Does SNOMED CT post-coordination scale?

SNOMED CT is a compositional terminology. Construction of post-coordinated expressions allows users to specify new meaning by referencing existing SNOMED CT concepts. The use of post-coordinated expressions in information systems requires special software, a reasoner, to give the exact relations between post-coordinated expressions and existing SNOMED CT content. Thus, the performance characteristics of reasoners are important for implementation of post-coordination in information systems. This study aims to test how reasoners perform when a large number of post-coordinated expressions are added to SNOMED CT. The time needed to classify an ontology consisting of SNOMED CT plus an increasing number of post-coordinated expressions is measured. The best performing reasoner in this test classifies SNOMED CT plus 1 million post-coordinated expressions in 42 seconds. The time to classify grows a little less than quadratic as the size of the ontology increases. In conclusion, classification time is not a problem using current reasoners and current SNOMED CT releases even if a large number of post-coordinated expressions are added.

User-directed coordination in SNOMED CT.

The possibility of post-coordination of SNOMED CT concepts, especially by clinical users, is both an asset and a challenge for SNOMED CT implementation. To get insight in the applicability of post-coordination, we analyzed scenarios for user-directed coordination that are described in the documentation of SNOMED CT. The analyses were based on experiences from previous and ongoing research and implementation work, including national mapping projects, and investigations on collection of data for multiple uses. These scenarios show various usability and representation problems: high number of relationships for refinement and qualification, improper options for refinement, incorrect formal definitions, and lack of support for applying editorial rules. Improved user-directed coordination in SNOMED CT in real practice requires advanced sanctioning, increased consistency of definitions of concepts in SNOMED CT, and real-time analysis of the post-coordinate expression.

Applying representational state transfer (REST) architecture to archetype-based electronic health record systems.

BACKGROUND: The openEHR project and the closely related ISO 13606 standard have defined structures supporting the content of Electronic Health Records (EHRs). However, there is not yet any finalized openEHR specification of a service interface to aid application developers in creating, accessing, and storing the EHR content.The aim of this paper is to explore how the Representational State Transfer (REST) architectural style can be used as a basis for a platform-independent, HTTP-based openEHR service interface. Associated benefits and tradeoffs of such a design are also explored. RESULTS: The main contribution is the formalization of the openEHR storage, retrieval, and version-handling semantics and related services into an implementable HTTP-based service interface. The modular design makes it possible to prototype, test, replicate, distribute, cache, and load-balance the system using ordinary web technology. Other contributions are approaches to query and retrieval of the EHR content that takes caching, logging, and distribution into account. Triggering on EHR change events is also explored.A final contribution is an open source openEHR implementation using the above-mentioned approaches to create LiU EEE, an educational EHR environment intended to help newcomers and developers experiment with and learn about the archetype-based EHR approach and enable rapid prototyping. CONCLUSIONS: Using REST addressed many architectural concerns in a successful way, but an additional messaging component was needed to address some architectural aspects. Many of our approaches are likely of value to other archetype-based EHR implementations and may contribute to associated service model specifications.

Coding of procedures documented by general practitioners in Swedish primary care-an explorative study using two procedure coding systems.

BACKGROUND: Procedures documented by general practitioners in primary care have not been studied in relation to procedure coding systems. We aimed to describe procedures documented by Swedish general practitioners in electronic patient records and to compare them to the Swedish Classification of Health Interventions (KVÅ) and SNOMED CT. METHODS: Procedures in 200 record entries were identified, coded, assessed in relation to two procedure coding systems and analysed. RESULTS: 417 procedures found in the 200 electronic patient record entries were coded with 36 different Classification of Health Interventions categories and 148 different SNOMED CT concepts. 22.8% of the procedures could not be coded with any Classification of Health Interventions category and 4.3% could not be coded with any SNOMED CT concept. 206 procedure-concept/category pairs were assessed as a complete match in SNOMED CT compared to 10 in the Classification of Health Interventions. CONCLUSIONS: Procedures documented by general practitioners were present in nearly all electronic patient record entries. Almost all procedures could be coded using SNOMED CT.Classification of Health Interventions covered the procedures to a lesser extent and with a much lower degree of concordance. SNOMED CT is a more flexible terminology system that can be used for different purposes for procedure coding in primary care.

Visualization of disease distribution with SNOMED CT and ICD-10.

Methods for presentation of disease and health problem distribution in a health care environment rely among other things on the inherent structure of the controlled terminology used for coding. In the present study, this aspect is explored with a focus on ICD-10 and SNOMED CT. The distribution of 2,5 million diagnostic codes from primary health care in the Stockholm region is presented and analyzed through the "lenses" of ICD-10 and SNOMED CT. The patient encounters, originally coded with a reduced set of ICD-10 codes used in primary health care in Sweden, were mapped to SNOMED CT concepts through a mapping table. The method used for utilizing the richer structure of SNOMED CT as compared to ICD-10 is presented, together with examples of produced disease distributions. Implications of the proposed method for enriching a traditional classification such as ICD-10 through mappings to SNOMED CT are discussed.

Enriching a primary health care version of ICD-10 using SNOMED CT mapping.

BACKGROUND: In order to satisfy different needs, medical terminology systems must have richer structures. This study examines whether a Swedish primary health care version of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structure using category and chapter mappings from KSH97-P to SNOMED CT and SNOMED CT's structure. Manually-built mappings from KSH97-P's categories and chapters to SNOMED CT's concepts are used as a starting point. RESULTS: The mappings are manually evaluated using computer-produced information and a small number of mappings are updated. A new and poly-hierarchical chapter division of KSH97-P's categories has been created using the category and chapter mappings and SNOMED CT's generic structure. In the new chapter division, most categories are included in their original chapters. A considerable number of concepts are included in other chapters than their original chapters. Most of these inclusions can be explained by ICD-10's design. KSH97-P's categories are also extended with attributes using the category mappings and SNOMED CT's defining attribute relationships. About three-fourths of all concepts receive an attribute of type Finding site and about half of all concepts receive an attribute of type Associated morphology. Other types of attributes are less common. CONCLUSIONS: It is possible to use mappings from KSH97-P to SNOMED CT and SNOMED CT's structure to enrich KSH97-P's mono-hierarchical structure with a poly-hierarchical chapter division and attributes of type Finding site and Associated morphology. The final mappings are available as additional files for this paper.

Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT.

BACKGROUND: Primary care (PC) in Sweden provides ambulatory and home health care outside hospitals. Within the County Council of Stockholm, coding of diagnoses in PC is mandatory and is done by general practitioners (GPs) using a Swedish primary care version of the International Statistical Classification of Diseases, version 10 (ICD-10). ICD-10 has a mono-hierarchical structure. SNOMED CT is poly-hierarchical and belongs to a new generation of terminology systems with attributes (characteristics) that connect concepts in SNOMED CT and build relationships. Mapping terminologies and classifications has been pointed out as a way to attain additional advantages in describing and documenting healthcare data. A poly-hierarchical system supports the representation and aggregation of healthcare data on the basis of specific medical aspects and various levels of clinical detail. OBJECTIVE: To describe and compare diagnoses and health problems in KSH97-P/ICD-10 and SNOMED CT using primary care diagnostic data, and to explore and exemplify complementary aggregations of diagnoses and health problems generated from a mapping to SNOMED CT. METHODS: We used diagnostic data collected throughout 2006 and coded in electronic patient records (EPRs), and a mapping from KSH97-P/ICD-10 to SNOMED CT, to aggregate the diagnostic data with SNOMED CT defining hierarchical relationship Is a and selected attribute relationships. RESULTS: The chapter level comparison between ICD-10 and SNOMED CT showed minor differences except for infectious and digestive system disorders. The relationships chosen aggregated the diagnostic data to 2861 concepts, showing a multidimensional view on different medical and specific levels and also including clinically relevant characteristics through attribute relationships. CONCLUSIONS: SNOMED CT provides a different view of diagnoses and health problems on a chapter level, and adds significant new views of the clinical data with aggregations generated from SNOMED CT Is a and attribute relationships. A broader use of SNOMED CT is therefore of importance when describing and developing primary care.

Integration of tools for binding archetypes to SNOMED CT.

BACKGROUND: The Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems. METHODS: Lexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings. RESULTS: An integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source. CONCLUSION: Finding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail.

Systematizing medical alerts.

The current Swedish regulations for medical alerts in health records were designed for paper records. Suggestions for computerized systems are now being investigated. A proposed model using three alert categories, graphically represented using three directions, probably combined with three severity levels is presented here. Up represents hypersensitivities, left/back represents alerting diagnosis and right/forward represents alerting current and planned treatments. A small qualitative user study of the alert classification model and some graphical representations of it was conducted. One main finding is that most respondents found the use of directions intuitive as a means of presenting categories. Context dependency, information overload, and future possibilities for automated alert-gathering are also discussed in the paper.

An archetype-based testing framework.

With the introduction of EHR two-level modelling and archetype methodologies pioneered by openEHR and standardized by CEN/ISO, we are one step closer to semantic interoperability and future-proof adaptive healthcare information systems. Along with the opportunities, there are also challenges. Archetypes provide the full semantics of EHR data explicitly to surrounding systems in a platform-independent way, yet it is up to the receiving system to interpret the semantics and process the data accordingly. In this paper we propose a design of an archetype-based platform-independent testing framework for validating implementations of the openEHR archetype formalism as a means of improving quality and interoperability of EHRs.

Creating a medical dictionary using word alignment: the influence of sources and resources.

BACKGROUND: Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. METHODS: We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. RESULTS: The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. CONCLUSION: More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10.

Graphical overview and navigation of electronic health records in a prototyping environment using Google Earth and openEHR archetypes.

This paper describes selected earlier approaches to graphically relating events to each other and to time; some new combinations are also suggested. These are then combined into a unified prototyping environment for visualization and navigation of electronic health records. Google Earth (GE) is used for handling display and interaction of clinical information stored using openEHR data structures and 'archetypes'. The strength of the approach comes from GE's sophisticated handling of detail levels, from coarse overviews to fine-grained details that has been combined with linear, polar and region-based views of clinical events related to time. The system should be easy to learn since all the visualization styles can use the same navigation. The structured and multifaceted approach to handling time that is possible with archetyped openEHR data lends itself well to visualizing and integration with openEHR components is provided in the environment.

An approach for generating fuzzy rules from decision trees.

Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree. After assigning membership functions for each variable in the crisp rules, they were converted into fuzzy rules and a mathematical model was constructed. One hundred randomly selected cases were examined by this model and compared with crisp rules predictions. The outcomes were examined by the area under the ROC curve (AUC). No significant difference was noticed between these two approaches for prediction of recurrence of breast cancer. By soft discretization of variables according to resulting rules from DTI, a predictive model, which is both more robust to noise and more comprehensible for clinicians, can be built.

Interactive visualization and navigation of complex terminology systems, exemplified by SNOMED CT.

Free-text queries are natural entries into the exploration of complex terminology systems. The way search results are presented has impact on the user's ability to grasp the overall structure of the system. Complex hierarchies like the one used in SNOMED CT, where nodes have multiple parents (IS-A) and several other relationship types, makes visualization challenging. This paper presents a prototype, TermViz, applying well known methods like "focus+context" and self-organizing layouts from the fields of Information Visualization and Graph Drawing to terminologies like SNOMED CT and ICD-10. The user can simultaneously focus on several nodes in the terminologies and then use interactive animated graph navigation and semantic zooming to further explore the terminology systems without loosing context. The prototype, based on Open Source Java components, demonstrates how a number of Information Visualisation methods can aid the exploration of medical terminologies with millions of elements and can serve as a base for further development.

Creating a medical English-Swedish dictionary using interactive word alignment.

BACKGROUND: This paper reports on a parallel collection of rubrics from the medical terminology systems ICD-10, ICF, MeSH, NCSP and KSH97-P and its use for semi-automatic creation of an English-Swedish dictionary of medical terminology. The methods presented are relevant for many other West European language pairs than English-Swedish. METHODS: The medical terminology systems were collected in electronic format in both English and Swedish and the rubrics were extracted in parallel language pairs. Initially, interactive word alignment was used to create training data from a sample. Then the training data were utilised in automatic word alignment in order to generate candidate term pairs. The last step was manual verification of the term pair candidates. RESULTS: A dictionary of 31,000 verified entries has been created in less than three man weeks, thus with considerably less time and effort needed compared to a manual approach, and without compromising quality. As a side effect of our work we found 40 different translation problems in the terminology systems and these results indicate the power of the method for finding inconsistencies in terminology translations. We also report on some factors that may contribute to making the process of dictionary creation with similar tools even more expedient. Finally, the contribution is discussed in relation to other ongoing efforts in constructing medical lexicons for non-English languages. CONCLUSION: In three man weeks we were able to produce a medical English-Swedish dictionary consisting of 31,000 entries and also found hidden translation errors in the utilized medical terminology systems.