IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Improving the Usability of HL7 Information Modelsby Automatic Filtering
Antonio Villegas1 Antoni Olive1 Josep Vilalta2
1Services and Information Systems Engineering DepartmentUniversitat Politecnica de Catalunya
2HL7 Education & e-Learning ServicesHL7 Spain (Health Level Seven International)
ESSI SeminarJune 2, 2010
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Outline1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)2 Health Level Seven
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
3 Filtering HL7 ModelsOverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
4 EvaluationPrecision AnalysisTime Analysis
5 ConclusionsVillegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 2/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
Outline1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)2 Health Level Seven
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
3 Filtering HL7 ModelsOverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
4 EvaluationPrecision AnalysisTime Analysis
5 ConclusionsVillegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 3/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
Introduction
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 4/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
IEEE 6th World Congress on Services (SERVICES 2010)www.servicescongress.org/2010 July 5–10 Miami USA
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
IEEE 6th World Congress on Services (SERVICES 2010)www.servicescongress.org/2010 July 5–10 Miami USA
Business services sectors:
Advertising ServicesBanking ServicesBroadcasting & Cable TV ServicesBusiness ServicesCasinos & Gaming ServicesCommunications ServicesCross-industry ServicesDesign Automation ServicesEnergy and Utilities ServicesFinancial ServicesGovernment ServicesHealthcare ServicesHotels & Motels ServicesInsurance ServicesInternet Services
Motion Pictures ServicesPersonal ServicesPrinting & Publishing ServicesReal Estate Operations ServicesRecreational Activities ServicesRental & Leasing ServicesRestaurants ServicesRetail ServicesSchools and Education ServicesSecurity Systems & ServicesTechnology ServicesTravel and Transportation ServicesWaste Management ServicesWholesale Distribution Services
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 6/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
IEEE 6th World Congress on Services (SERVICES 2010)www.servicescongress.org/2010 July 5–10 Miami USA
Business services sectors:
Advertising ServicesBanking ServicesBroadcasting & Cable TV ServicesBusiness ServicesCasinos & Gaming ServicesCommunications ServicesCross-industry ServicesDesign Automation ServicesEnergy and Utilities ServicesFinancial ServicesGovernment ServicesHealthcare ServicesHotels & Motels ServicesInsurance ServicesInternet Services
Motion Pictures ServicesPersonal ServicesPrinting & Publishing ServicesReal Estate Operations ServicesRecreational Activities ServicesRental & Leasing ServicesRestaurants ServicesRetail ServicesSchools and Education ServicesSecurity Systems & ServicesTechnology ServicesTravel and Transportation ServicesWaste Management ServicesWholesale Distribution Services
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Outline1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)2 Health Level Seven
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
3 Filtering HL7 ModelsOverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
4 EvaluationPrecision AnalysisTime Analysis
5 ConclusionsVillegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 7/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesExample?
? pictures from hl7.org
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesExample?
? pictures from hl7.org
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesExample?
? pictures from hl7.org
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesExample?
? pictures from hl7.org
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesExample?
? pictures from hl7.org
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesExample?
? pictures from hl7.org
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare Services
Key challenges faced by healthcare organizations today include:
Impact on the safety, effectiveness, and cost of healthcare bynot having the right information at the right place at theright time.
Presentation of disparate healthcare information at thepoint of treatment
Increased cost in transferring paper records in this age ofe-commerce
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesMotivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesMotivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 10/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesMotivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Healthcare ServicesMotivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 10/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Health Level Seven
The Health Level Seven International (HL7) is a not-for profit,ANSI-accredited standards developing organization dedicated toproviding a comprehensive framework and related standards for theexchange, integration, sharing, and retrieval of electronic healthinformation that supports clinical practice and the management, deliveryand evaluation of health services.
HL7 develops specifications, the most widely used being a messagingstandard that enables disparate healthcare applications to exchange keysets of clinical and administrative data.
The HL7 standard specifications are unified by shared referencemodels of the healthcare and technical domains.
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Health Level Seven
The Health Level Seven International (HL7) is a not-for profit,ANSI-accredited standards developing organization dedicated toproviding a comprehensive framework and related standards for theexchange, integration, sharing, and retrieval of electronic healthinformation that supports clinical practice and the management, deliveryand evaluation of health services.
HL7 develops specifications, the most widely used being a messagingstandard that enables disparate healthcare applications to exchange keysets of clinical and administrative data.
The HL7 standard specifications are unified by shared referencemodels of the healthcare and technical domains.
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Health Level Seven
The Health Level Seven International (HL7) is a not-for profit,ANSI-accredited standards developing organization dedicated toproviding a comprehensive framework and related standards for theexchange, integration, sharing, and retrieval of electronic healthinformation that supports clinical practice and the management, deliveryand evaluation of health services.
HL7 develops specifications, the most widely used being a messagingstandard that enables disparate healthcare applications to exchange keysets of clinical and administrative data.
The HL7 standard specifications are unified by shared referencemodels of the healthcare and technical domains.
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models Overview
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models Overview
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models Overview
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models Overview
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models OverviewRefinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIMclasses are refined in the subclasses.The multiplicities of an association defined between RIM classesare strengthened in the subclasses.The multiplicity of an attribute of a RIM class is strengthened ina subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models OverviewRefinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIMclasses are refined in the subclasses.The multiplicities of an association defined between RIM classesare strengthened in the subclasses.The multiplicity of an attribute of a RIM class is strengthened ina subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models OverviewRefinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIMclasses are refined in the subclasses.The multiplicities of an association defined between RIM classesare strengthened in the subclasses.The multiplicity of an attribute of a RIM class is strengthened ina subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 13/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models OverviewRefinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIMclasses are refined in the subclasses.The multiplicities of an association defined between RIM classesare strengthened in the subclasses.The multiplicity of an attribute of a RIM class is strengthened ina subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 13/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Models OverviewRefinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIMclasses are refined in the subclasses.The multiplicities of an association defined between RIM classesare strengthened in the subclasses.The multiplicity of an attribute of a RIM class is strengthened ina subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Information Model (RIM)
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Reference Information Model (RIM)
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Domain Message Information Model (D-MIM)HL7 Domains
Administrative ManagementAccount and BillingClaims & ReimbursementPatient AdministrationMaterials ManagementPersonnel ManagementSchedulingBlood BankCare ProvisionClinical Decision SupportClinical Document Architecture
Clinical GenomicsDiagnostic ImagingLaboratoryOrders and ObservationsMedical RecordsMedicationPharmacyPublic HealthRegulated ProductsRegulated StudiesSpecimen DomainTherapeutic Devices
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Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Domain Message Information Model (D-MIM)Scheduling Domain
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Refined Message Information Model (R-MIM)
The R-MIM is a subset of a D-MIM that is used to express theinformation content for a message/document or set ofmessages/documents with annotations and refinements that aremessage/document specific.
The content of an R-MIM is drawn from the D-MIM for thespecific domain in which the R-MIM is used.
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Refined Message Information Model (R-MIM)Full Appointment R-MIM
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Filtering HL7 ModelsEvaluation
Conclusions
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
Interchanging Messages
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Outline1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)2 Health Level Seven
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
3 Filtering HL7 ModelsOverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
4 EvaluationPrecision AnalysisTime Analysis
5 ConclusionsVillegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 20/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Filtering HL7 Models
Objective
Automatically provide a filtered information model of the wholeHL7 models according to the user preferences.
Filtered Information Model
A small information model that focus on the knowledge of theuser’s request.Its reduced size and self-contained aspect make it easier to the userthe comprehension and understandability of the focused knowledge.
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Filtering HL7 Models
Objective
Automatically provide a filtered information model of the wholeHL7 models according to the user preferences.
Filtered Information Model
A small information model that focus on the knowledge of theuser’s request.Its reduced size and self-contained aspect make it easier to the userthe comprehension and understandability of the focused knowledge.
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Method Overview
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Method Overview
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 1: Setting the User Preferences
The user selects:
Focus Set (FS)
a non-empty set of classes the user is interested in.
Rejection Set (RS)
an optional set with those classes that have no interest to the user.
Filter Size (Cmax)
the amount of additional classes the user wants to obtain.
Example
FS = Patient,ActAppointment and RS = ∅ and Cmax = 12
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 1: Setting the User Preferences
The user selects:
Focus Set (FS)
a non-empty set of classes the user is interested in.
Rejection Set (RS)
an optional set with those classes that have no interest to the user.
Filter Size (Cmax)
the amount of additional classes the user wants to obtain.
Example
FS = Patient,ActAppointment and RS = ∅ and Cmax = 12
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 1: Setting the User Preferences
The user selects:
Focus Set (FS)
a non-empty set of classes the user is interested in.
Rejection Set (RS)
an optional set with those classes that have no interest to the user.
Filter Size (Cmax)
the amount of additional classes the user wants to obtain.
Example
FS = Patient,ActAppointment and RS = ∅ and Cmax = 12
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 1: Setting the User Preferences
The user selects:
Focus Set (FS)
a non-empty set of classes the user is interested in.
Rejection Set (RS)
an optional set with those classes that have no interest to the user.
Filter Size (Cmax)
the amount of additional classes the user wants to obtain.
Example
FS = Patient,ActAppointment and RS = ∅ and Cmax = 12
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Method Overview
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresImportance (Ψ)
Definition (Importance (Ψ))
The importance Ψ(c) of a class c is a real number that measuresthe relative importance of that class in a model.
Methods 1
Occurrence Counting
Link Analysis
Instance-dependent1 On Computing the Importance of Entity Types in Large Conceptual Schemas. Villegas, A. and Olive, A. ER 2009.
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OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresImportance (Ψ)
Definition (Importance (Ψ))
The importance Ψ(c) of a class c is a real number that measuresthe relative importance of that class in a model.
Methods 1
Occurrence Counting
Link Analysis
Instance-dependent1 On Computing the Importance of Entity Types in Large Conceptual Schemas. Villegas, A. and Olive, A. ER 2009.
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresImportance (Ψ)
Top-10 Most Important Classes.
Rank Class Importance Ψ
1 Act 7.51
2 Role 5.11
3 ActRelationship 4.03
4 Participation 3.67
5 Entity 3.5
6 Observation 2.64
7 InfrastructureRoot 1.81
8 Organization 1.72
9 RoleLink 1.59
10 FinancialTransaction 1.54
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OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
The importance problem
The importance of a class is an absolute metric that depends onlyon the whole set of HL7 models.
The metric is useful when a user wants to know which are themost important classes, but it is of little use when the user isinterested in a specific subset of classes, independently from theirimportance.
What is needed then is a metric that measures the interest of aclass with respect to the focus set.
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresCloseness (Ω)
There may be several ways to compute the closeness Ω(c,FS) of aclass c with respect to the classes of FS.
Intuitively, the closeness of class c should be directly related to theinverse of the distance of c to the focus set FS.
Ω(c,FS) =|FS|∑
c′∈FSd(c, c′)
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresCloseness (Ω)
There may be several ways to compute the closeness Ω(c,FS) of aclass c with respect to the classes of FS.
Intuitively, the closeness of class c should be directly related to theinverse of the distance of c to the focus set FS.
Ω(c,FS) =|FS|∑
c′∈FSd(c, c′)
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IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresCloseness (Ω)
Intuitively, the closeness of class c should be directly related to theinverse of the distance of c to the focus set FS.
Ω(c,FS) =|FS|∑
c′∈FSd(c, c′)
number of classes in FS
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresCloseness (Ω)
Intuitively, the closeness of class c should be directly related to theinverse of the distance of c to the focus set FS.
Ω(c,FS) =|FS|∑
c′∈FSd(c, c′)
minimum distance between c and c′ ∈ FS
c and c′ directly connected → d(c, c′) = 1
otherwise → d(c, c′) = length of the shortest path between them
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresCloseness (Ω)
Intuitively, the closeness of class c should be directly related to theinverse of the distance of c to the focus set FS.
Ω(c,FS) =|FS|∑
c′∈FSd(c, c′)
minimum distance between c and c′ ∈ FS
c and c′ directly connected → d(c, c′) = 1
otherwise → d(c, c′) = length of the shortest path between them
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
Definition (Interest Ψ)
The interest Φ(c,FS) of a class c with respect to a focus set FSis a combination of the importance of c and its closeness to FS.
Φ(c,FS) = α×Ψ(c) + (1− α)× Ω(c,FS)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 37/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
Definition (Interest Ψ)
The interest Φ(c,FS) of a class c with respect to a focus set FSis a combination of the importance of c and its closeness to FS.
Φ(c,FS) = α×Ψ(c) + (1− α)× Ω(c,FS)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 37/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
Definition (Interest Ψ)
The interest Φ(c,FS) of a class c with respect to a focus set FSis a combination of the importance of c and its closeness to FS.
Φ(c,FS) = α×Ψ(c) + (1− α)× Ω(c,FS)
Component of Importance
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 38/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
Definition (Interest Ψ)
The interest Φ(c,FS) of a class c with respect to a focus set FSis a combination of the importance of c and its closeness to FS.
Φ(c,FS) = α×Ψ(c) + (1− α)× Ω(c,FS)
Component of Closeness
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 39/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
Definition (Interest Ψ)
The interest Φ(c,FS) of a class c with respect to a focus set FSis a combination of the importance of c and its closeness to FS.
Φ(c,FS) = α ×Ψ(c) + ( 1− α )× Ω(c,FS)
Balancing Parameter (default α = 0.5)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 40/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
Closeness Ω
Impo
rtan
ceΨ
r
r′
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 41/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 2: Compute Filtering MeasuresInterest (Φ)
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1P1
P2
P3P4
P5
P6P7
P8
Closeness Ω
Impo
rtan
ceΨ
r′
dist
(P6, r
′ )di
st(P
2, r
′ )Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 41/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Method Overview
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 42/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 3: Select Interest Set
Select the top classes of the ranking produced by the computation of theinterest Φ s.t. |Interest Set| = Cmax − |FS|.
Most Interesting classes with regard to FS = Patient, ActAppointment.
Rank Class (c)Importance Distance Distance Closeness Interest
Ψ(c) d(c, Patient) d(c, ActAppointment) Ω(c,FS) Φ(c,FS)
1 Organization 1.72 1 3 0.5 1.11
2 Person 1.22 1 3 0.5 0.86
3 ServiceDeliveryLocation 0.79 2 2 0.5 0.65
4 AssignedPerson 0.72 2 2 0.5 0.61
5 SubjectOfActAppointment 0.11 1 1 1.0 0.56
6 ManufacturedDevice 0.55 2 2 0.5 0.53
7 LocationOfActAppointment 0.26 3 1 0.5 0.38
8 ReusableDeviceOfActAppointment 0.19 3 1 0.5 0.35
9 SubjectOfAccountEvent 0.13 1 3 0.5 0.32
10 AuthorOfActAppointment 0.12 3 1 0.5 0.31
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 43/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 3: Select Interest Set
Select the top classes of the ranking produced by the computation of theinterest Φ s.t. |Interest Set| = Cmax − |FS|.
Most Interesting classes with regard to FS = Patient, ActAppointment.
Rank Class (c)Importance Distance Distance Closeness Interest
Ψ(c) d(c, Patient) d(c, ActAppointment) Ω(c,FS) Φ(c,FS)
1 Organization 1.72 1 3 0.5 1.11
2 Person 1.22 1 3 0.5 0.86
3 ServiceDeliveryLocation 0.79 2 2 0.5 0.65
4 AssignedPerson 0.72 2 2 0.5 0.61
5 SubjectOfActAppointment 0.11 1 1 1.0 0.56
6 ManufacturedDevice 0.55 2 2 0.5 0.53
7 LocationOfActAppointment 0.26 3 1 0.5 0.38
8 ReusableDeviceOfActAppointment 0.19 3 1 0.5 0.35
9 SubjectOfAccountEvent 0.13 1 3 0.5 0.32
10 AuthorOfActAppointment 0.12 3 1 0.5 0.31
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 43/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Method Overview
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 44/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
ClassesFS ClassesInterest Set ClassesAuxiliary Classes
AssociationsParticipant Classes are in FIMParticipant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization RelationshipsSuperclass and subclass are in FIMIndirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 45/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
ClassesFS ClassesInterest Set ClassesAuxiliary Classes
AssociationsParticipant Classes are in FIMParticipant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization RelationshipsSuperclass and subclass are in FIMIndirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 45/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
ClassesFS ClassesInterest Set ClassesAuxiliary Classes
AssociationsParticipant Classes are in FIMParticipant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization RelationshipsSuperclass and subclass are in FIMIndirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 45/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
ClassesFS ClassesInterest Set ClassesAuxiliary Classes
AssociationsParticipant Classes are in FIMParticipant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization RelationshipsSuperclass and subclass are in FIMIndirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 45/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
ClassesFS ClassesInterest Set ClassesAuxiliary Classes
AssociationsParticipant Classes are in FIMParticipant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization RelationshipsSuperclass and subclass are in FIMIndirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 45/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
ClassesFS ClassesInterest Set ClassesAuxiliary Classes
AssociationsParticipant Classes are in FIMParticipant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization RelationshipsSuperclass and subclass are in FIMIndirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 45/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information ModelProjection of Association
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 46/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information ModelProjection of Association
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 46/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information ModelProjection of Association
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 46/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information ModelGeneralization-Specialization Relationships
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 47/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information ModelGeneralization-Specialization Relationships
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 47/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
OverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
Step 4: Compute Filtered Information Model
FS = Patient, ActAppointment and Cmax = 12.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 48/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Outline1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)2 Health Level Seven
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
3 Filtering HL7 ModelsOverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
4 EvaluationPrecision AnalysisTime Analysis
5 ConclusionsVillegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 49/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Evaluation
To find a measure that reflects the ability of our method to satisfythe user is a complicated task.
However, there exists measurable quantities in the field ofinformation retrieval that can be applied to our context:
The ability of the method to withhold non-relevant knowledge(precision)
The interval between the request being made and the answerbeing given (time)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 50/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Evaluation
To find a measure that reflects the ability of our method to satisfythe user is a complicated task.
However, there exists measurable quantities in the field ofinformation retrieval that can be applied to our context:
The ability of the method to withhold non-relevant knowledge(precision)
The interval between the request being made and the answerbeing given (time)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 50/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Evaluation
To find a measure that reflects the ability of our method to satisfythe user is a complicated task.
However, there exists measurable quantities in the field ofinformation retrieval that can be applied to our context:
The ability of the method to withhold non-relevant knowledge(precision)
The interval between the request being made and the answerbeing given (time)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 50/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Evaluation
To find a measure that reflects the ability of our method to satisfythe user is a complicated task.
However, there exists measurable quantities in the field ofinformation retrieval that can be applied to our context:
The ability of the method to withhold non-relevant knowledge(precision)
The interval between the request being made and the answerbeing given (time)
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 50/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
The precision of a method is defined as the percentage of relevantknowledge presented to the user.
We use the concept of precision applied to HL7 universal domains(specified with D-MIM’s).
Precision =|relevant classes| ∩ |retrieved classes|
|retrieved classes|
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 51/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
The precision of a method is defined as the percentage of relevantknowledge presented to the user.
We use the concept of precision applied to HL7 universal domains(specified with D-MIM’s).
Precision =|relevant classes| ∩ |retrieved classes|
|retrieved classes|
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 51/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
Each domain contains a main class which is the central point ofknowledge to the users interested in such domain. The otherclasses presented in the domain conform the relevant knowledgerelated to the main class.
A common situation for a user is to focus on the main class of adomain and to navigate through the D-MIM to understand itsrelated knowledge.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 52/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 53/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 53/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
We simulate the generation of a D-MIM from its main class.
Initialization
FS = main class of the D-MIMCmax = number of classes of the D-MIM
This way, we will obtain a filtered information model with the same number ofclasses as such domain.
Following Iterations
FS = main class of the D-MIMCmax = number of classes of the D-MIMRS = includes non-relevant classes retrieved in the previous iteration
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 54/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
We simulate the generation of a D-MIM from its main class.
Initialization
FS = main class of the D-MIMCmax = number of classes of the D-MIM
This way, we will obtain a filtered information model with the same number ofclasses as such domain.
Following Iterations
FS = main class of the D-MIMCmax = number of classes of the D-MIMRS = includes non-relevant classes retrieved in the previous iteration
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 54/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
0 5 10 15 20 25 30
4050
6070
8090
100
Precision
Iterations
Prec
isio
n (%
)
Medical RecordsSchedulingAccount and BillingLaboratory
1 2 3 4 5
4050
6070
8090
100
Precision (Zoom Iterations 1−5)
Iterations
Prec
isio
n (%
)
Medical RecordsSchedulingAccount and BillingLaboratory
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 55/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
0 5 10 15 20 25 30
4050
6070
8090
100
Precision
Iterations
Prec
isio
n (%
)
Medical RecordsSchedulingAccount and BillingLaboratory
1 2 3 4 5
4050
6070
8090
100
Precision (Zoom Iterations 1−5)
Iterations
Prec
isio
n (%
)
Medical RecordsSchedulingAccount and BillingLaboratory
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 55/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Precision Analysis
The test reveals that to reach more than 80% of the relevantclasses of a domain, only three iterations are required.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 56/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
A good method does not only require precision, but it also needsto present the results in an acceptable time according to the user.
Test
Record the time lapse between the request of knowledge, i.e. oncea focus set FS has been indicated by the user, and the receipt ofthe filtered information model.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 57/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
A good method does not only require precision, but it also needsto present the results in an acceptable time according to the user.
Test
Record the time lapse between the request of knowledge, i.e. oncea focus set FS has been indicated by the user, and the receipt ofthe filtered information model.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 57/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
It is expected that as we increase the size of the focus set, the timewill increase linearly.
Reason
Our method computes the distances from each class in the focusset to all the rest of classes. This computation requires the sametime (in average) for each class in the focus set.
Therefore, the more classes we have in a focus set, the more thetime our method spends in computing distances.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 58/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
It is expected that as we increase the size of the focus set, the timewill increase linearly.
Reason
Our method computes the distances from each class in the focusset to all the rest of classes. This computation requires the sametime (in average) for each class in the focus set.
Therefore, the more classes we have in a focus set, the more thetime our method spends in computing distances.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 58/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
Average Time
Focus Set Size
Tim
e (s
)
0 5 10 15 20 25 30 35 40
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 59/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
According to the expected use of our method, having a focus setFS of 40 classes is not a common situation (although possible).
Sizes of focus set up to 10 classes are more realistic, in which casethe average time does not exceed one second.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 60/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Precision AnalysisTime Analysis
Time Analysis
According to the expected use of our method, having a focus setFS of 40 classes is not a common situation (although possible).
Sizes of focus set up to 10 classes are more realistic, in which casethe average time does not exceed one second.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 60/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Outline1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)2 Health Level Seven
Healthcare ServicesReference Models OverviewRIMD-MIMR-MIM
3 Filtering HL7 ModelsOverviewUser PreferencesFiltering MeasuresInterest SetFiltered Information Model
4 EvaluationPrecision AnalysisTime Analysis
5 ConclusionsVillegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 61/ 63
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Filtering HL7 ModelsEvaluation
Conclusions
Conclusions
HL7 information models are very large. The wealth of knowledge theycontain makes them very useful to their potential target audience.
However, the size and the organization of these models make it difficult tomanually extract knowledge from them. This task is basic for theimprovement of services provided by HL7 affiliates, vendors and otherorganizations that use those models for the development of health systems.
What is needed is a tool that improves the usability of HL7 models for thattask. Ours selects the most interesting classes from those models, includingtheir defined knowledge in the original models.
The experiments show that our prototype tool recovers morethan 80% of the knowledge of a D-MIM in three iterations, withan average time per iteration that for common uses does notexceed one second.
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 62/ 63
IntroductionHealth Level Seven
Filtering HL7 ModelsEvaluation
Conclusions
Improving the Usability of HL7 Information Modelsby Automatic Filtering
Antonio Villegas1 Antoni Olive1 Josep Vilalta2
1Services and Information Systems Engineering DepartmentUniversitat Politecnica de Catalunya
2HL7 Education & e-Learning ServicesHL7 Spain (Health Level Seven International)
ESSI SeminarJune 2, 2010
Villegas A., Olive A., Vilalta J. Improving the Usability of HL7 Models by Filtering 63/ 63