Development of an EHR System for Sharing - a Semantic Perspective , - - PowerPoint PPT Presentation

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Development of an EHR System for Sharing - a Semantic Perspective , - - PowerPoint PPT Presentation

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Development of an EHR System for Sharing - a Semantic Perspective

Haifeng LIU, Xue Qiao HOU, Gang HU, Jing LI and Yu Qi DING 2009-08-31 I&K Department IBM China Research Lab.

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Content

  • ! Motivation
  • ! CDA-based Approach
  • ! System Architecture
  • ! Example demo
  • ! Conclusion
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Motivation and Goals

  • ! Being able to perform semantic search is particularly crucial for

clinical workers and researchers, as they often need to search for the descriptions of a specific clinical finding from a large volume

  • f clinical documents while keywords search will often return

incomplete search results or too many unrelated information.

  • ! The goal is to demonstrate the power of leveraging semantic

technology to enable semantic search with an EHR system.

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Technical Approach: Modeling Medical Records for Interoperability

What is “interoperability”

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HL7 CDA : Clinical Document Architecture

Header Body Section

Narrative Clinical Statement

CDA Entry CDA Entry CDA Entry

CDA Document

code code code

  • !XML based architecture for clinical

document (Flexible and extensible)

  • !Incremental semantic interoperability
  • ! Level One
  • !The unconstrained CDA specification.
  • ! Level Two
  • !The CDA specification with section-level templates

applied.

  • ! Level Three
  • !The CDA specification with entry-level templates

applied.

  • !Semantic annotation is key to enable interoperability

and analytics

Any to CDA Transforma tion Documents Exchange Schema Structurali ze Terminolog y Formal Semantics Semantic Annotation Level One

 

Level Two

  *

Level Three

     

Incremental level of semantics

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Semantic Annotation (CDA Anchor)

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HL7 CDA based EMR/EHR data model and application

IBM Clinical Terminology Annotator

Semantic Interoperability

EHR/ EMR

Entry

section Header Body

Text

HL7 CD HL7 CDA R2 A R2

  • Dr. George
  • Dr. Smith

Patient Mr. Joe Content profiles

Scenario: (1) Mr. Joe fractured his left arm, and go to see the Dr. George from Hospital A. (2) Dr. George gave Mr. Joe a treatment for two weeks. He wrote some medical records in the EMR system (3) This EMR system can transfer the text into HL7 CDA format, and annotated by SNOMED CT. (4) Two weeks later, Mr. Joe was introduced to Dr. Smith for more

  • rehabilitation. Dr. Smith can read

those EMR documents.

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System architecture

Semantic search engine EHR repository Web user interface Reasoner Publisher EHR registry RDF Indexer CDA RDF triples RDF triples Clinical terminologies RDF triples Search request Results Document metadata Document IDs Document IDs Document information Search request CDA level 3 documents CDA Level 3 generator

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…… ……

<component> <section> <templateId root="1.3.6.1.4.1.19376.1.5.3.1.1.13.2.1" /> <id root="TEST" extension="2.16.840.1.113883.3.933"/> <code code=“10154-3” , displayName=‘History of present illness’, codeSystem="2.16.840.1.113883.6.1“ systemName="LOINC" /> <title> History of present illness </title> <text> 缘患者近期情绪不稳,时常发怒,于今年2月1日晚上约8: 00PM与家人发生争执,情绪激动,约9:15PM家人发现患者昏坐于 椅,呼之不应,当时无肢体抽搐、呕吐,遂呼120出车,当时查患者呈 浅昏迷状态,小便失禁,测血压为210/100mmHg,予吸氧,含服心 痛定、静滴硝酸甘油降压,送我院急诊就诊,急查头颅CT口头报告: 左侧基底节脑出血,出血量约20ml。急诊生化、血常规、凝血三项 正常。脑外科会诊指示暂时内科治疗。经予利喜定、醒脑静、清开 灵、七叶皂甙钠、甘露醇等药物治疗后,患者于今晨约7:00AM苏 醒,右侧肢体乏力,不能上抬,言语不清,对答欠合理,为进一步 治疗,收入我区。 有高血压病史4年,间中服复方罗布麻片降压,血 压控制不详。 入院症见:神志清,烦躁,反应迟钝,对答欠合理, 面色偏红,言语不清,右侧肢体乏力,伴头痛,停留尿管、胃管, 大便今日未解,口干。 无头晕、恶心欲呕、肢体抽搐、胸闷,无发 热、咳嗽、咯痰。 </text> </section> </component>

… … … …

CD CDA instance A instance

CDA (L3) Generator

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Core technique:Localized terminology annotator

Terminology repository

Terminology 1

IBM terminology annotator

Terminology 2 Terminology 3 Terminology 4

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Publisher

1) We first employ the publisher to internally publish the coded CDA entries into RDF

  • triples. Figure below shows an example of publishing RDF triples from a CDA 3

fragment of document “D00028” which represents an observation of “weakness of limb was found in entire limb".

<observation classCode="OBS" moodCode="EVN"> <code code="309774006" displayName="Weakness of limb" codeSystem="2.16.840.1.113883.6.96" codeSystemName="SNOMED CT"/> <qualifier> <name code="363698007" displayName="Finding site (attribute)"/> <value> <code code="243996003" displayName="Entire limb" codeSystem="2.16.840.1.113883.6.96" codeSystemName="SNOMED CT" /> </value> </qualifier> </observation>

  • !

CDA coded entry triples: <Weakness_of_limb/309774006> <findingSite> <Entire_limb/243996003>

  • !

Default context-value triples for SNOMED CT concept “weakness of limb”: <Weakness_of_limb/309774006><findingContext> Known_present/410515003> <Weakness_of_limb/309774006><temporalContext><Current/ 410512000> <Weakness_of_limb/309774006><subjectRelationshipContext><Subject_of_record/ 410604004>

  • !

Additional triples to associate the observation with a document: <D00028> <hasObservation> < Weakness_of_limb/309774006>

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Reasoner

2) The reasoner performs reasoning over the existing RDF triples fed by the publisher together with the triples from the referenced terminology repository, thus expands the RDF triples that could be harnessed by the RDF indexer. For example, as illustrated in Figure below, relying on the content related to “Weakness of limb” of the referenced terminology SNOMED CT, the reasoner is able to generate four more triples associated with document “D00028” from the published triple “Weakness_of_limb/309774006> <findingSite> <Entire_limb/243996003>” in the previous step.

<Weakness_of_limb/309774006> <isA> <Muscle_weakness/26544005> <Weakness_of_limb/309774006> <isA> <Finding_of_limb_structure/302293008> <Weakness_of_limb/309774006> <findingSite> <Skeletal_muscle_structure/127954009> <Weakness_of_limb/309774006> <findingSite> <Limb_structure/66019005>

Muscle weakness

Finding of limb structure Weakness of limb Skeletal muscle structure Limb structure isA isA findingSite findingSite SNOMED CT

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Indexer

3)! The RDF indexer adopts existing information retrieval (IR) structures and functions to index the RDF triples acquired in step 2), and supports efficient and scalable search capability. Based

  • n a classical IR inverted index structure, the semantic search

engine can answer queries about instances of a semantic concept (e.g., weakness of limb) and a semantic relation (e.g., findingSite), and relates the results with the specific documents using document-centric triples published in step 1).

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Performing search

3)! Users submit their search requests through the Web interface where users start from typing in a keyword related to a clinical concept, and repeatedly add in multiple relation and concept constraints to form a semantic pattern which explicitly describes the question they want to ask. The pattern can later help the search engine return complete and meaningful results by automatically expanding the search with related clinical concepts that can be inferred from the question. 4)! The semantic search engine performs the search against the index files according to the user input, and feeds the resulted document IDs satisfying the search criteria into the EHR registry. The registry then returns a list of corresponding documents information to the search engine which in turn forwards it to the user interface. Users can further retrieve the specific documents of interest from EHR repository through the interface.

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Example demo

User can start from a key word to find the core clinical concept

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Return the candidate concepts that either contain the keyword or can be inferred from the matched concepts, at the same time, they are all founded in the document stored in repository. The candidates are sorted by concept subsumption relationship.

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Choose the concept “Edema” as search condition to narrow down the search scope.

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User can also use related concepts to further articulate the search, for exmaple, choose the “Finding context” relationship, and set the range as “Known present”

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To formulate a semantic pattern, multiple relationship and concept constraints can be added by repeating above steps.

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For example, user can add another constraint to only search the “Finding site” of “Edema” is at the “Dorsum of hand”.

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And, further articulate it as the left side of the “Dorsum of hand” via “Laterality” relationship.

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The result documents are filtered with more and more constraints added in this search.

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User can browse the resulted document by clicking the link.

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Here is the targeted CDA entry of the finding user searched.

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Conclusion

  • ! The prototype system was developed for GDHTCM (the largest

hospital in southwest of China) and achieved the satisfactory comments from the preliminary testing.

  • ! The system
  • ! Enables users to search the most related information via defining a semantic

pattern, which explicitly describes the question they want to ask.

  • ! Returns more complete and meaningful results by automatically expanding

the search with related clinical concepts that can be inferred from the question

  • ! Provides a scalable engine that can co-work with Semantic Annotator and

deal with large volume of unstructured medical records or HL7 CDA Level 3 documents.

  • ! Future (ongoing) work:
  • !

develop regional EHR systems for the four subordinate hospitals of GDHTCM in next phase, and implement semantic interoperability among them.

  • !

consider integrating the developed EHR system with a knowledge-centric clinical decision support system.

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Thanks! Q&A