Int J Tuberc Lung Dis

OBJECTIVE:To measure the frequency of diseases related to latent tuberculosis infection (LTBI) and tuberculosis (TB), we assessed the agreement between diagnosis codes for TB or LTBI in electronic health records (EHRs) and insurance claims for the same person.METHODS:In a US population-based, retrospective cohort study, we matched TB-related Systematized Nomenclature of Medicine\u2013Clinical Terms (SNOMED CT) EHR codes and International Statistical Classification of Diseases, 10thRevision, Clinical Modification (ICD-10-CM) claims codes. Furthermore, LTBI was identified using a published ICD-based algorithm and all LTBI- and TB-related SNOMED CT codes.RESULTS:Of people with the 10 most frequent TB-related claim codes, 50% did not have an exact-matched EHR code. Positive tuberculin skin test was the most frequent unmatched EHR code and people with the 10 most frequent TB EHR codes, 40% did not have an exact-matched claim code. The most frequent unmatched claim code was TB screening encounter. EHR codes for LTBI matched to claims codes for TB testing; pulmonary TB; and nonspecific, positive or adverse tuberculin reaction.CONCLUSION:TB-related EHR codes and claims diagnostic codes often disagree, and people with claims codes for LTBI have unexpected EHR codes, indicating the need to reconcile these coding systems.CC999999/ImCDC/Intramural CDC HHS/United States2020-10-26T00:00:00Z32718404PMC75867228583vault:3611

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph