Evaluating OpenEHR for Storing Computable Representations of Electronic Health Record Phenotyping Algorithms

Abstract

Abstract-Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.

Big data from electronic health records for early and late translational cardiovascular research: challenges and potential

“…For example, defining atrial fibrillation using structured national health records may involve several hundred codes for diagnoses, drugs, procedures in a phenotyping algorithm. Clinical information standards such as openEHR 51 or semantic web technologies 52 , 53 can enable researchers to create computational representations of phenotyping algorithms which facilitate their sharing across the research community.…”

UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER

“…Risk factors for initial presentation of heart failure (HF) phenotype: hazard ratio (HR) and 95% confidence interval of smoking status, type 2 diabetes mellitus (T2DM), systolic blood pressure (BP) and heart rate based on previously published CALIBER studies 29 , 75 , 76 compared with estimates obtained from investigator-led studies derived using manually curated research data 77–80 . All individual analyses have been adjusted for age and sex and other covariates.…”

“…Observational Medical Outcomes Partnership Common Data Model can potentially act as Relational Database Management System agnostic schema which standardized analytical tools can be deployed on and has been shown to be robust 73 , 74 and we are currently in the process of evaluating the fidelity of the data transformation. We have additionally evaluated different approaches (Semantic Web Technologies, openEHR) 75 , 76 for storing phenotype definitions in a computable format that can enable high-throughput phenotyping and eliminate the need for manual human-driven translation to SQL queries. Given that all of UK primary care EHR data are hosted on 4 clinical information systems vendors, there is a real opportunity to create computable phenotypes which can be utilized across the NHS 77 .…”

UK phenomics platform for developing and validating EHR phenotypes: CALIBER

“…OMOP CDM can potentially act as Relational Database Management System (RDBMS) agnostic schema which standardized analytical tools can be deployed on and has been shown to be robust [73,74] and we are currently in the process of evaluating the fidelity of the data transformation. We have additionally evaluated different approaches (Semantic Web Technologies, openEHR [75,76]) for storing phenotype definitions in a computable format that can enable high-throughput phenotyping and eliminate the need for manual human-driven translation to SQL queries. Given that all of UK primary care EHR data are hosted on four clinical information systems vendors, there is a real opportunity to create computable phenotypes which can be utilized across the NHS [77].…”