Envisioning a Learning Health Care System: The Electronic Primary Care Research Network, A Case Study

Brendan C. Delaney; Kevin A. Peterson; Stuart Speedie; Adel Taweel; Theodoros N. Arvanitis; F. D. Richard Hobbs

doi:10.1370/afm.1313

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Figure 1
The learning health care system.

RCT = randomized controlled trial.
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Figure 2
The electronic Primary Care Research Network (ePCRN).

eCRF = electronic case report form.

Note: The ePCRN system functions in a modular fashion. A researcher is able to create a standardized computable representation of their study eligibility criteria using a Web-based workbench and a terminology service. With permission from the network, the system creates a search tailored for the target clinical data, referred to as a gateway. The gateway can be a copy of a single health record system or an aggregate data repository. It holds records that are potentially identifiable, however, either directly or via pseudoanonymization. The search reports counts of eligible subjects to the researcher, and then tags the identified subjects for local recruitment by practice staff.

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Table 1

Lessons Learned From the ePCRN (Electronic Primary Care Research Network) Project

Problem	Potential Solution
Extracting coded data from an EHR loses information and may introduce inaccuracies, as different EHR systems do not record some clinical data items consistently, eg, blood pressure. Inaccuracies usually arise where the main terminology adopted within the EHR is a disease-based classification, such as ICD, that only partially meets the system’s requirement for clinical terminology	Promote the uptake of standard clinical concept representations, such as archetypes based on the CEN/ISO 13606 standard or HL7 detailed clinical models. Rather than a simple term, a more detailed description of the clinical context and value of the clinical data are maintained
Data extraction standards based on a snapshot transfer of EHR data on a given date (such as CCD/CCR) can be unwieldy when data needs to be extracted on a repeated basis, and bulk export facilities are rarely provided by EHR vendors	Promote mandatory adoption of HL7 information exchange standards as an international standard for the exchange of data between and with EHR systems. Include the necessary terminology services to map between different vocabulary systems where necessary
Clinicians and health care providers are rarely incentivized to maintain good data quality on the basis of research use alone	Provide good clinical reasons for data quality and detailed record keeping, such as audit or decision support
Legal and ethical constraints in many countries limit linkage of data and its use for research without consent	Promote international consensus as to how and when data can be linked without consent, and develop systems for managing consent to extraction or study participation across systems. Include adoption of privacy-enhancing technologies, such as filters and third-party linkage systems
Researchers largely unaware of potential benefits offered by electronic systems to support research, and do not therefore create demand for wider deployment	Conduct well-publicized pilot deployments and evaluations

CCD = continuity of care document ; CCR = Continuity of Care Record; CEN = European Committee for Standardization; EHR = electronic health record; HL7 = Health Level Seven International; ICD = International Classification of Diseases; ISO = International Organization for Standardization.

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Table 2

Steps That May Support a Learning Health Care System

Knowledge translation

Develop meaningful use of the EHR to support diagnostic and therapeutic support based on evidence

Develop a computable representation of research evidence and make that available to EHR systems as a Web service

Develop a means of providing diagnostic or therapeutic prompts within an EHR that works across a variety of EHR systems

Turning data into knowledge

Develop a standardized means of using archetypes based on CEN/ISO 13606 within EHR systems

Maintain up-to-date terminology services that can be used by both clinical and research systems

Promote the wide reuse and archiving of defined research data elements via CEN/ISO 11179

Start to link well-defined clinical concepts with well-defined research concepts by mapping archetypes to defined data elements

Create domain specific data models, linking together via BRIDG to enable sharing of archetypes and data elements between domains

Create open-source middleware capable of brokering data exchange and clinical research activities (recruitment, follow-up, etc) between clinical systems

Extend clinical research standards to enable sharing computable representations of clinical study protocols between systems

BRIDG = Biomedical Research Integrated Domain Group; EHR = electronic health record; CEN = European Committee for Standardization; ISO = International Organization for Standardization.

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Supplemental Appendix and Figure

Supplemental Appendix: Glossary of Terms; Supplemental Figure 1. Electronic Primary Care Research Network (ePCRN) architecture.

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Supplemental data: Appendix - PDF file, 1 page, 156 KB
Supplemental data: Figure - PDF file, 1 page, 156 KB
The Article in Brief

Envisioning a Learning Health Care System: The Electronic Primary Care Research Network, a Case Study

Brendan C. Delaney , and colleagues

Background Highlighting both the challenges and opportunities for using existing electronic clinical data from dispersed primary care practices for large-scale clinical research, the authors examine options and evaluate a functional software prototype for facilitating research in community practice settings. The electronic Primary Care Research Network (ePRN) project was one of 12 funded by the National Institutes of Health with the aim of developing clinical research.

What This Study Found This study identifies requirements for delivering clinical studies via a distributed electronic network linked to electronic health records. The study finds that electronic health record systems must use more sophisticated tools to capture and preserve rich clinical context in coded data. The authors also identify barriers to adopting prototype software in practice-based research networks, and describe an international consortium and model for sharing further developments across ongoing projects in the United States and Europe.

Implications

To fully realize the vision of a true learning health care system, the authors call for the creation of an open market for e-health applications. Such a system, they assert, can be achieved only through open collaboration between the various stakeholders � from clinicians to vendors � with the goal of creating a single shared architecture.