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Searching 
SNOMED 
CT 
Brandon 
Ulrich 
– 
B2i 
Healthcare 
Reymond 
Wilaisono 
– 
Ministry 
of 
Health 
Holdings, 
Singap...
Main 
challenge: 
Simplicity 
versus 
expressivity 
• Simplicity 
and 
usability 
– Keep 
primary 
search 
interface 
as 
...
Demo 
• Quick 
search 
– Generally-­‐accepted 
search 
principles 
– More 
controversial 
choices 
• SemanOc 
search 
– ES...
Demo 
screenshots 
The 
following 
slides 
contain 
sample 
screenshots 
from 
the 
live 
demonstraOon. 
October 
26, 
201...
Generally-­‐agreed 
SNOMED 
CT 
search 
principles 
• Progressive 
matching 
• Highlight 
matching 
terms 
• Prefix 
match...
Progressive 
matching 
across 
all 
terminologies 
October 
26, 
2012
Highlight 
matching 
terms 
October 
26, 
2012
Prefix 
matching 
October 
26, 
2012
Implicit 
AND 
October 
26, 
2012
Strip 
accents 
and 
punctuaOon 
October 
26, 
2012
Visually 
idenOfy 
top-­‐level 
concepts 
October 
26, 
2012
Previous 
choices 
October 
26, 
2012
Bookmarks 
October 
26, 
2012
Concept 
model 
October 
26, 
2012
Search 
Profile 
Default 
search 
profile 
Clinician 
search 
profile 
October 
26, 
2012
Display 
a 
single 
preferred 
term 
per 
concept 
October 
26, 
2012
AutocompleOon 
October 
26, 
2012
AutomaOc 
query 
expansion 
October 
26, 
2012
SemanOc 
queries 
ESCG: 
Extended 
SNOMED 
ComposiOonal 
Grammar 
• ~HL7 
TermInfo 
with 
support 
for 
reference 
sets 
•...
Clinical 
finding 
descendants 
October 
26, 
2012
Cardiac 
valve 
findings 
October 
26, 
2012
Dog-­‐free 
cardiac 
valve 
findings 
October 
26, 
2012
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Searching SNOMED CT

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The purpose of this presentation is to understand techniques for leveraging SNOMED CT's semantics in clinical document search and analysis.

Abstract:
The availability of semantically rich electronic health records utilizing SNOMED CT as a reference terminology continues to grow, providing new opportunities to improve patient care and reduce costs. However, traditional data warehouses struggle to unleash the full semantic meaning within the health records, as the data is built around a limited number of concepts.

This presentation suggests an alternate strategy for executing meaningful queries. EHR data is represented using an information model bound to SNOMED CT terminology, where the information model is agnostic to the underlying standard (e.g. CIMI reference model, Singapores Logical Reference Model, the UKs Logical Record Architecture, HL7, openEHR). Meaningful queries can be formulated using a query language that utilizes the SNOMED CT compositional grammar for post-coordinated expressions. This allows querying on not only the concept hierarchy but also the defining relationships as well, resulting in semantically aggregated patient data. Complex queries can be executed in real-time for millions of EHRs without the need for extraction and aggregation to analytical stores. The results of the query can be further analysed using a cloud-based analytics engine.

Please see our website http://b2i.sg for further information.

Published in: Software
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Searching SNOMED CT

  1. 1. Searching SNOMED CT Brandon Ulrich – B2i Healthcare Reymond Wilaisono – Ministry of Health Holdings, Singapore October 26, 2012 10:45
  2. 2. Main challenge: Simplicity versus expressivity • Simplicity and usability – Keep primary search interface as simple as possible – Use the same interface everywhere – Concept model to reduce potenOal matches – Search profiles and usage frequencies to rank results • Advanced searches – Find concepts based on SNOMED CT’s semanOcs – Reuse searches for refsets, terminology binding, etc. October 26, 2012
  3. 3. Demo • Quick search – Generally-­‐accepted search principles – More controversial choices • SemanOc search – ESCG (Extended SNOMED ComposiOonal Grammar) / HL7 TermInfo queries October 26, 2012
  4. 4. Demo screenshots The following slides contain sample screenshots from the live demonstraOon. October 26, 2012
  5. 5. Generally-­‐agreed SNOMED CT search principles • Progressive matching • Highlight matching terms • Prefix matching • Implicit AND • Strip accents and punctuaOon • Visually idenOfy top-­‐level concept • Previous choices (history) • Bookmarks (favorites) • Concept model to constrain results • Search profile (context) October 26, 2012
  6. 6. Progressive matching across all terminologies October 26, 2012
  7. 7. Highlight matching terms October 26, 2012
  8. 8. Prefix matching October 26, 2012
  9. 9. Implicit AND October 26, 2012
  10. 10. Strip accents and punctuaOon October 26, 2012
  11. 11. Visually idenOfy top-­‐level concepts October 26, 2012
  12. 12. Previous choices October 26, 2012
  13. 13. Bookmarks October 26, 2012
  14. 14. Concept model October 26, 2012
  15. 15. Search Profile Default search profile Clinician search profile October 26, 2012
  16. 16. Display a single preferred term per concept October 26, 2012
  17. 17. AutocompleOon October 26, 2012
  18. 18. AutomaOc query expansion October 26, 2012
  19. 19. SemanOc queries ESCG: Extended SNOMED ComposiOonal Grammar • ~HL7 TermInfo with support for reference sets • AddiOonal UNION and some Boolean operators October 26, 2012
  20. 20. Clinical finding descendants October 26, 2012
  21. 21. Cardiac valve findings October 26, 2012
  22. 22. Dog-­‐free cardiac valve findings October 26, 2012

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