Abstract
Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
Highlights
Electronic health records (EHRs) have been widely adopted in US hospitals since the Health Information Technology for Electronic and Clinical Health Act (HITECH) was passed in 2009, and offer an unprecedented opportunity to accelerate translational research because of advantages of scale and cost efficiency as compared with traditional cohort-based studies.[1]
We present an approach to mapping the outcomes of laboratory tests as encoded in EHRs with Laboratory Observation Identifier Names and Codes (LOINC) terms for the tests and Fast Healthcare Interoperability Resource (FHIR) Observation resources representing the test results as Human Phenotype Ontology (HPO) terms
We present an approach to the semantic integration of laboratory tests and results in EHR data
Summary
Electronic health records (EHRs) have been widely adopted in US hospitals since the Health Information Technology for Electronic and Clinical Health Act (HITECH) was passed in 2009, and offer an unprecedented opportunity to accelerate translational research because of advantages of scale and cost efficiency as compared with traditional cohort-based studies.[1]. Phenome-wide association studies (PheWAS) can exploit EHR data to define case–control cohorts for disease diagnoses or laboratory traits and analyze associations with hundreds of thousands of genetic variants.[2,3,4] Despite the great potential of EHR data, patient phenotyping from EHRs is still challenging because the phenotype information is distributed in many EHR locations (laboratories, notes, problem lists, imaging data, etc.) and since EHRs have vastly different structures across sites This lack of integration represents a substantial barrier to widespread use of EHR data in translational research. The software rolls up LOINC terms for tests whose outcomes are medically comparable into common categories and interprets the outcome as HPO terms, thereby automatically extracting detailed, deep phenotypic profiles of laboratory results for downstream studies.
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