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

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

PURPOSE The learning health care system refers to the cycle of turning health care data into knowledge, translating that knowledge into practice, and creating new data by means of advanced information technology. The electronic Primary Care Research Network (ePCRN) was a project, funded by the US National Institutes of Health, with the aim to facilitate clinical research using primary care electronic health records (EHRs). METHODS We identifi ed the requirements necessary to deliver clinical studies via a distributed electronic network linked to EHRs. After we explored a variety of informatics solutions, we constructed a functional prototype of the software. We then explored the barriers to adoption of the prototype software within US practice-based research networks. RESULTS We developed a system to assist in the identifi cation of eligible cohorts from EHR data. To preserve privacy, counts and fl agging were performed remotely, and no data were transferred out of the EHR. A lack of batch export facilities from EHR systems and ambiguities in the coding of clinical data, such as blood pressure, have so far prevented a full-scale deployment. We created an international consortium and a model for sharing further ePCRN development across a variety of ongoing projects in the United States and Europe. CONCLUSIONS A means of accessing health care data for research is not suffi cient in itself to deliver a learning health care system. EHR systems need to use sophisticated tools to capture and preserve rich clinical context in coded data, and business models need to be developed that incentivize all stakeholders from clinicians to vendors to participate in the system.

Original languageEnglish (US)
Pages (from-to)54-59
Number of pages6
JournalAnnals of family medicine
Volume10
Issue number1
DOIs
StatePublished - 2012

Bibliographical note

Funding Information:
Funding support: ePCRN has been funded with Federal funds from the National Institutes of Health , under contract No. HHS268N200425212C , “Re-engineering the Clinical Research Enterprise.” Further funding in the United Kingdom has been provided by the NIHR National School for Primary Care Research. The TRANSFoRm project, which is partially funded by the European Commission, DG INFSO (FP7 247787), will contribute to the further development of software under the ePCRN consortium 2010-2015.

Funding Information:
Simple mapping of vocabularies is inadequate in coupling the research and clinical domains. The entire premise of the learning health care system rests on the ability to exchange data between clinical and research systems (system interoperability). Terminology services that map one terminology to another are a necessary but not sufficient approach to this problem. The development of standards in data models is an international problem that requires an international solution. At present, this work is being carried out from the research perspective under the auspices of CDISC, a consortium originally set up to ensure interoperability of data for the commercial clinical trials community, HL7, supported by EHR vendors, and caBIG, until recently supported by the NIH. The needs of the learning health care system require a greater integration of research and clinical data standards, already evident with the mapping of BRIDG v3 to the HL7 Reference Information Model and a much deeper delving into domain concepts in contrast to simple data items. Support for this work, as well as the maintenance of terminology services, needs to be built into the business models that support the meaningful use of EHRs.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Electronic health records
  • Information management/informatics
  • Quantitative methods
  • Randomized clinical trials
  • Research capacity building

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