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

A health terminological system for inherited retinal diseases: Content coverage evaluation and a proposed classification

PURPOSE

To present a classification of inherited retinal diseases (IRDs) and evaluate its content coverage in comparison with common standard terminology systems.

METHODS

In this comparative cross-sectional study, a panel of subject matter experts annotated a list of IRDs based on a comprehensive review of the literature. Then, they leveraged clinical terminologies from various reference sets including Unified Medical Language System (UMLS), Online Mendelian Inheritance in Man (OMIM), International Classification of Diseases (ICD-11), Systematized Nomenclature of Medicine (SNOMED-CT) and Orphanet Rare Disease Ontology (ORDO).

RESULTS

Initially, we generated a hierarchical classification of 62 IRD diagnosis concepts in six categories. Subsequently, the classification was extended to 164 IRD diagnoses after adding concepts from various standard terminologies. Finally, 158 concepts were selected to be classified into six categories and genetic subtypes of 412 cases were added to the related concepts. UMLS has the greatest content coverage of 90.51% followed respectively by SNOMED-CT (83.54%), ORDO (81.01%), OMIM (60.76%), and ICD-11 (60.13%). There were 53 IRD concepts (33.54%) that were covered by all five investigated systems. However, 2.53% of the IRD concepts in our classification were not covered by any of the standard terminologies.

CONCLUSIONS

This comprehensive classification system was established to organize IRD diseases based on phenotypic and genotypic specifications. It could potentially be used for IRD clinical documentation purposes and could also be considered a preliminary step forward to developing a more robust standard ontology for IRDs or updating available standard terminologies. In comparison, the greatest content coverage of our proposed classification was related to the UMLS Metathesaurus.

Fulfilling information needs of patients in online health communities

BACKGROUND

Online health communities (OHCs) experience difficulties in utilising patient reported posts to fulfil the information needs of online patients concerning health related issues.

OBJECTIVES

We aim to propose a comprehensive method that leverages medical domain knowledge to extract useful information from posts to fulfil information needs of online patients.

METHODS

A knowledge representation framework based on authoritative knowledge sources in the medical field for the OHC is proposed. On the basis of the framework, a health related information extraction process for analysing the posts in the OHC is proposed. Then, knowledge support rate (KSR) and effective information rate (EIR) are introduced as metrics to evaluate changes in knowledge extracted from the knowledge sources in terms of fulfilling the information needs of patients in the OHC.

RESULTS

On the basis of a data set with 372 343 posts in an OHC, experimental results indicate that our method effectively extracts relevant knowledge for online patients. Moreover, KSR and EIR are feasible metrics of changes in knowledge in terms of fulfilling the information needs.

CONCLUSIONS

The OHCs effectively fulfil the information needs of patients by utilising authoritative domain knowledge in the medical field. Knowledge based services for online patients facilitate an intelligent OHC in the future.

SNOMED CT revisions and coded data repositories: when to upgrade?

SNOMED CT is gaining momentum in its acceptance and operational application as a reference terminology in electronic health systems. Because it is revised every six months, organizations using SNOMED CT might feel a need to ensure that their systems are synchronized with these revisions. It has been shown that for certain sorts of applications migration to a new version is a labor-intensive process. Here two indicators - the evolution of the global information content of an ontology over consecutive versions, and the perseverance of suspicious events - are proposed to assess whether it is worthwhile upgrading when a new version is released. The indicators can be computed automatically when a new version is released and are statistically unrelated. Trend breaks in their evolution are suggestive for the possible benefit of an upgrade and their predictive power correlates well with the retrospective realism-based quality metric which forms the basis of Evolutionary Terminology Auditing.

Systematized nomenclature of medicine clinical terms (SNOMED CT) to represent computed tomography procedures

OBJECTIVE

To evaluate the ability of systematized nomenclature of medicine clinical terms (SNOMED CT) to represent computed tomography procedures in computed tomography dictionaries used in the Canadian province of Newfoundland and Labrador.

METHODS

This study was conducted in two stages. In the first stage computed tomography dictionaries were collected and consolidated to one master list. The duplicated procedure names were deleted from the list. In the second stage the unique data items from the master list were matched with the SNOMED CT concepts. Sensitivity, specificity, and positive and negative predictive values of SNOMED CT were investigated.

RESULTS

After eliminating 680 duplicate procedures from the total of 833, the study sample consisted of 153 data items. For pre-coordination, SNOMED CT had sensitivity of 56% and for post-coordination SNOMED CT had sensitivity of 98%.

CONCLUSION

Our results suggest that SNOMED CT is a valid nomenclature for representing computed tomography procedures.