Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project

Abstract

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.

Feasibility of Using Clinical Element Models (CEM) to Standardize Phenotype Variables in the Database of Genotypes and Phenotypes (dbGaP)

“…The SHARPn researchers suggested additional or different data requirements for particular secondary data use cases, since CEMs were originally created to retrieve EHR data [15,16]. The SHARPn team has been involved in revising or extending CEMs to meet the secondary data uses and has noted that creating common models to normalization of data is much needed but a big challenge [16,36]. Finally, we realized that many attributes of the CEM template models were not utilized in modeling the phenotype variables from dbGaP.…”

“…doi: 10.1371/journal.pone.0076384.g003 successfully adopted as a type system for NLP processing in SHARPn [14,15,16]. However, its applicability to the phenotype variables generated from research has not been tested.…”

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

“…Some studies applied standard NLP techniques, such as cTAKES, MedLEE, and MetaMap, others applied 'custom-made' NLP techniques. Examples of the combined use of standard NLP and text-and data-mining are found in [139][140][141] where cTAKES is used with Boolean logic to perform phenotyping and to extract drug-side effects. MedLEE was applied for: 1) adverse drug reaction (ADR) signaling, where the association between a drug and an ADR was obtained by using disproportionality analysis [142,143] or Boolean logic [144], or by building and analyzing statistical distributions of concepts (i.e., diseases, symptoms, medications) extracted from the narrative text [145]; 2) EHR-data driven phenotyping using Boolean logic on MedLEE-extracted concepts [136,146]; 3) automated classification of outcomes from the analysis of emergency department computed tomography imaging reports using machine learning methods, such as decision trees [147].…”

“…Incentives also spurred adoption of EHRs by general practitioners in the U.K. [10] Recent initiatives such as EHR4CR, [11] the Clinical and Translational Science Awards (CTSA) [12], the Strategic Health IT Advanced Research Projects (SHARP) program [13], and the Electronic Medical Records and Genomics (eMERGE) consortium [14] further added to this opportunity, contributing to the surge in clinical data reuse projects and publications observed. The fast growing quantity of clinical information available in electronic format makes reused clinical data a candidate for "big data" solutions [15].…”

Variations in Measurement of Sexual Activity Based on EHR Definitions

“…[1][2][3][4] The introduction of electronic health record (EHR) systems to manage clinical information has spurred a national movement toward using these systems for performance measurement. [5][6][7][8][9][10][11][12][13] Similarly, federal government efforts are encouraging quality measurement and use of health information technology in the area of pediatric and adolescent health care. 14,15 For example, the Pediatric Quality Measures Program assesses responsiveness to health information technology as a criterion for quality measures development.…”