Creating the Foundational Blocks for the Learning Health Care System: Structured Data Capture (SDC)

STATUS: Completed Project

BACKGROUND

Electronic health records (EHRs) have the potential to provide useful information for patient-centered outcomes research (PCOR). Many PCOR studies rely on patients’ clinical information collected and stored in EHRs. The use of this information for PCOR would be increased if relevant patient data (e.g., lab‑test results) were defined and collected in a common way across research studies using structured data definitions (i.e. common data elements) that comply with the consensus‑derived health data standards (e.g., LOINC, SNOMED), which were included under the Centers For Medicare & Medicaid Services (CMS) EHR Incentive Program. Development and adoption of common data element (CDE) standards enables interoperability in real‑world settings to support PCOR.

PROJECT PURPOSE & GOALS

This project identified and resulted in the development of the functional and technical specifications necessary to enable an EHR system to retrieve, display, 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).

Project Objectives:

• Conduct an environmental scan of existing technical standards and current use of data elements in research settings.

• Create a detailed use case document to guide the standards development process.

• Prepare a reference implementation guide for each of the technical standards.

• Develop, select, validate, and ballot standards for CDEs and the electronic case‑reporting template. This includes CDE standards for the data elements for the form, the structure, and design of the form, a standard for how the EHR interacts with the form, and a standard to enable these forms to auto‑populate with data extracted from the existing EHR.

PROJECT ACHIEVEMENTS & HIGHLIGHTS

• The SDC Initiative included tasks and work products surrounding the development of two standards-based profiles: Integrating Healthcare Enterprise (IHE) SDC profile and the Fast Healthcare Interoperability Resources (FHIR) SDC profile. The status for the IHE SDC profile and FHIR SDC profile are described below.

  • The most recently published IHE SDC profile (v2.1) was successfully published through IHE QRPH as a Standard for Trial.
  • Implementation occurred in October 2016.
  • IHE SDC Pilot teams (who began their activities in October 2015) successfully completed their work activities, gave final presentations and demonstrations of their implementations to ONC and community leadership, and provided final reports and lessons learned in October 2016.
  
  
  
   
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• One of the most important outcomes of SDC is the additional utility outside of the established use case. There is value in considering SDC for disaster reporting, adverse event reporting, exchanging patient care templates, registries, newborn screening for various diseases, death certificates, community engagement, immunization, etc.

PRESENTATIONS, PUBLICATIONS, AND OTHER PUBLICALLY AVAILABLE RESOURCES

Resources:

• FHIR SDC Profile can be found at: http://hl7.org/fhir/us/sdc/2016Sep/sdc.html

• FHIR DE Profile can be found at: https://www.hl7.org/fhir/elementdefinition-de.profile.xml.html

• The most recently published IHE SDC profile can be found at: https://www.ihe.net/resources/technical_frameworks/#qrph

RELATED PROJECTS

Below is a list of ASPE-funded PCORTF projects that are related to this project

Development of Data Infrastructure for Use of Electronic Health Records (EHRs) in Comparative – It is challenging to define and collect patient data (e.g., lab test results, marital status, and patient-reported depression) in a standardized way in different CER studies. This makes it difficult to enable the comparison of results and facilitate the use of EHR systems as a source of valid CER data. At the onset of this project, various clinical and health service research groups were already engaged in independent initiatives to standardize electronic data collection. Work was needed to build on these individual efforts to make it easier to conduct CER in diverse community health care settings, reduce data collection burden on health care providers and patients, and make site-specific modifications to EHR system capabilities in order to enable community participation. The infrastructure that would accommodate this goal consists of two components: 1) an authoritative repository to maintain and distribute up-to-date common data elements (CDEs) and patient assessment specifications with underlying terminology values sets for researchers and EHR developers: and 2) a standardized mechanism that would enable an EHR to capture structured data for a specified use case. With this infrastructure in place, researchers would readily have access to standardized electronic versions of data collection instruments relevant to CER.

Creating the Foundational Building Blocks for the Learning Health Care System: Data Access Standards for Electronic Health Records (EHRs) – EHRs have the potential to provide useful information for PCOR by aggregating patient data across disparate systems. Many current PCOR projects rely on patients’ clinical data collected in site or system specific EHRs. While other Office of the National Coordinator for Health Technology (ONC) activities are focused on standardizing the way the EHR data is collected within EHRs, researchers also need standardized ways to access that data. This project sought to make it easier to get data out of an EHR in a consistent and reproducible way and was a critical next step to enabling and simplifying 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.