Introduction
Purveyor: Annual Hospital Association
Years in the DataCore: 2008-2017
Years of data owned: 2008-2017
Unit of data: Hospital (AHAID)
Dataset website: http://www.ahadata.com/aha-healthcare-database
Purveyor website: https://www.aha.org/front
General description: The American Hospital Association (AHA) has conducted the AHA IT Supplemental Survey since 2008. The AHA IT Supplemental Survey provides information about electronic medical records, interoperability, health information exchange barriers, reporting, and degree of electronic transition for over 3,500 hospitals.
The AHA Annual Survey Information Technology Supplement (AHAIT) is a collaboration between the AHA and the Office of the National Coordinator (ONC). AHAIT investigates questions of:
- Inventory of the hospital’s computerized system capabilities
- Patient engagement
- Provider burden
- Querying information from outside providers or sources
- Interoperability barriers
- EHR system and IT vendors
Common Key Linking Variables
The AHAIT dataset is structured as a single table by year. The primary key of this table is the AHAID, which can be used to uniquely identify a hospital and link to AHAAS.
- AHA Identification Number
- Medicare Provider Number (CMS Certification Number, or CCN)
- Hospital Name
- Street Address
Licensing and Access
Access to the AHAIT dataset comes with access to the AHA Annual Survey dataset.
DataCore Staff Errata
5/25/2019:
Why is 2011 data absent?
It isn't that 2011 doesn't exist; it's more than in 2011 AHA changed how it named the surveys. For example, the 2008 survey was filled out in 2009; however, it was a supplement to the 2008 annual survey. In 2011, this was changed by skipping a year. So, for example, the 2017 survey was filled out in 2017 but was a supplement to the 2016 annual survey. In AHA's current year calculation, the 2010, 2009, and 2008 surveys would be equivalent to 2011, 2010, and 2009 respectively.
Data exceptions and corrections
Changes made to the raw dataset:
- In the file 2010 AHA Annual IT supplement Database, change cell BX2 from A4_B4 to Q4_B4
- In the file 2012 AHA Annual IT supplement Database, change cell AQ2 from v0 to Q2_B
- In the file 2016 AHA Annual IT supplement Database, some cells contained a single space character rather than empty cells. These cells were treated as empty cells.
Decisions made in mapping variables:
- In 2017, the degree of implementation question changed from a six-point scale to a three-point scale. In the merged dataset, we are using the three-point scale because that is the new scale going forward. In this new scale, fully implemented across all units maps to fully implemented across all units; fully implemented in at least one unit and beginning to implement in at least one unit map to partly implemented; and have the resources to implement in the next year, do not have the resources but considering implementing, and not in place and not considering implementing map to not implemented. [1=1, 2=2|3, 3=4|5|6]
DataCore Purveyor Errata
Data Updates
5/25/2019: No data errata, data exceptions or data corrections have been issued.
Provenance
The data from AHA is sent in the form of Excel spreadsheets. The first two rows of these spreadsheets contain the question name for the year of the question and a "database" name that links questions across multiple years. For this harmonization, we used the database name to link questions from year to year.
For the code used for these processes, email datacore@osumc.edu.
Step 1: Convert the Excel spreadsheet files into tab separated value files (.tsv)
Step 2: Create tables to hold these raw data in SQL. Also create a table containing all variables for all years.
Step 3: Load the raw data into SQL. We used a bulk copy program (BCP) utility to load these data into SQL.
Step 4: Union all of the annualized data into the table, which contains variables for all years.
Step 5: From the dictionary, we created a table that contains all pertinent information about all questions, and another table that contains all pertinent information about all category lists. These metadata tables allow us to load these data into other datasets.