Electronic Health Information Exchange by Hospitals

The 5 measured items are defined as follows:

Send: Whether the hospital uses specific electronic methods to push a summary of care record to other care settings or organizations. Those methods are 1) Provider portals that allow outside organization to view records in your EHR system; 2) Interface connection between EHR systems (e.g., HL7 interface); 3) Login credentials that allow access to your EHR; 4) HISPs that enable messaging via DIRECT protocol; 4) Regional, state, or local health information exchange organization (HIE/HIO); 5) EHR vendor-based network that enables record location within the network (e.g., Care Everywhere); 6) EHR connection to national networks that enable record location across EHRs in different networks (e.g., CommonWell, eHealth Exchange, Carequality).

Receive: Whether the hospital uses specific electronic methods to receive a summary of care record from other care settings or organizations. Those methods are 1) Provider portals that allow outside organization to view records in your EHR system; 2) Interface connection between EHR systems (e.g., HL7 interface); 3) Login credentials that allow access to your EHR; 4) HISPs that enable messaging via DIRECT protocol; 5) Regional, state, or local health information exchange organization (HIE/HIO); 6) EHR vendor-based network that enables record location within the network (e.g., Care Everywhere); 7) EHR connection to national networks that enable record location across EHRs in different networks (e.g., CommonWell, eHealth Exchange, Carequality).

Find: Whether providers at your hospital query electronically for patients’ health information (e.g., medications, outside encounters) from sources outside of your organization or hospital system.

Integrate: Whether the EHR integrates summary of care record received electronically (not eFax) from providers or sources outside your hospital system/organization without the need for manual entry.

All Four Domains: Whether the hospital engages in all aspects of exchange (sends, receives, finds information) and integrates information into their electronic health record.

Data are from the American Hospital Association (AHA) Information Technology (IT) Supplement to the AHA Annual Survey. Since 2008, ONC has partnered with the AHA to measure the adoption and use of health IT in U.S. hospitals. ONC funded the AHA IT Supplement to track hospital adoption and use of EHRs and the exchange of clinical data.

The chief executive officer of each U.S. hospital was invited to participate in the survey regardless of AHA membership status. The person most knowledgeable about the hospital’s health IT (typically the chief information officer) was requested to provide the information via a mail survey or secure online site. Non-respondents received follow-up mailings and phone calls to encourage response.

This quick stat presents results from the 2017, 2018, 2019, 2020, 2022, and 2023 AHA IT Supplements. The 2017 survey was fielded from January 2018 to May 2018; the 2018 survey was fielded from January 2019 to May 2019; and the 2019 survey was fielded from January 2020 to June 2020. Due to pandemic-related delays, the 2020 survey was not fielded on time and was fielded from April 2021 to September 2021. Since the IT supplement survey instructed respondents to answer questions as of the day the survey is completed, we refer to responses to the 2020 IT supplement survey as measuring hospitals in 2021. The 2022 survey was fielded from July 2022 to December 2022, and the 2023 survey was fielded from March 2023 to August 2023. The response rate for non-federal acute care hospitals for the 2022 survey was 54 percent, and the response rate for the 2023 survey was 50 percent. A logistic regression model was used to predict the propensity of survey response as a function of hospital characteristics, including size, ownership, teaching status, system membership, and availability of a cardiac intensive care unit, urban status, and region. Hospital-level weights were derived by the inverse of the predicted propensity.