Why do it the hard way? The case for an expressive description logic for SNOMED.

There has been major progress both in description logics and ontology design since SNOMED was originally developed. The emergence of the standard Web Ontology language in its latest revision, OWL 1.1 is leading to a rapid proliferation of tools. Combined with the increase in computing power in the past two decades, these developments mean that many of the restrictions that limited SNOMED's original formulation no longer need apply. We argue that many of the difficulties identified in SNOMED could be more easily dealt with using a more expressive language than that in which SNOMED was originally, and still is, formulated. The use of a more expressive language would bring major benefits including a uniform structure for context and negation. The result would be easier to use and would simplify developing software and formulating queries.

What's in a code? Towards a formal account of the relation of ontologies and coding systems.

Terminologies are increasingly based on "ontologies" developed in description logics and related languages such as the new Web Ontology Language, OWL. The use of description logic has been expected to reduce ambiguity and make it easier determine logical equivalence, deal with negation, and specify EHRs. However, this promise has not been fully realised: in part because early description logics were relatively inexpressive, in part, because the relation between coding systems, EHRs, and ontologies expressed in description logics has not been fully understood. This paper presents a unifying approach using the expressive formalisms available in the latest version of OWL, OWL 1.1.

Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT.

OBJECTIVES: 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. METHODS: We developed a methodology to crowdsource ontology verification that uses micro-tasking combined with a Bayesian classifier. We then conducted a prospective study in which both the crowd and domain experts verified a subset of SNOMED CT comprising 200 taxonomic relationships. RESULTS: The crowd identified errors as well as any single expert at about one-quarter of the cost. The inter-rater agreement (κ) between the crowd and the experts was 0.58; the inter-rater agreement between experts themselves was 0.59, suggesting that the crowd is nearly indistinguishable from any one expert. Furthermore, the crowd identified 39 previously undiscovered, critical errors in SNOMED CT (eg, 'septic shock is a soft-tissue infection'). DISCUSSION: The results show that the crowd can indeed identify errors in SNOMED CT that experts also find, and the results suggest that our method will likely perform well on similar ontologies. The crowd may be particularly useful in situations where an expert is unavailable, budget is limited, or an ontology is too large for manual error checking. Finally, our results suggest that the online anonymous crowd could successfully complete other domain-specific tasks. CONCLUSIONS: We have demonstrated that the crowd can address the challenges of scalable ontology verification, completing not only intuitive, common-sense tasks, but also expert-level, knowledge-intensive tasks.

Automated quality checks on repeat prescribing.

BACKGROUND: Good clinical practice in primary care includes periodic review of repeat prescriptions. Markers of prescriptions that may need review have been described, but manually checking all repeat prescriptions against the markers would be impractical. AIM: To investigate the feasibility of computerising the application of repeat prescribing quality checks to electronic patient records in United Kingdom (UK) primary care. DESIGN OF STUDY: Software performance test against benchmark manual analysis of cross-sectional convenience sample of prescribing documentation. SETTING: Three general practices in Greater Manchester, in the north west of England, during a 4-month period in 2001. METHOD: A machine-readable drug information resource, based on the British National Formulary (BNF) as the 'gold standard' for valid drug indications, was installed in three practices. Software raised alerts for each repeat prescribed item where the electronic patient record contained no valid indication for the medication. Alerts raised by the software in two practices were analysed manually. Clinical reaction to the software was assessed by semi-structured interviews in three practices. RESULTS: There was no valid indication in the electronic medical records for 14.8% of repeat prescribed items. Sixty-two per cent of all alerts generated were incorrect. Forty-three per cent of all incorrect alerts were as a result of errors in the drug information resource, 44% to locally idiosyncratic clinical coding, 8% to the use of the BNF without adaptation as a gold standard, and 5% to the inability of the system to infer diagnoses that, although unrecorded, would be 'obvious' to a clinical reading the record. The interviewed clinicians supported the goals of the software. CONCLUSION: Using electronic records for secondary decision support purposes will benefit from (and may require) both more consistent electronic clinical data collection across multiple sites, and reconciling clinicians' willingness to infer unstated but 'obvious' diagnoses with the machine's inability to do the same.

Models and inference methods for clinical systems: a principled approach.

Previous papers have argued for the existence of three different models in many clinical information systems--for the medical record, for inference in guidelines, and for concepts and re-usable facts. This paper presents a principled approach to deciding which information belongs in each model based on the nature of the queries or inference to be performed: necessary or contingent, open or closed world, algorithmic vs heuristic. It then discusses an important class of systems--"ontologically indexed knowledge bases"--and issues of metadata within this framework.

Getting the foot out of the pelvis: modeling problems affecting use of SNOMED CT hierarchies in practical applications.

OBJECTIVES: (a) To determine the extent and range of errors and issues in the Systematised Nomenclature of Medicine-Clinical Terms (SNOMED CT) hierarchies as they affect two practical projects. (b) To determine the origin of issues raised and propose methods to address them. METHODS: The hierarchies for concepts in the Core Problem List Subset published by the Unified Medical Language System were examined for their appropriateness in two applications. Anomalies were traced to their source to determine whether they were simple local errors, systematic inferences propagated by SNOMED's classification process, or the result of problems with SNOMED's schemas. Conclusions were confirmed by showing that altering the root cause and reclassifying had the intended effects, and not others. MAIN RESULTS: Major problems were encountered, involving concepts central to medicine including myocardial infarction, diabetes, and hypertension. Most of the issues raised were systematic. Some exposed fundamental errors in SNOMED's schemas, particularly with regards to anatomy. In many cases, the root cause could only be identified and corrected with the aid of a classifier. LIMITATIONS: This is a preliminary 'experiment of opportunity.' The results are not exhaustive; nor is consensus on all points definitive. CONCLUSIONS: The SNOMED CT hierarchies cannot be relied upon in their present state in our applications. However, systematic quality assurance and correction are possible and practical but require sound techniques analogous to software engineering and combined lexical and semantic techniques. Until this is done, anyone using SNOMED codes should exercise caution. Errors in the hierarchies, or attempts to compensate for them, are likely to compromise interoperability and meaningful use.

Unambiguous data modeling to ensure higher accuracy term binding to clinical terminologies.

Work in the field of recording standard, coded data is important to reduce medical errors caused by misinterpretation and misrepresentation of data. The paper discusses the need to ensure that the source of the data i.e. the clinical data model is unambiguous to increase the quality and accuracy of the data mapping to terminology codes. The study chooses one especially ambiguous data model and remodels it to make clearer both the structure of the data, as well as its intended use and semantics. By ensuring an unambiguous model, results of the data mapping increased in accuracy from 64.7% to 80.55%. The clinical experts evaluating the models found it easier working with the revised model and agreed on the mappings 93.1% times as against 48.57% times previously. The aim of the study is to encourage good modeling practice to enable clinicians to record and code patient data unambiguously and accurately.

Relations in biomedical ontologies.

To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

Scale and context: issues in ontologies to link health- and bio-informatics.

Bridging levels of scale and context are key problems for integrating Bio- and Health Informatics. Formal, logic-based ontologies using expressive formalisms are naturally "fractal" and provide new methods to support these aims. The basic notion of composition can be used to bridge scales; axioms can be used to carry implicit information; specific context markers can be included in definitions; and a hierarchy of semantic links can be used to represent subtle differences in point of view. Experience with OpenGALEN, the UK Drug Ontology and new experiments with the Gene Ontology and Foundational Model of Anatomy suggest that these are powerful tools provide practical solutions.