J Am Med Inform Assoc - Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

Tópicos

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Resumo

SEARCH OBJECTIVE: To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction.MATERIALS AND METHODS: Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine.RESULTS: Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria.CONCLUSIONS: End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

Resumo Limpo

search object develop scalabl informat infrastructur normal structur unstructur electron health record ehr data unifi conceptbas model highthroughput phenotyp extractionmateri method softwar tool applic develop extract inform ehr repres conveni sampl structur unstructur data two ehr systemsmayo clinic intermountain healthcarewer use develop valid extract inform standard normal meaning use mu conform terminolog valu set standard use clinic element model cem resourc use demonstr semiautomat execut mu clinicalqu measur model use qualiti data model qdm opensourc rule engineresult use cem opensourc natur languag process terminolog servic enginesnam apach clinic text analysi knowledg extract system ctake common terminolog servic ctswe develop datanorm platform ensur data secur endtoend connect reliabl data flow within across institut demonstr applic platform execut qdmbase mu qualiti measur determin percentag patient year diabet whose recent lowdens lipoprotein cholesterol test result measur year mgdl random select cohort mayo clinic patient platform identifi patient denomin numer qualiti measur respect valid result indic identifi patient meet qdmbase criteriaconclus endtoend autom system extract clinic inform divers ehr system requir extens use standard vocabulari terminolog well robust inform model store discov process inform studi demonstr applic modular opensourc resourc enabl secondari use ehr data normal standardsbas compar consist format highthroughput phenotyp identifi patient cohort

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