medinfo01

Medical Records and Electronic Documents : a Proposal: 

Medical Records and Electronic Documents : a Proposal F. Laforest, A. Flory LISI (Information Systems Lab) {frederique.laforest,flory}@insa-lyon.fr

Information Capture vs Data Computing: 

Information Capture vs Data Computing Computerised medical records main barrier: information capture slowness and rigidity compared to computing facilities Structured databases are necessary to query and compute data are associated to capture forms Electronic documents a universal way of communication due to the Web expansion provides simplicity and flexibility data units are difficult to extract

Our Proposal : DRUID: 

Our Proposal : DRUID The DRUID system we are proposing can be used in the specific case of medical records offers freedom : end-users would fill in documents with the information they want to store in a convenient order and in a more free way ensures efficiency : it allows to fill in a database quasi-automatically from document paragraphs

Documents captured in DRUID: 

Documents captured in DRUID Semi-structured documents XML language More precisely : Weakly-structured documents The end-user builds documents by including free pieces of text quoted by tags Strongly-structured documents are used for system-to-system communication A Document Type Definition (DTD) provides the list of tags allowed

Strongly-structured vs Weakly-structured: 

Strongly-structured vs Weakly-structured <patient id=‘245’> <name> Dupont </name> <first_name> Henri </first_name> <prescription> Give <dose> 3 </dose> <dose_unit> pills </dose_unit> of <medication id=‘12’> aspirin </medication> <frequency> 3 </frequency> times a day during <duration> 10 </duration> <duration_unit> days </ duration_unit > </prescription></patient> <patient id=‘245’> <name> Dupont </name> <first_name> Henri </first_name> <prescription> Give 3 pills of aspirin 3 times a day during 10 days </prescription></patient>

Prototype: Weakly-Structured Documents: 

Prototype: Weakly-Structured Documents

Prototype: Strongly-Structured Documents: 

Prototype: Strongly-Structured Documents

A Document Analyzer: 

A Document Analyzer To go automatically and transparently from the Weakly-structured document to the Strongly-structured document from the Strongly-structured document to data in the Database Using mapping rules that tell: what to find in which type of paragraph how to build the strongly structured document how to fill in the database Mapping rules are hand-written by a designer

Mapping rules - Introduction: 

Mapping rules - Introduction Based on standard ways of writing => patterns to recognize thesauri and classifications of the application domain Light example duration of a prescription : search patterns < during x days > or < during x months > The pattern: < “during” (number) (duration units)>

Mapping rules - 3 levels in a rule: 

Mapping rules - 3 levels in a rule Rule level for a paragraph, set of segments to find in it PrescriptionRule = doseSegment?, durationSegment?, drugSegment Segment level lists the different ways to write a segment durationSegment = durationExpr1|durationExpr2 Expression level pattern itself and database filling durationExpr1 = duringWordsList, NumberList:Posology.duration, UnitsList:Posology.durUnit

Mapping rules - Format: 

Mapping rules - Format rule ruleName ‘=’ segmentName option? [‘,’ segmentName option?]* segment segmentName ‘=’ expressionName [‘|’ expressionName]* expression expressionName ‘=’ (thesaurus‘:’table.attribut) | thesaurus [‘,’ (thesaurus‘:’table.attribut)|thesaurus]* option: ‘?’ //for optional segments

Architecture of our System: 

Architecture of our System

Conclusion and future works: 

Conclusion and future works DRUID weakly-structured documents for information capture data stored automatically into a database Prototype in Java and Enterprise Java Beans analyzer for prescriptions done user interface done To go further improve analyzer test on other paragraph types