SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

An evaluation of SNOMED CT® in the domain of complex chronic conditions

<p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Objective</strong>: To determine the content coverage in SNOMED CT<strong style="mso-bidi-font-weight: normal;">®</strong> to represent the multidisciplinary terms and concepts in the domain for complex chronic conditions</p><p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Methods</strong>: An evaluation of the coverage of multidisciplinary health factors in SNOMED CT<strong style="mso-bidi-font-weight: normal;">®</strong> for the complex and chronic condition, Multiple Chemical Sensitivity (MCS) is conducted in the study. The methodology included a retrospective audit of patient charts and feedback from multidisciplinary clinicians in the creation of a controlled vocabulary used in the generation of patient profiles for MCS. Clinicians and experts in the field reviewed and tested the vocabulary for its usefulness (scope, specificity and structure) by re-coding 3 patient profiles using the vocabulary. Cohen's kappa analysis was conducted to determine inter-rater reliability. Cronbach's alpha analysis was conducted to determine the internal reliability of the survey questionnaire.</p><p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Results</strong>: One hundred patient charts and 9 clinicians from varying health disciplines participated in the study. SNOMED CT<strong style="mso-bidi-font-weight: normal;">®</strong> was shown to capture nearly 82% of the concepts spanning multidisciplinary areas of health focus. The nutrition area of health focus had the highest level of exact matches Furthermore post-coordination was applied in an attempt to improve coverage of concepts to 75% ( of 45 terms) of the missing terms in SNOMED CT ® . Seventy-five percent (n=9) of the clinicians agreed on the overall usefulness of the vocabulary.</p><p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Conclusions</strong>: SNOMED CT® had a reasonable coverage of the multidisciplinary health concepts required to describe a complex and chronic condition. Standardizing the multidisciplinary vocabulary with reference tag to a widely used reference terminology such as SNOMED CT® to discuss the terms and concepts used may improve the understanding across disciplines and communities of practice. Overall, based on the availability of concepts in SNOMED CT® and the feedback from clinicians, the approach looks promising and should be further explored.</p

Adding HL7 version 3 data types to PostgreSQL

The HL7 standard is widely used to exchange medical information electronically. As a part of the standard, HL7 defines scalar communication data types like physical quantity, point in time and concept descriptor but also complex types such as interval types, collection types and probabilistic types. Typical HL7 applications will store their communications in a database, resulting in a translation from HL7 concepts and types into database types. Since the data types were not designed to be implemented in a relational database server, this transition is cumbersome and fraught with programmer error. The purpose of this paper is two fold. First we analyze the HL7 version 3 data type definitions and define a number of conditions that must be met, for the data type to be suitable for implementation in a relational database. As a result of this analysis we describe a number of possible improvements in the HL7 specification. Second we describe an implementation in the PostgreSQL database server and show that the database server can effectively execute scientific calculations with units of measure, supports a large number of operations on time points and intervals, and can perform operations that are akin to a medical terminology server. Experiments on synthetic data show that the user defined types perform better than an implementation that uses only standard data types from the database server.Comment: 12 pages, 9 figures, 6 table

Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

The integration of WHO classifications and reference terminologies to improve information exchange and quality of electronic health records: the SNOMED\u2013CT ICF harmonization within the ICD-11 revision process

Introduction The Family of International Classifications (WHO-FIC) is a suite of integrated classification products of the World Health Organization (WHO) that can be used to provide information on different aspects of health and the health-care system. These tools and their national modifications allow, together with the related classifications of health interventions, full representation of the volumes of health services provided in the various countries that adopt case mix systems. The use of standardized terminologies in classifications, for the definition of the descriptive characteristics of the disease, is a necessary step to allow full integration between different information systems, making available information about the diagnosed diseases, the performed health procedures and the level of functioning of the person, for very different uses such as, for example, public health, safety of care and quality control. Materials and methods Within the WHO and International Health Terminology Standards Development Organization (IHTSDO) collaboration agreement, a work of independent review was carried out on all the Activities and Participation categories (A&P) of the WHO International Classification of Functioning, Disability and Health (ICF), in order to identify equivalence and gaps to the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) concepts in terms of lexical, semantic (content) and hierarchical matching, to harmonize WHO classifications and SNOMED CT. Results and conclusions The performed mapping suggests that the ICF A&P categories are semantically and hierarchically different from the terms of SNOMED CT thus confirming the high value of the WHO-IHTSDO synergy aiming to frame together, in a joint effort, their respective unique contribution. Recommendations were formulated to WHO and IHTSDO in order to better frame together, in a joint effort, their respective unique contribution ensuring that SNOMED CT and ICF can interoperate in electronic health records

Clinical coverage of an archetype repository over SNOMED-CT

AbstractClinical archetypes provide a means for health professionals to design what should be communicated as part of an Electronic Health Record (EHR). An ever-growing number of archetype definitions follow this health information modelling approach, and this international archetype resource will eventually cover a large number of clinical concepts. On the other hand, clinical terminology systems that can be referenced by archetypes also have a wide coverage over many types of health-care information.No existing work measures the clinical content coverage of archetypes using terminology systems as a metric. Archetype authors require guidance to identify under-covered clinical areas that may need to be the focus of further modelling effort according to this paradigm.This paper develops a first map of SNOMED-CT concepts covered by archetypes in a repository by creating a so-called terminological Shadow. This is achieved by mapping appropriate SNOMED-CT concepts from all nodes that contain archetype terms, finding the top two category levels of the mapped concepts in the SNOMED-CT hierarchy, and calculating the coverage of each category. A quantitative study of the results compares the coverage of different categories to identify relatively under-covered as well as well-covered areas. The results show that the coverage of the well-known National Health Service (NHS) Connecting for Health (CfH) archetype repository on all categories of SNOMED-CT is not equally balanced. Categories worth investigating emerged at different points on the coverage spectrum, including well-covered categories such as Attributes, Qualifier value, under-covered categories such as Microorganism, Kingdom animalia, and categories that are not covered at all such as Cardiovascular drug (product)

Supporting Clinical Decision Making in Cancer Care Delivery

Background: Cancer treatment and management require complicated clinical decision making to provide the highest quality of care for an individual patient. This is facilitated in part with ever-increasing availability of medications and treatments but hindered due to barriers such as access to care, cost of medications, clinician knowledge, and patient preferences or clinical factors. Although guidelines for cancer treatment and many symptoms have been developed to inform clinical practice, implementation of these guidelines into practice is often delayed or does not occur. Informatics-based approaches, such as clinical decision support, may be an effective tool to improve guideline implementation by delivering patient-specific and evidence-based knowledge to the clinician at the point of care to allow shared decision making with a patient and their family. The large amount of data in the electronic health record can be utilized to develop, evaluate, and implement automated approaches; however, the quality of the data must first be examined and evaluated. Methods: This dissertation addresses gaps the literature about clinical decision making for cancer care delivery. Specifically, following an introduction and review of the literature for relevant topics to this dissertation, the researcher presents three studies. In Study One, the researcher explores the use of clinical decision support in cancer therapeutic decision making by conducting a systematic review of the literature. In Study Two, the researcher conducts a quantitative study to describe the rate of guideline concordant care provided for prevention of acute chemotherapy-induced nausea and vomiting (CINV) and to identify predictors of receiving guideline concordant care. In Study Three, the researcher conducts a mixed-methods study to evaluate the completeness, concordance, and heterogeneity of clinician documentation of CINV. The final chapter of this dissertation is comprised of key findings of each study, the strengths and limitations, clinical and research implications, and future research. Results: In Study One, the systematic review, the researcher identified ten studies that prospectively studied clinical decision support systems or tools in a cancer setting to guide therapeutic decision making. There was variability in these studies, including study design, outcomes measured, and results. There was a trend toward benefit, both in process and patient-specific outcomes. Importantly, few studies were integrated into the electronic health record. In Study Two, of 180 patients age 26 years or less, 36% received guideline concordant care as defined by pediatric or adult guidelines, as appropriate. Factors associated with receiving guideline concordant care included receiving a cisplatin-based regimen, being treated in adult oncology compared to pediatric oncology, and solid tumor diagnosis. In Study Three, of the 127 patient records reviewed for the documentation of chemotherapy-induced nausea and vomiting, 75% had prescriber assessment documented and 58% had nursing assessment documented. Of those who had documented assessments by both prescriber and nurse, 72% were in agreement of the presence/absence of chemotherapy-induced nausea and vomiting. After mapping the concept through the United Medical Language System and developing a post-coordinated expression to identify chemotherapy-induced nausea and vomiting in the text, 85% of prescriber documentation and 100% of nurse documentation could be correctly categorized as present/absent. Further descriptors of the symptoms, such as severity or temporality, however, were infrequently reported. Conclusion: In summary, this dissertation provides new knowledge about decision making in cancer care delivery. Specifically, in Study One the researcher describes that clinical decision support, one potential implementation strategy to improve guideline concordant care, is understudied or under published but a promising potential intervention. In Study Two, I identified factors that were associated with receipt of guideline concordant care for CINV, and these should be further explored to develop interventions. Finally, in Study Three, I report on the limitations of the data quality of CINV documentation in the electronic health record. Future work should focus on validating these results on a multi-institutional level

Decision support system for in-flight emergency events

Medical problems during flight have become an important issue as the number of passengers and miles flown continues to increase. The case of an incident in the plane falls within the scope of the healthcare management in the context of scarce resources associated with isolation of medical actors working in very complex conditions, both in terms of human and material resources. Telemedicine uses information and communication technologies to provide remote and flexible medical services, especially for geographically isolated people. Therefore, telemedicine can generate interesting solutions to the medical problems during flight. Our aim is to build a knowledge-based system able to help health professionals or staff members addressing an urgent situation by given them relevant information, some knowledge, and some judicious advice. In this context, knowledge representation and reasoning can be correctly realized using an ontology that is a representation of concepts, their attributes, and the relationships between them in a particular domain. Particularly, a medical ontology is a formal representation of a vocabulary related to a specific health domain. We propose a new approach to explain the arrangement of different ontological models (task ontology, inference ontology, and domain ontology), which are useful for monitoring remote medical activities and generating required information. These layers of ontologies facilitate the semantic modeling and structuring of health information. The incorporation of existing ontologies [for instance, Systematic Nomenclature Medical Clinical Terms (SNOMED CT)] guarantees improved health concept coverage with experienced knowledge. The proposal comprises conceptual means to generate substantial reasoning and relevant knowledge supporting telemedicine activities during the management of a medical incident and its characterization in the context of air travel. The considered modeling framework is sufficiently generic to cover complex medical situations for isolated and vulnerable populations needing some care and support services