HCUP SASD - Healthcare Cost and Utilization Project, State Ambulatory Surgery Database

General description

1. Database primary purpose - Include data for ambulatory surgery and other outpatient services from hospital-owned facilities. Some States also provide ambulatory surgery and outpatient services from non-hospital-owned facilities.
2. Overall data type - Health outcomes
3. Dataset type - Cross-sectional
4. Data source - Claims
5. Data level - Patient level
6. Geographic location of the data collection sites - California, Colorado, Washington D.C., Florida, Georgia, Hawaii, Iowa, Kansas, Kentucky, Maryland, Maine, Michigan, Minnesota, Missouri, North Carolina, Nebraska, New Jersey, Nevada, New York, Oregon, South Carolina, South Dakota, Utah, Vermont, Wisconsin
7. Sponsor, manager, or home institution - Agency for Healthcare Research and Quality's (AHRQ)
8. Date range - within onetomap (see "others" section): Florida 2009-2019, Maryland 2009-2019, and New York 2009-2016
9. Geolocation data - State postal code, urban/rural code, state/county FIPS code, ZIP code
10. Dates - Admission weekday, month, and year; discharge month and quarter; days to event
11. Hospital identifiers - State-specific hospital identifier and the National Provider Identifier (NPI)
12. Physicians identifiers - Provides de-identified physician identifiers, which can be used to distinguish between physicians. If the original physician identifier is based on a state license number or Universal Physician Identification Number (UPIN), then Physician number can be used to track a physician across hospitals. If the original physician identifier is based on hospital-specific identifiers, then it can only be used to track physicians within a hospital.
13. NPI - For facilities
14. Longitudinal tracking - Track patients within hospitals (medical record number), track providers (physician number)
15. Financial variables - Expected payer and total charges
16. Clinical areas of interest - Surgery
17. Variables that are uniquely present in this dataset - Provide a uniform format that facilitates cross-State comparisons. Also, SASD are well suited for research that requires a complete enumeration of hospital-based ambulatory surgeries within geographic areas or States.
18. Database caveats and limitations - Not all data elements are available from every state, and not in all the years.
19. Other - The complete years and states available are: California: 2007 - 2011; 2018 - 2020, Colorado: 2014 - 2019, Washington D.C.: 2016 - 2019, Florida: 2005 - 2019, Georgia: 2014 - 2019, Hawaii: 2014 - 2016, Iowa: 2014 - 2020, Kansas: 2014 - 2019, Kentucky: 2014 - 2020, Maryland: 2009 - 2020, Maine: 2014 - 2018, Michigan: 2014 - 2019, Minnesota: 2014 - 2020, Missouri: 2017 - 2019, North Carolina: 2014 - 2019, Nebraska: 2014 - 2019, New Jersey: 2014 - 2019, Nevada: 2014 - 2019, New York: 2009 - 2018, Oregon: 2014 - 2020, South Carolina: 2014 - 2018, South Dakota: 2017 - 2019, Utah: 2014 - 2018, Vermont: 2014 - 2019, Wisconsin: 2014 - 2020

Applicable methods

1. Association analysis, such as difference-in-differences regression (1), linear, logistic, and Poisson regression (2, 3), ANOVA (4)
2. Descriptive analysis (5)
3. Machine learning (6, 7, 8, 9)
4. Propensity score (10, 11)
5. Time-to-event (12)

High-impact designs

• Evaluate trends relating to procedures (13)
• Describe demographics and associations of socioeconomic characteristics (14)
• Evaluate the relationship between ownership and practice patterns (15)
• Assess revisit rates and factors (16)

Data dictionary

To access HCUP SASD data dictionary, click here

Variable categories

1. Patient demographics (e.g., sex, age, race, ethnicity, language)
2. Hospital discharge records (e.g., dates of admission and discharge, LOS,
3. Charges (expected payer, total charges)
4. Injury information (i.e., type and intent)
5. All-listed diagnoses and procedures codes

Linkage to other datasets

• Linkages can be established through Hospital identifiers to inpatient hospital databases, such as the AHRQ-sponsored State Inpatient Databases (SID) and the American Hospital Association Annual Survey File (AHA)

• SASD can also be linked to social determinants of health data using patient ZIP codes (e.g., Distressed Communities Index Data)