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We show how the HL7 Virtual Medical Record (vMR) standard can be used to design and implement a data integrator (DI) component that collects patient information from heterogeneous sources and stores it into a personal health record, from which it can then retrieve data. Our working hypothesis is that the HL7 vMR standard in its release 1 version can properly capture the semantics needed to drive evidence-based clinical decision support [...]

To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI's evolution, and future directions and synergies for the group.

Homeless patients experience poor health outcomes and consume a disproportionate amount of health care resources compared with domiciled patients. There is increasing interest in the federal government in providing care coordination for homeless patients, which will require a systematic way of identifying these individuals.

There is wide recognition that, with the rapid implementation of electronic health records (EHRs), large data sets are available for research. However, essential standardized nursing data are seldom integrated into EHRs and clinical data repositories. There are many diverse activities that exist to implement standardized nursing languages in EHRs; however, these activities are not coordinated, resulting in duplicate efforts rather than building a shared learning environment and resources.

Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and [...]

The healthcare landscape is changing, driven by innovative care models and the emergence of new roles that are inter-professional in nature. Currently, the HL7/LOINC Document Ontology (DO) aids the use and exchange of clinical documents using a multi-axis structure of document attributes for Kind of Document, Setting, Role, Subject Matter Domain, and Type of Service. In this study, the adequacy of the Role axis for representing the type of author [...]

The verification of biomedical ontologies is an arduous process that typically involves peer review by subject-matter experts. This work evaluated the ability of crowdsourcing methods to detect errors in SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and to address the challenges of scalable ontology verification.

Standards terminologies may be large and complex, making their quality assurance challenging. Some terminology quality assurance (TQA) methodologies are based on abstraction networks (AbNs), compact terminology summaries. We have tested AbNs and the performance of related TQA methodologies on small terminology hierarchies. However, some standards terminologies, for example, SNOMED, are composed of very large hierarchies. Scaling AbN TQA techniques to such hierarchies poses a significant challenge. We present a scalable [...]

Large and complex terminologies, such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), are prone to errors and inconsistencies. Abstraction networks are compact summarizations of the content and structure of a terminology. Abstraction networks have been shown to support terminology quality assurance. In this paper, we introduce an abstraction network derivation methodology which can be applied to SNOMED CT target hierarchies whose classes are defined using only hierarchical relationships (ie [...]