STATUS: Completed Project
Electronic Health Records (EHRs) have the potential to provide useful information for patient-centered outcomes research (PCOR) by aggregating patient data across disparate systems. Many PCOR projects rely on patients’ clinical data collected in site or system specific EHRs. While other ONC activities are focused on standardizing the way data is collected within EHRs, researchers also need standardized ways to access that data - building on near widespread adoption of EHRs utilizing standard data and vocabulary requirements, as specified by Meaningful Use and the Medicare Access and CHIP Reauthorization Act (MACRA). This project sought to make it easier to get data out of an EHR in a consistent and reproducible way; this was acritical next step to enable and simplify data aggregation across widely distributed EHR systems (i.e., distributed population queries). To accomplish this goal, ONC developed an application programming interface (API) that “connects” to a provider’s EHR to extract data in a standard way. An API is a technology that allows one software program to access the services provided by another software program.
PROJECT PURPOSE & GOALS
The goal of this project was to develop technical standards for how health care providers, researchers, and the public health community access and extract data from EHRs in order to conduct PCOR. The project had three phases, each building on the capacity developed in a prior phase. The project objectives acted as phases, under the Data Access Framework (DAF) Initiative.
Local Access API Initiative: Develop an API that allows providers to access data in their own EHR in a standardized way. The API focuses on a standard way to extract patient, practice, National Death Index (NDI) database and through linking NDI records with nationally collected hospital and epidemiologic studies to determine both fact and outcomes data from the EHR. ONC developed two use cases to develop a standard way to query an EHR and a standard format for how that data is returned.
Secure Enterprise Access Initiative: Using the local access data standards, add a standardized interface to allow researchers outside of a particular organization with remote access to another organization’s EHR data. This phase focused on robust, secure authentication and authorization of the researcher for access to EHR data.
Distributed Access Initiative: Leverage both the Local Access and Secure Enterprise Access standards, to focus on the development of a governance structure for standardizing distributed research queries. This phase built upon ONC’s former Standards and Interoperability (S&I) Framework QueryHealth Initiative, aimed at identifying standards and services to support distributed population queries.
PROJECT ACHIEVEMENTS & HIGHLIGHTS
In each of the phases, ONC established draft standards through an open and consensus based standards organization, tested and piloted the standards, and developed testing and certification tools to support consistent and interposable implementations. All phases of the DAF Initiative have been completed.
The project completed an environmental scan report on existing standards relevant to The Local Access API initiative (Phase 1), The Secure Enterprise Access Initiative (Phase 2), and The Distributed Access Initiative (Phase 3), including identifying what is available in Meaningful Use Stage 2 (MU2) and where there are gaps in existing standards.
PUBLICATIIONS, PRESENTATIONS, AND OTHER PUBLICALLY AVAILABLE RESOURCES
Learn more about the history of the Query Health Initiative at: https://www.healthit.gov/buzz-blog/from-the-onc-desk/queryhealth
Learn more about the ONC S&I Framework at: https://www.healthit.gov/sites/default/files/pdf/fact-sheets/standards-and-interoperability-framework.pdf
Learn more about the ONC the Data Access Framework (DAF) Initiative at: https://www.healthit.gov/topic/scientific-initiatives/pcor/data-access-framework-daf
Phase 1 and 2 implementation guides can be found at: http://hl7.org/FHIR/us/daf/2016Sep/index.html
Phase 3 implementation guide can be found at: http://hl7.org/FHIR/us/daf/2016Sep/daf-research.html
Use case for phase 1 can be found at: https://oncprojectracking.healthit.gov/wiki/display/TechLabSC/DAF+Use+Case+1-+Local+Data+Access+Consensus
Use case for phase 2 can be found at: https://oncprojectracking.healthit.gov/wiki/display/TechLabSC/DAF+Use+Case+2-+Targeted+Data+Access+Consensus
Use case for phase 3 can be found at: https://oncprojectracking.healthit.gov/wiki/display/TechLabSC/DAF+Home
Below is a list of ASPE-funded PCORTF projects that are related to this project
Creating the Foundational Blocks for the Learning Health Care System: Structured Data Capture – This project identified and developed the functional and technical specifications necessary to enable an EHR system to retrieve, display, and fill a structured form or template, and store and submit the completed form to an external repository. The goal of this project was to develop, pilot, and ballot technical data standards for common data elements as well as an electronic template for use in case reporting. Electronic case reporting refers to the ability of an EHR to automatically identify and report specific cases and submit this information in a particular format or template to an end point (e.g., clinical research, public health registry surveillance system).
PCOR: Privacy and Security Blueprint, Legal Analysis and Ethics Framework for Data Use, & Use of Technology for Privacy – Patient level data are essential to understanding and improving health outcomes. These data must be made available to researchers in a way that ensures the protection of patient privacy while providing sufficient granularity to allow meaningful research questions to be assessed. However, current laws and policies around the use of patient level data are nuanced and sometimes conflicting, creating confusion for researchers, providers, and patients. This is a collaborative effort between the ONC and Centers for Disease Control (CDC) to conduct research and create resources to improve the privacy of patients and their data.