non-reimbursed care, information technology investment, staffing information); community benefits; and management diversity. The AHA can be used independently or through linkages with other CMS and non-CMS datasets to report trends, performance, and quality indicators.
Key Terms Hospital Characteristics (Organizational Structure, Facilities and Services Provided, Total Facility Beds, Utilization, Finances, and Staffing), Community Benefits, Management Diversity
Study Design Longitudinal
Data Type(s) Administrative
Sponsoring Agency/Entity American Hospital Association (AHA)
Health Conditions/Disability Measures
Health Condition(s)


Disability Measures NA
Measures/Outcomes of Interest

Types of Hospitals from survey: General medical and surgical, Hospital unit of an institution (prison hospital, college infirmary, etc.), Hospital unit within an institution for those with intellectual disabilities

Surgical; Psychiatric; Tuberculosis and other respiratory diseases; Cancer; Heart; Obstetrics and gynecology; Eye, ear, nose and throat; Rehabilitation

Orthopedic, Chronic disease, Other specialty,

Children’s general, Children’s hospital unit of an institution, Children’s psychiatric, Children’s tuberculosis and other respiratory diseases Children’s eye, ear, nose and throat; Children’s rehabilitation; Children’s orthopedic; Children’s chronic disease; Children’s other specialty; Institution for those with intellectual disabilities

Acute long-term care, Alcoholism and other chemical dependency, Children's acute long-term care

Sample Population Hospitals (members of the American Hospital Association)
Sample Size/Notes ~6,500 hospitals
Unit of Observation Hospital & Provider
Continent(s) North America

United States

Geographic Coverage National
Geographic Specificity Zip Code
Data Collection
Data Collection Mode Administrative
Years Collected 2011-present
Data Collection Frequency Annual
Strengths and Limitations
Strengths Comprehensive information about hospitals/providers. Ideal for building nested and hierarchical models. Data can be linked with other CMS/non-CMS datasets. Good resource for health policy research-related data.
Limitations Documentation and variable information is limited. Reasons for changes in structure of a facility (provider) are undocumented. Utility as stand-alone dataset is limited.
Data Details
Primary Website
Data Access

Data Access Requirements Data Use agreement, $ Cost
Summary Tables/Reports
Data Components NA
Selected Papers
Other Papers
Technical AHA Annual Survey Database ™Documentation:

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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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