TY - JOUR
T1 - Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data
T2 - The SHARPn project
AU - Rea, Susan
AU - Pathak, Jyotishman
AU - Savova, Guergana
AU - Oniki, Thomas A.
AU - Westberg, Les
AU - Beebe, Calvin E.
AU - Tao, Cui
AU - Parker, Craig G.
AU - Haug, Peter J.
AU - Huff, Stanley M.
AU - Chute, Christopher G.
N1 - Funding Information:
This manuscript was made possible by funding from the Strategic Health IT Advanced Research Projects (SHARP) Program administered by the Office of the National Coordinator for Health Information Technology. The contents of the manuscript are solely the responsibility of the authors.
PY - 2012/8
Y1 - 2012/8
N2 - The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation's health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation's many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or 'liquidity' of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.
AB - The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation's health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation's many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or 'liquidity' of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.
KW - Electronic health records
KW - Health information exchange
KW - High-throughput phenotyping
KW - Meaningful use
KW - Natural language processing
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U2 - 10.1016/j.jbi.2012.01.009
DO - 10.1016/j.jbi.2012.01.009
M3 - Article
C2 - 22326800
AN - SCOPUS:84865077477
SN - 1532-0464
VL - 45
SP - 763
EP - 771
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 4
ER -