This document discusses improving the usability of HL7 information models through automatic filtering. It provides an introduction to HL7 and its reference models, including the RIM, D-MIM, and R-MIM. It then outlines a method for filtering HL7 models based on user preferences to generate a filtered information model. The filtering approach is evaluated based on precision and time analysis.
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This document lists several types of produce: maduixes (strawberries), enciams (lettuce), cebes (onions), and tomàquets (tomatoes). It also mentions el pas del temps a l'hort (the passage of time in the garden). In a few words, the document seems to be listing common garden vegetables and fruits.
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Los niños jugaron dentro de cajas de cartón, usando papel de diario y telas para imaginar y crear. Se escondieron y llenaron y vaciaron las cajas, inventando nuevas formas de juego mientras usaban las telas blancas para representar nubes, divirtiéndose mucho en el proceso.
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The document describes a method for filtering large conceptual schemas to aid understanding of constraint expressions. The method takes as input a focus set of constraints and outputs a minimized filtered schema containing just the elements necessary to understand the constraints. It proceeds in 6 stages: 1) extracting referenced entity and event types; 2) relationship types; 3) generalizations; 4) data types; 5) schema rules; and 6) presentation of the filtered schema. An example constraint from the Magento schema is used to illustrate the stages.
The document discusses a panel on innovation networks held at the 6th INSME Annual Meeting in Rio de Janeiro, Brazil in 2010. It provides an overview of the panel topics which include what a living lab is, the European Network of Living Labs, living lab methodologies and tools, best practice cases, the Brazil Living Labs Network, and EU-Brazil living lab collaboration. Living labs are described as user-driven open innovation ecosystems where users co-create, experiment, and test new ideas, products, and services in real-life environments.
This document tests the reader's knowledge of Disney characters and movies with 10 multiple choice trivia questions. It asks about the spell used by Cinderella's fairy godmother, Donald Duck's nephews' names, the pizzeria Buzz and Woody visit in Toy Story, Hercules' trainer, the forest where Robin Hood lives, the Disney character that likes Elvis Presley, Wendy's brothers' names in Peter Pan, what Merlin is called, Mulan's dragon's name.
Angela ribes, marta tornero y carla olivares 3. activitiesEscolapias Gandia
The document contains snippets of dialogue from films and asks the reader to identify the movie. It also includes links about regional English accents and a video example of Antonio Banderas speaking English. The key dialogue pieces are from the films Dirty Dancing, Resident Evil, and Tomb Raider, and the document tests the reader's familiarity with memorable quotes from these popular movies.
This document lists several types of produce: maduixes (strawberries), enciams (lettuce), cebes (onions), and tomàquets (tomatoes). It also mentions el pas del temps a l'hort (the passage of time in the garden). In a few words, the document seems to be listing common garden vegetables and fruits.
The document discusses standards and tools used in book publishing. It provides an overview of BookNet Canada, including its role in maintaining standards for EDI, ONIX, subject classification and product identifiers. Specific standards discussed include ISBN, ISNI, and ISTC. The presenters explain how these standards can help identify digital products, connect works to their creators, and facilitate selling more books.
Los niños jugaron dentro de cajas de cartón, usando papel de diario y telas para imaginar y crear. Se escondieron y llenaron y vaciaron las cajas, inventando nuevas formas de juego mientras usaban las telas blancas para representar nubes, divirtiéndose mucho en el proceso.
Understanding Constraint Expressions in Large Conceptual Schemas by Automatic...Antonio Villegas
The document describes a method for filtering large conceptual schemas to aid understanding of constraint expressions. The method takes as input a focus set of constraints and outputs a minimized filtered schema containing just the elements necessary to understand the constraints. It proceeds in 6 stages: 1) extracting referenced entity and event types; 2) relationship types; 3) generalizations; 4) data types; 5) schema rules; and 6) presentation of the filtered schema. An example constraint from the Magento schema is used to illustrate the stages.
The document discusses a panel on innovation networks held at the 6th INSME Annual Meeting in Rio de Janeiro, Brazil in 2010. It provides an overview of the panel topics which include what a living lab is, the European Network of Living Labs, living lab methodologies and tools, best practice cases, the Brazil Living Labs Network, and EU-Brazil living lab collaboration. Living labs are described as user-driven open innovation ecosystems where users co-create, experiment, and test new ideas, products, and services in real-life environments.
The Agency for Information Society Services aims to improve public administration performance and increase citizen comfort through centralized government IT systems, common methodologies and standards, and partnering with other public institutions to develop e-government policies, applications and provide IT support. The agency faces challenges in transitioning to a more client-oriented structure and gaining financial autonomy, but offers partnerships to other institutions to develop e-government solutions.
Results of the Apollon pilot in homecare and independent livingimec.archive
The document summarizes the results of the Apollon pilot project evaluating the use of living lab networks for testing homecare and independent living services across borders. The pilot involved transferring three such services between four living labs in different countries. A key finding was that a common cross-border ecosystem model for living labs in healthcare was not feasible due to differences between countries in areas like value networks, organization of healthcare, regulations, and infrastructure. However, living labs could still effectively serve as brokers and matchmakers to enable cross-border collaboration by addressing issues around stakeholders, access to users, liability, ethics, rules, and safety. Based on this pilot, the document advocates for a domain-specific network of smart care living labs to facilitate knowledge
Japan Institute for Design Promotion, December 21st, 2011, Tokyo, Japan.Stephen Kwan
This presentation discusses service design from a systems perspective, integrating system thinking, design thinking, and business thinking. It defines key concepts in service science like service systems and value co-creation. The presentation outlines a multi-disciplinary approach to service system design incorporating knowledge management. Finally, it examines stages in customer empowerment from traditional value chains to customer-driven service value networks.
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The document discusses the changes made in ITIL Version 3, including updated and new processes and concepts. Key changes include a stronger emphasis on business value, a lifecycle approach to service management, and the introduction of new processes like service portfolio management and demand management. ITIL Version 3 aims to provide best practice guidance for an integrated approach and alignment with other frameworks like ISO/IEC 20000.
The document discusses service science and its importance for building a smarter planet. It outlines how the world's economies and jobs have shifted towards services. Service science aims to study complex service systems and improve customer-provider interactions. The document discusses key concepts in service science like service systems, value co-creation, and a systems-disciplines matrix. It emphasizes the need for a skilled multi-disciplinary workforce and highlights opportunities in areas that improve quality of life.
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- SOAs typically involve 30 services on average but there is high variance.
- REST is used for communication by 95% of participants while 40% use SOAP. JSON and XML are the main data formats.
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We look at ISO 20000 in a fresh perspective: not as a certification endpoint in the IT Service Management Journey, but as a good place to begin representing the minimum critical activities necessary to achieve basic, overall ITSM maturity.
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The document discusses service science and its role in building a smarter planet. It outlines how service sectors have grown globally and how IBM is responding by focusing on service science priorities. These priorities include understanding service systems, creating and improving complex systems, and developing skills and tools to advance service innovation. The goal of service science is to discover principles of service systems and create a smarter planet where systems are instrumented, interconnected and intelligent to improve quality of life.
Forging a (ehealth) health Information System - A Multi-Axial enterprise appr...Ime Asangansi, MD, PhD
The document presents a framework for developing a multi-axial health information systems approach. It discusses current problems with fragmented systems and lack of integration. The proposed framework involves decomposing systems into modular components like actors, processes, domains and hierarchy. These components would be analyzed and represented using matrices to show relationships. This multi-axial enterprise approach aims to provide a reusable and collaborative framework to help manage complexity in health information systems.
CeBIT 2011 is the world's largest and most important ICT conference, held in Hannover, Germany. It attracts 350,000 visitors from 100 countries and has connections to over 2 billion media contacts and 8 million business contacts. CeBIT is organized into 4 main platforms - CeBITpro focuses on professional ICT solutions for business, CeBITgov focuses on solutions for the public sector, CeBITlife focuses on consumer ICT products, and CeBITlab focuses on research and development. CeBIT aims to connect ICT users with providers and support dialogue on emerging technologies and solutions.
Join us for an overview of ITIL 2011 updates. This session describes the scope and benefits of the updates, key changes to each core book and a high-level overview of the new processes.
This document provides an overview of Work Package 5 (WP5) in the ViBRANT project. WP5 will design and implement tools and services for analyzing biodiversity data within Scratchpads, including identification services, modeling services, and computing platforms. It will enhance the Scratchpad user interface and provide a history of analysis jobs. The leader, Neil Caithness, provides background on his experience and outlines goals, risks, and mitigation strategies for WP5. A schedule of talks at a meeting is also included.
The document discusses challenges for service level assurance in open innovation projects. It outlines demographic and societal challenges facing cities, such as an aging population and talent management issues. It then discusses metrics for evaluating business value, agile deployment, and sustainable delivery of services. The document proposes a smart city cloud platform using a networked infrastructure and core management services. It argues that converging digital, physical, natural, and human infrastructures using an integrated network and virtualized computing infrastructure is essential for smart information discovery and decision making in cities.
ISHIMR2011: Facilitating an Open Source Ecosystem for the UK e-Health CommunityYing Liao
This document proposes a framework to facilitate an open source ecosystem for the UK e-health community. It introduces concepts of an e-health open source ecosystem and why such an ecosystem is needed in the UK healthcare system. The proposed framework consists of four interconnected models: a marketplace model, contribution model, governance model, and partnership model. The framework aims to foster collaboration and interoperable software solutions to support improved patient-centric care.
The document discusses the Trust in Digital Life (TDL) consortium, which aims to stimulate research and development of trustworthy information and communication technology (ICT) solutions. TDL has over 20 members from industry, academia, and government working to set a research agenda. The consortium's goals are to establish an inspiring and self-sustaining community to advance knowledge and collaborative projects, develop an innovative research agenda, enable public funding for related projects, and increase awareness through demonstrations. TDL will measure progress using key performance indicators like adoption rates of e-services and survey scores on consumer trust.
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This presentation discusses service design from a systems perspective, integrating system thinking, design thinking, and business thinking. It defines key concepts in service science like service systems and value co-creation. The presentation outlines a multi-disciplinary approach to service system design incorporating knowledge management. Finally, it examines stages in customer empowerment from traditional value chains to customer-driven service value networks.
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This document summarizes the findings of a survey study on industrial service computing practices. Key findings include:
- Services vary in size but few actually implement microservices. Most services are internal rather than web services.
- SOAs typically involve 30 services on average but there is high variance.
- REST is used for communication by 95% of participants while 40% use SOAP. JSON and XML are the main data formats.
- Monitoring focuses on metrics like response times, failures, and infrastructure rather than discovery since services typically only have one or two implementations.
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Similar to Improving the Usability of HL7 Information Models by Automatic Filtering (20)
Improving the Usability of HL7 Information Models by Automatic Filtering
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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
e Improving the Usability of HL7 Models by Filtering 3/ 63
4. Introduction
Health Level Seven
Filtering HL7 Models IEEE 6th World Congress on Services (SERVICES 2010)
Evaluation
Conclusions
Introduction
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
e Improving the Usability of HL7 Models by Filtering 7/ 63
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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
e Improving the Usability of HL7 Models by Filtering 10/ 63
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
Villegas A., Oliv´ A., Vilalta J.
e Improving the Usability of HL7 Models by Filtering 10/ 63
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
Villegas A., Oliv´ A., Vilalta J.
e Improving the Usability of HL7 Models by Filtering 10/ 63
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.
Villegas A., Oliv´ A., Vilalta J.
e Improving the Usability of HL7 Models by Filtering 11/ 63
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.
e Improving the Usability of HL7 Models by Filtering 11/ 63
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.
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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.
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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.
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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.
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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.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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
Villegas A., Oliv´ A., Vilalta J.
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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)
Villegas A., Oliv´ A., Vilalta J.
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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)
Villegas A., Oliv´ A., Vilalta J.
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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)
Villegas A., Oliv´ A., Vilalta J.
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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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