Exploring outcomes of research data structured by SNOMED CT: - An exemplification based on mapping a syncope research

Steenvinkel, Camilla Lærke

4. term

Biomedical Engineering and Informatics, Master

2003

2014-01-03

Baggrund: Når klinisk information skal mappes til SNOMED CT er det vigtigt, at mapningen holdes konsistent. Men udvælgelse af SNOMED CT begreber er ikke entydig. Derfor er retningslinjer for mapning nødvendige for, at sikre konsistent mapning. Kun ganske lidt information findes inden for dette område. [1] har udviklet et sæt retningslinjer til, at mappe termer fra EPJ-templates til SNOMED CT. I dette projekt blev der foretaget et litteraturreview og der er her ikke fundet nogen publikationer vedrørende retningslinjer til, at mappe forskningsdata til SNOMED CT. Formålet med dette projekt var derfor, at undersøge om retningslinjerne udviklet af [1] kan anvendes til, at mappe et forskningsdatasæt til SNOMED CT. Og i så fald, hvilke ændringer af deres retningslinjer det kræver for, at kunne anvende deres retningslinjer til, at mappe et forskningsdatasæt. Dertil var formålet, at undersøge hvilken merværdi SNOMED CT kan tilføje til et forskningsdatasæt, som er mappet vha. dette sæt mapningsregler. Metode: Projektets undersøgelsesfelt blev undersøgt gennem en eksemplificering, hvor et forskningsdatasæt, bestående af data fra 941 synkopepatienter, blev mappet til SNOMED CT. Mapningsprocessen bestod af 3 trin: Opdeling af DEerne i grupper, mapning af hver gruppe og forfining af mapningen. Mapningsprocessen blev udført iterativt. Forfining af mapningen blev udført ud fra et sæt kvalitetskriterier, som blev anvendt til, at evaluere mapningskvaliteten og dermed sikre en konsistent mapning. Udvælgelsen af SNOMED CT begreber blev udført i henhold til [1]s retningslinjer. For de områder, som disse retningslinjer ikke dækkede blev mapningen udført i 3 trin: 1) Identificering af begreber som var mulige kandidater, 2) dannelse af overblik over muligheder og begrænsninger ved, at vælge hvert enkelt kandidatbegreb, ved at tegne begrebets definerende og kvalificerende relationer på papir og 3) uvælgelse af det bedst egnede begreb. Resultater: Datasættets DEer blev opdelt i 6 grupper. Heraf blev 2 grupper ("‘diagnoser"’ og "‘medicin"’) udvalgt og mappet til SNOMED CT. For begge grupper var det muligt, at mappe alle DEer således, at hvert DE var nedarvet fra det OE. Dermed dannede hver gruppe et subset cluster, hvor det OE var LCP af gruppens DEer. Det var ikke muligt, at finde egnede SNOMED CT begreber til, at mappe de kontekstuelle dataværdier ("‘Ja"’/Nej"’/"’Uspecificeret"’). Derfor blev disse dataværdier ikke mappet til SNOMED CT. For 1 DE var det ikke muligt, at tolke dets semantiske betydning, hvorfor dette DE ikke kunne mappes. Konklusion: SNOMED CT muliggør præsentation af forskningsdata med et valgfrit niveau af granularitet. Derudover, tilføjer stukturen i SNOMED CT flere detaljer til datasættet. Forskningsdata baseret på SNOMED CT er et skridt mod opnåelse af semantisk interoperabilitet samt effektive dataudtræk fra EPJ-systemer og dermed også et skridt mod mere effektiv translationel forskning samt forbedring af den klinisk behandling.

Background: When mapping clinical information to SNOMED CT it is important that mapping is kept consistent. But selection of SNOMED CT concepts is ambiguous. Therefore, mapping guidelines are necessary to ensure consistent mapping. Only limited instructions of how to map clinical information to SNOMED CT are available. [1] have developed a mapping guideline for mapping EHR-template terms to SNOMED CT. No research studies of how research data should be mapped to SNOMED CT were found by the literature review conducted in this project. The objective of this project was to investigate the applicability of [1]s’ mapping guideline for mapping a research dataset and to investigate how this mapping guideline should be adapted to facilitate mapping of research data. Further, it was investigated what SNOMED CT may add to a research dataset which is mapped by use of this mapping guideline. Method: Investigation was conducted by an exemplification, where a research dataset of 941 syncope patients was mapped to SNOMED CT. The mapping process involved the 3 steps: Grouping of the DEs of the dataset, mapping each group, and refinements of the mapping. Mapping was conducted by an iterative mapping process and refinements were conducted until the mapped DEs fulfilled a set of quality criteria. These quality criteria were used to evaluate the quality of mapping, thus ensure consistency. Selection of SNOMED CT concepts was conducted according to [1]s’ mapping guideline. For areas which this guideline did not cover mapping was conducted in 3 steps; 1) candidate concepts were identified. 2) An overview of the possibilities and limitations of each candidate was provided by drawing the defining- and qualifying relationships of each candidate concept. 3) The best candidate was selected. Results: The DEs of the dataset was divided into 6 groups. Of these, two groups were selected ("‘diagnoses"’ and "‘medication"’) and mapped to SNOMED CT. It was possible to map the DEs of each group to subtype descendants of the OE of each group, respectively. Thus, each group created a subset cluster where the OE was the LCP of the DEs of the group. Since it was not possible to find appropriate SNOMED CT concepts to represent the contextual data values of the dataset ("‘Yes"’/"’No"’/"’Unspecified"’) these where not mapped to SNOMED CT. 1 DE was not mapped, since it was not possible to interpret its semantic meaning. Conclusion: SNOMED CT provides representation of research data with optional level of granularity. Further, SNOMED CT adds more details to the dataset. SNOMED CT based research data is one step towards semantic interoperability and efficient data extraction from EHR-systems, thus one step towards efficient, high-quality translational research and improved outcome of the clinical care process.

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