Title: Medical Records and Electronic Documents : a Proposal
1Medical Records and Electronic Documents a
Proposal
- F. Laforest, A. Flory
- LISI (Information Systems Lab)
- frederique.laforest,flory_at_insa-lyon.fr
2Information 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
3Our 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
4Documents 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
5Strongly-structured vs Weakly-structured
ltpatient id245gt ltnamegt Dupont lt/namegt
ltfirst_namegt Henri lt/first_namegt ltprescriptiongt
Give ltdosegt 3 lt/dosegt ltdose_unitgt pills
lt/dose_unitgt of ltmedication id12gt aspirin
lt/medicationgt ltfrequencygt 3 lt/frequencygt times a
day during ltdurationgt 10 lt/durationgt
ltduration_unitgt days lt/ duration_unit gt
lt/prescriptiongtlt/patientgt
ltpatient id245gt ltnamegt Dupont lt/namegt
ltfirst_namegt Henri lt/first_namegt ltprescriptiongt
Give 3 pills of aspirin 3 times a day during 10
days lt/prescriptiongtlt/patientgt
6Prototype Weakly-Structured Documents
7Prototype Strongly-Structured Documents
8A 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
9Mapping rules - Introduction
- Based on
- standard ways of writing gt patterns to recognize
- thesauri and classifications of the application
domain - Light example
- duration of a prescription search patterns lt
during x days gt or lt during x months gt - The pattern lt during (number) (duration
units)gt
10Mapping 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 durationExpr1durationExpr2
- Expression level
- pattern itself and database filling
- durationExpr1 duringWordsList,
NumberListPosology.duration,
UnitsListPosology.durUnit
11Mapping rules - Format
- rule
- ruleName segmentName option? , segmentName
option? - segment
- segmentName expressionName
expressionName - expression
- expressionName (thesaurustable.attribut)
thesaurus , (thesaurustable.attribut)thesau
rus - option ? //for optional segments
12Architecture of our System
13Conclusion 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