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Display Omitted A graph database approach employing SNOMED CT as the semantic core of a patient data warehouse.SNOMED CT data queries demonstrating negation and disjunction using a graph database approach.Using multiple controlled medical terminologies without external terminology services. ObjectiveSNOMED CT is the international lingua franca of terminologies for human health. Based in Description Logics (DL), the terminology enables data queries that incorporate inferences between data elements, as well as, those relationships that are explicitly stated. However, the ontologic and polyhierarchical nature of the SNOMED CT concept model make it difficult to implement in its entirety within electronic health record systems that largely employ object oriented or relational database architectures. The result is a reduction of data richness, limitations of query capability and increased systems overhead. The hypothesis of this research was that a graph database (graph DB) architecture using SNOMED CT as the basis for the data model and subsequently modeling patient data upon the semantic core of SNOMED CT could exploit the full value of the terminology to enrich and support advanced data querying capability of patient data sets. MethodsThe hypothesis was tested by instantiating a graph DB with the fully classified SNOMED CT concept model. The graph DB instance was tested for integrity by calculating the transitive closure table for the SNOMED CT hierarchy and comparing the results with transitive closure tables created using current, validated methods. The graph DB was then populated with 461,171 anonymized patient record fragments and over 2.1 million associated SNOMED CT clinical findings. Queries, including concept negation and disjunction, were then run against the graph database and an enterprise Oracle relational database (RDBMS) of the same patient data sets. The graph DB was then populated with laboratory data encoded using LOINC, as well as, medication data encoded with RxNorm and complex queries performed using LOINC, RxNorm and SNOMED CT to identify uniquely described patient populations. ResultsA graph database instance was successfully created for two international releases of SNOMED CT and two US SNOMED CT editions. Transitive closure tables and descriptive statistics generated using the graph database were identical to those using validated methods. Patient queries produced identical patient count results to the Oracle RDBMS with comparable times. Database queries involving defining attributes of SNOMED CT concepts were possible with the graph DB. The same queries could not be directly performed with the Oracle RDBMS representation of the patient data and required the creation and use of external terminology services. Further, queries of undefined depth were successful in identifying unknown relationships between patient cohorts. ConclusionThe results of this study supported the hypothesis that a patient database built upon and around the semantic model of SNOMED CT was possible. The model supported queries that leveraged all aspects of the SNOMED CT logical model to produce clinically relevant query results. Logical disjunction and negation queries were possible using the data model, as well as, queries that extended beyond the structural IS_A hierarchy of SNOMED CT to include queries that employed defining attribute-values of SNOMED CT concepts as search parameters. As medical terminologies, such as SNOMED CT, continue to expand, they will become more complex and model consistency will be more difficult to assure. Simultaneously, consumers of data will increasingly demand improvements to query functionality to accommodate additional granularity of clinical concepts without sacrificing speed. This new line of research provides an alternative approach to instantiating and querying patient data represented using advanced computable clinical terminologies.

Newborn screening (NBS) has high‐stakes health implications and requires rapid and effective communication between many people and organizations. Multiple NBS stakeholders worked together to create national guidance for reporting NBS results with HL7 (Health Level 7) messages that contain LOINC (Logical Observation Identifiers Names and Codes) and SNOMED-CT (Systematized Nomenclature of Medicine–Clinical Terms) codes, report quantitative test results, and use standardized computer‐readable UCUM units of measure. This guidance (a LOINC panel and an example annotated HL7 message) enables standard HL7 v2.5.1 laboratory messages to carry the information required for reporting NBS results. Other efforts include HL7 implementation guides for reporting point-of-care (POC) NBS results as well as standardizing follow-up of patients diagnosed with conditions identified through NBS. If the guidance is used nationally, regional and national registries can aggregate results from state programs to facilitate research and quality assurance and help ensure continuity of operations following a disaster situation.

Graphical abstractDisplay Omitted Highlights? Ten existing cephalometric standards were unified to one terminology set. ? LOINC was used as the unifying standard the 10 standards mapped to. ? This methodology could be used for other similar projects. Despite the existence of multiple standards for the coding of biomedical data and the known benefits of doing so, there remain a myriad of biomedical information domain spaces that are essentially un-coded and unstandardized. Perhaps a worse situation is when the same or similar information in a given domain is coded to a variety of different standards. Such is the case with cephalometrics - standardized measurements of angles and distances between specified landmarks on X-ray film used for orthodontic treatment planning and a variety of research applications. We describe how we unified the existing cephalometric definitions from 10 existing cephalometric standards to one unifying terminology set using an existing standard (LOINC). Using our example of an open and web-based orthodontic case file system, we describe how this work benefited our project and discuss how adopting or expanding established standards can benefit other similar projects in specialized domains.

Graphical abstractDisplay Omitted Highlights? A process for auditing the consistency and usefulness of LOINC use. ? Use of the version space approach to reduce auditing tasks to a manageable size. ? Created a system to classify the degree of semantic interoperability between two systems that exchange data using LOINC codes. ObjectivesWe wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC. MethodsWe collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness. ResultsThe number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'. ConclusionsThere are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange.

Nonlinear dynamical properties of sensitively recorded breathing signals (SRBS), which include cardiac induced air flow pulsations so-called pneumocardiogram (PNCG) signals, are investigated, in this methodological study. For this purpose, we assessed the SRBS of laboratory rat. The nonlinear behaviors of SRBS were investigated by the reconstructing phase space, using the autocorrelation function and the false nearest neighbor method. The chaotic SRBS attractors were discussed from the point of view of the cardiopulmonary system. This method can be used to assess the heart performance and respiratory mechanics, and might be useful to design for the physiological studies of cardiorespiratory system in small laboratory animals.