Automatically provide a filtered information model of the whole HL7 models according to the user preferences.

1. Introduction
Health Level Seven
Filtering HL7 Models
Evaluation
Conclusions
Improving the Usability of HL7 Information Models
by Automatic Filtering
Antonio Villegas1 Antoni Oliv´1
e Josep Vilalta2
1 Services and Information Systems Engineering Department
Universitat Polit`cnica de Catalunya
e
2 HL7 Education & e-Learning Services
HL7 Spain (Health Level Seven International)
ESSI Seminar
June 2, 2010
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2. Introduction
Health Level Seven
Filtering HL7 Models
Evaluation
Conclusions
Outline
1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)
2 Health Level Seven
Healthcare Services
Reference Models Overview
RIM
D-MIM
R-MIM
3 Filtering HL7 Models
Overview
User Preferences
Filtering Measures
Interest Set
Filtered Information Model
4 Evaluation
Precision Analysis
Time Analysis
5 Conclusions
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3. Introduction
Health Level Seven
Filtering HL7 Models IEEE 6th World Congress on Services (SERVICES 2010)
Evaluation
Conclusions
Outline
1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)
2 Health Level Seven
Healthcare Services
Reference Models Overview
RIM
D-MIM
R-MIM
3 Filtering HL7 Models
Overview
User Preferences
Filtering Measures
Interest Set
Filtered Information Model
4 Evaluation
Precision Analysis
Time Analysis
5 Conclusions
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4. Introduction
Health Level Seven
Filtering HL7 Models IEEE 6th World Congress on Services (SERVICES 2010)
Evaluation
Conclusions
Introduction
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5. Introduction
Health Level Seven
Filtering HL7 Models IEEE 6th World Congress on Services (SERVICES 2010)
Evaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
www.servicescongress.org/2010 July 5–10 Miami USA
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6. Introduction
Health Level Seven
Filtering HL7 Models IEEE 6th World Congress on Services (SERVICES 2010)
Evaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
www.servicescongress.org/2010 July 5–10 Miami USA
Business services sectors:
Advertising Services
Motion Pictures Services
Banking Services
Personal Services
Broadcasting & Cable TV Services
Printing & Publishing Services
Business Services
Real Estate Operations Services
Casinos & Gaming Services
Recreational Activities Services
Communications Services
Rental & Leasing Services
Cross-industry Services
Restaurants Services
Design Automation Services
Retail Services
Energy and Utilities Services
Schools and Education Services
Financial Services
Security Systems & Services
Government Services
Technology Services
Healthcare Services
Travel and Transportation Services
Hotels & Motels Services
Waste Management Services
Insurance Services
Wholesale Distribution Services
Internet Services
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7. Introduction
Health Level Seven
Filtering HL7 Models IEEE 6th World Congress on Services (SERVICES 2010)
Evaluation
Conclusions
IEEE 6th World Congress on Services (SERVICES 2010)
www.servicescongress.org/2010 July 5–10 Miami USA
Business services sectors:
Advertising Services
Motion Pictures Services
Banking Services
Personal Services
Broadcasting & Cable TV Services
Printing & Publishing Services
Business Services
Real Estate Operations Services
Casinos & Gaming Services
Recreational Activities Services
Communications Services
Rental & Leasing Services
Cross-industry Services
Restaurants Services
Design Automation Services
Retail Services
Energy and Utilities Services
Schools and Education Services
Financial Services
Security Systems & Services
Government Services
Technology Services
Healthcare Services
Travel and Transportation Services
Hotels & Motels Services
Waste Management Services
Insurance Services
Wholesale Distribution Services
Internet Services
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8. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Outline
1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)
2 Health Level Seven
Healthcare Services
Reference Models Overview
RIM
D-MIM
R-MIM
3 Filtering HL7 Models
Overview
User Preferences
Filtering Measures
Interest Set
Filtered Information Model
4 Evaluation
Precision Analysis
Time Analysis
5 Conclusions
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9. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Example
pictures from hl7.org
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10. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Example
pictures from hl7.org
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11. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Example
pictures from hl7.org
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12. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Example
pictures from hl7.org
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13. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Example
pictures from hl7.org
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14. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Example
pictures from hl7.org
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15. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Key challenges faced by healthcare organizations today include:
Impact on the safety, effectiveness, and cost of healthcare by
not having the right information at the right place at the
right time.
Presentation of disparate healthcare information at the
point of treatment
Increased cost in transferring paper records in this age of
e-commerce
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16. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Motivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
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17. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Motivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
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18. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Motivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
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19. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Healthcare Services
Motivation
Problem
Inability to share and manage data within and across organizations
Requirement
Interoperability of Services
Solution
Use of Standards
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20. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Health Level Seven
The Health Level Seven International (HL7) is a not-for profit,
ANSI-accredited standards developing organization dedicated to
providing a comprehensive framework and related standards for the
exchange, integration, sharing, and retrieval of electronic health
information that supports clinical practice and the management, delivery
and evaluation of health services.
HL7 develops specifications, the most widely used being a messaging
standard that enables disparate healthcare applications to exchange key
sets of clinical and administrative data.
The HL7 standard specifications are unified by shared reference
models of the healthcare and technical domains.
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21. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Health Level Seven
The Health Level Seven International (HL7) is a not-for profit,
ANSI-accredited standards developing organization dedicated to
providing a comprehensive framework and related standards for the
exchange, integration, sharing, and retrieval of electronic health
information that supports clinical practice and the management, delivery
and evaluation of health services.
HL7 develops specifications, the most widely used being a messaging
standard that enables disparate healthcare applications to exchange key
sets of clinical and administrative data.
The HL7 standard specifications are unified by shared reference
models of the healthcare and technical domains.
Villegas A., Oliv´ A., Vilalta J.
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22. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Health Level Seven
The Health Level Seven International (HL7) is a not-for profit,
ANSI-accredited standards developing organization dedicated to
providing a comprehensive framework and related standards for the
exchange, integration, sharing, and retrieval of electronic health
information that supports clinical practice and the management, delivery
and evaluation of health services.
HL7 develops specifications, the most widely used being a messaging
standard that enables disparate healthcare applications to exchange key
sets of clinical and administrative data.
The HL7 standard specifications are unified by shared reference
models of the healthcare and technical domains.
Villegas A., Oliv´ A., Vilalta J.
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23. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
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24. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
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25. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
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26. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
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27. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
Refinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIM
classes are refined in the subclasses.
The multiplicities of an association defined between RIM classes
are strengthened in the subclasses.
The multiplicity of an attribute of a RIM class is strengthened in
a 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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28. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
Refinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIM
classes are refined in the subclasses.
The multiplicities of an association defined between RIM classes
are strengthened in the subclasses.
The multiplicity of an attribute of a RIM class is strengthened in
a subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
Villegas A., Oliv´ A., Vilalta J.
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29. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
Refinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIM
classes are refined in the subclasses.
The multiplicities of an association defined between RIM classes
are strengthened in the subclasses.
The multiplicity of an attribute of a RIM class is strengthened in
a subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
Villegas A., Oliv´ A., Vilalta J.
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30. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
Refinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIM
classes are refined in the subclasses.
The multiplicities of an association defined between RIM classes
are strengthened in the subclasses.
The multiplicity of an attribute of a RIM class is strengthened in
a subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
Villegas A., Oliv´ A., Vilalta J.
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31. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Models Overview
Refinements
D-MIM models refine the RIM in three ways:
The participants of one of the associations defined between RIM
classes are refined in the subclasses.
The multiplicities of an association defined between RIM classes
are strengthened in the subclasses.
The multiplicity of an attribute of a RIM class is strengthened in
a subclass.
Note that it is not allowed to add new information.
R-MIM models refine D-MIM models in the same way.
Villegas A., Oliv´ A., Vilalta J.
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32. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Information Model (RIM)
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33. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Reference Information Model (RIM)
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34. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Domain Message Information Model (D-MIM)
HL7 Domains
Clinical Genomics
Administrative Management Diagnostic Imaging
Account and Billing Laboratory
Claims & Reimbursement Orders and Observations
Patient Administration Medical Records
Materials Management Medication
Personnel Management Pharmacy
Scheduling Public Health
Blood Bank Regulated Products
Care Provision Regulated Studies
Clinical Decision Support Specimen Domain
Clinical Document Architecture Therapeutic Devices
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35. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Domain Message Information Model (D-MIM)
Scheduling Domain
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36. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Refined Message Information Model (R-MIM)
The R-MIM is a subset of a D-MIM that is used to express the
information content for a message/document or set of
messages/documents with annotations and refinements that are
message/document specific.
The content of an R-MIM is drawn from the D-MIM for the
specific domain in which the R-MIM is used.
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37. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Refined Message Information Model (R-MIM)
Full Appointment R-MIM
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38. Introduction Healthcare Services
Health Level Seven Reference Models Overview
Filtering HL7 Models RIM
Evaluation D-MIM
Conclusions R-MIM
Interchanging Messages
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39. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Outline
1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)
2 Health Level Seven
Healthcare Services
Reference Models Overview
RIM
D-MIM
R-MIM
3 Filtering HL7 Models
Overview
User Preferences
Filtering Measures
Interest Set
Filtered Information Model
4 Evaluation
Precision Analysis
Time Analysis
5 Conclusions
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40. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Filtering HL7 Models
Objective
Automatically provide a filtered information model of the whole
HL7 models according to the user preferences.
Filtered Information Model
A small information model that focus on the knowledge of the
user’s request.
Its reduced size and self-contained aspect make it easier to the user
the comprehension and understandability of the focused knowledge.
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41. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Filtering HL7 Models
Objective
Automatically provide a filtered information model of the whole
HL7 models according to the user preferences.
Filtered Information Model
A small information model that focus on the knowledge of the
user’s request.
Its reduced size and self-contained aspect make it easier to the user
the comprehension and understandability of the focused knowledge.
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42. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Method Overview
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43. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Method Overview
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44. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered 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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45. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered 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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46. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered 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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47. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered 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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48. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Method Overview
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49. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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50. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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51. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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52. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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53. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance (Ψ)
Definition (Importance (Ψ))
The importance Ψ(c) of a class c is a real number that measures
the relative importance of that class in a model.
Methods 1
Occurrence Counting
Link Analysis
Instance-dependent
1
On Computing the Importance of Entity Types in Large Conceptual Schemas. Villegas, A. and Oliv´, A. ER 2009.
e
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54. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance (Ψ)
Definition (Importance (Ψ))
The importance Ψ(c) of a class c is a real number that measures
the relative importance of that class in a model.
Methods 1
Occurrence Counting
Link Analysis
Instance-dependent
1
On Computing the Importance of Entity Types in Large Conceptual Schemas. Villegas, A. and Oliv´, A. ER 2009.
e
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55. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance (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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56. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
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57. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
The importance problem
The importance of a class is an absolute metric that depends only
on the whole set of HL7 models.
The metric is useful when a user wants to know which are the
most important classes, but it is of little use when the user is
interested in a specific subset of classes, independently from their
importance.
What is needed then is a metric that measures the interest of a
class with respect to the focus set.
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58. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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59. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Closeness (Ω)
There may be several ways to compute the closeness Ω(c, FS) of a
class c with respect to the classes of FS.
Intuitively, the closeness of class c should be directly related to the
inverse of the distance of c to the focus set FS.
|FS|
Ω(c, FS) =
d(c, c )
c ∈F S
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60. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Closeness (Ω)
There may be several ways to compute the closeness Ω(c, FS) of a
class c with respect to the classes of FS.
Intuitively, the closeness of class c should be directly related to the
inverse of the distance of c to the focus set FS.
|FS|
Ω(c, FS) =
d(c, c )
c ∈F S
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61. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Closeness (Ω)
Intuitively, the closeness of class c should be directly related to the
inverse of the distance of c to the focus set FS.
|FS|
Ω(c, FS) =
d(c, c )
c ∈F S
number of classes in FS
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62. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Closeness (Ω)
Intuitively, the closeness of class c should be directly related to the
inverse of the distance of c to the focus set FS.
|FS|
Ω(c, FS) =
d(c, c )
c ∈F S
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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63. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Closeness (Ω)
Intuitively, the closeness of class c should be directly related to the
inverse of the distance of c to the focus set FS.
|FS|
Ω(c, FS) =
d(c, c )
c ∈F S
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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64. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Importance of classes (Ψ)
Closeness between classes (Ω)
Interest of classes (Φ)
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65. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
Definition (Interest Ψ)
The interest Φ(c, FS) of a class c with respect to a focus set FS
is a combination of the importance of c and its closeness to FS.
Φ(c, FS) = α × Ψ(c) + (1 − α) × Ω(c, FS)
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66. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
Definition (Interest Ψ)
The interest Φ(c, FS) of a class c with respect to a focus set FS
is a combination of the importance of c and its closeness to FS.
Φ(c, FS) = α × Ψ(c) + (1 − α) × Ω(c, FS)
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67. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
Definition (Interest Ψ)
The interest Φ(c, FS) of a class c with respect to a focus set FS
is 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
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68. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
Definition (Interest Ψ)
The interest Φ(c, FS) of a class c with respect to a focus set FS
is 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
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69. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
Definition (Interest Ψ)
The interest Φ(c, FS) of a class c with respect to a focus set FS
is 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)
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70. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
r
1
0.8
Importance Ψ
0.6
0.4 r
0.2
0
0 0.2 0.4 0.6 0.8 1
Closeness Ω
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71. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 2: Compute Filtering Measures
Interest (Φ)
r
1
r)
r)
P1
2,
0.8
6,
Importance Ψ
t(P
t( P
dis
dis
0.6 P5
P2
0.4 P7 P6 P4 P3
0.2 P8
0
0 0.2 0.4 0.6 0.8 1
Closeness Ω
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72. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Method Overview
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73. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 3: Select Interest Set
Select the top classes of the ranking produced by the computation of the
interest Φ s.t. |Interest Set| = Cmax − |F S|.
Most Interesting classes with regard to FS = {Patient, ActAppointment}.
Importance Distance Distance Closeness Interest
Rank Class (c)
Ψ(c) d(c, Patient) d(c, ActAppointment) Ω(c, F S) Φ(c, F S)
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
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74. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 3: Select Interest Set
Select the top classes of the ranking produced by the computation of the
interest Φ s.t. |Interest Set| = Cmax − |F S|.
Most Interesting classes with regard to FS = {Patient, ActAppointment}.
Importance Distance Distance Closeness Interest
Rank Class (c)
Ψ(c) d(c, Patient) d(c, ActAppointment) Ω(c, F S) Φ(c, F S)
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
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75. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Method Overview
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76. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
Classes
FS Classes
Interest Set Classes
Auxiliary Classes
Associations
Participant Classes are in FIM
Participant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization Relationships
Superclass and subclass are in FIM
Indirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
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77. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
Classes
FS Classes
Interest Set Classes
Auxiliary Classes
Associations
Participant Classes are in FIM
Participant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization Relationships
Superclass and subclass are in FIM
Indirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
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78. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
Classes
FS Classes
Interest Set Classes
Auxiliary Classes
Associations
Participant Classes are in FIM
Participant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization Relationships
Superclass and subclass are in FIM
Indirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
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79. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
Classes
FS Classes
Interest Set Classes
Auxiliary Classes
Associations
Participant Classes are in FIM
Participant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization Relationships
Superclass and subclass are in FIM
Indirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
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80. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
Classes
FS Classes
Interest Set Classes
Auxiliary Classes
Associations
Participant Classes are in FIM
Participant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization Relationships
Superclass and subclass are in FIM
Indirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
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81. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Filtered Information Model (FIM) Construction
Classes
FS Classes
Interest Set Classes
Auxiliary Classes
Associations
Participant Classes are in FIM
Participant Classes are superclasses of classes in FIM
Project the association to subclasses
Generalization-Specialization Relationships
Superclass and subclass are in FIM
Indirect path of generalizations induces superclass and subclass in FIM
Mark generalization as indirect
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82. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Projection of Association
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83. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Projection of Association
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84. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Projection of Association
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85. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Generalization-Specialization Relationships
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86. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
Generalization-Specialization Relationships
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87. Introduction Overview
Health Level Seven User Preferences
Filtering HL7 Models Filtering Measures
Evaluation Interest Set
Conclusions Filtered Information Model
Step 4: Compute Filtered Information Model
FS = {Patient, ActAppointment} and Cmax = 12.
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88. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Outline
1 Introduction
IEEE 6th World Congress on Services (SERVICES 2010)
2 Health Level Seven
Healthcare Services
Reference Models Overview
RIM
D-MIM
R-MIM
3 Filtering HL7 Models
Overview
User Preferences
Filtering Measures
Interest Set
Filtered Information Model
4 Evaluation
Precision Analysis
Time Analysis
5 Conclusions
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89. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Evaluation
To find a measure that reflects the ability of our method to satisfy
the user is a complicated task.
However, there exists measurable quantities in the field of
information 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 answer
being given (time)
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90. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Evaluation
To find a measure that reflects the ability of our method to satisfy
the user is a complicated task.
However, there exists measurable quantities in the field of
information 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 answer
being given (time)
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91. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Evaluation
To find a measure that reflects the ability of our method to satisfy
the user is a complicated task.
However, there exists measurable quantities in the field of
information 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 answer
being given (time)
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92. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Evaluation
To find a measure that reflects the ability of our method to satisfy
the user is a complicated task.
However, there exists measurable quantities in the field of
information 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 answer
being given (time)
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93. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
The precision of a method is defined as the percentage of relevant
knowledge presented to the user.
We use the concept of precision applied to HL7 universal domains
(specified with D-MIM’s).
|{relevant classes}| ∩ |{retrieved classes}|
Precision =
|{retrieved classes}|
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94. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
The precision of a method is defined as the percentage of relevant
knowledge presented to the user.
We use the concept of precision applied to HL7 universal domains
(specified with D-MIM’s).
|{relevant classes}| ∩ |{retrieved classes}|
Precision =
|{retrieved classes}|
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95. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
Each domain contains a main class which is the central point of
knowledge to the users interested in such domain. The other
classes presented in the domain conform the relevant knowledge
related to the main class.
A common situation for a user is to focus on the main class of a
domain and to navigate through the D-MIM to understand its
related knowledge.
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96. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
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97. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
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98. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
We simulate the generation of a D-MIM from its main class.
Initialization
FS = main class of the D-MIM
Cmax = number of classes of the D-MIM
This way, we will obtain a filtered information model with the same number of
classes as such domain.
Following Iterations
FS = main class of the D-MIM
Cmax = number of classes of the D-MIM
RS = includes non-relevant classes retrieved in the previous iteration
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99. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
We simulate the generation of a D-MIM from its main class.
Initialization
FS = main class of the D-MIM
Cmax = number of classes of the D-MIM
This way, we will obtain a filtered information model with the same number of
classes as such domain.
Following Iterations
FS = main class of the D-MIM
Cmax = number of classes of the D-MIM
RS = includes non-relevant classes retrieved in the previous iteration
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100. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
Precision Pr
100
100
●
● ● ●
● ● ● ●
90
90
● ● ● ● ● ●
●
80
80
Precision (%)
Precision (%)
●
70
70
60
60
Medical Records
Scheduling
● ●
Account and Billing ●
50
50
Laboratory
40
40
0 5 10 15 20 25 30 1
Iterations
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101. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
Precision (Zoom Iterations 1−5)
100
90
● ●
●
80
Precision (%)
●
70
60
Medical Records
Scheduling
● ●
Account and Billing
50
Laboratory
40
30 1 2 3 4 5
Iterations
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102. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Precision Analysis
The test reveals that to reach more than 80% of the relevant
classes of a domain, only three iterations are required.
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103. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Time Analysis
A good method does not only require precision, but it also needs
to present the results in an acceptable time according to the user.
Test
Record the time lapse between the request of knowledge, i.e. once
a focus set FS has been indicated by the user, and the receipt of
the filtered information model.
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104. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Time Analysis
A good method does not only require precision, but it also needs
to present the results in an acceptable time according to the user.
Test
Record the time lapse between the request of knowledge, i.e. once
a focus set FS has been indicated by the user, and the receipt of
the filtered information model.
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105. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Time Analysis
It is expected that as we increase the size of the focus set, the time
will increase linearly.
Reason
Our method computes the distances from each class in the focus
set to all the rest of classes. This computation requires the same
time (in average) for each class in the focus set.
Therefore, the more classes we have in a focus set, the more the
time our method spends in computing distances.
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106. Introduction
Health Level Seven
Precision Analysis
Filtering HL7 Models
Time Analysis
Evaluation
Conclusions
Time Analysis
It is expected that as we increase the size of the focus set, the time
will increase linearly.
Reason
Our method computes the distances from each class in the focus
set to all the rest of classes. This computation requires the same
time (in average) for each class in the focus set.
Therefore, the more classes we have in a focus set, the more the
time our method spends in computing distances.
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