HL7 Models Auto Filtering

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

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HL7 Models Auto Filtering

  1. 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. e Improving the Usability of HL7 Models by Filtering 1/ 63
  2. 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. e Improving the Usability of HL7 Models by Filtering 2/ 63
  3. 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. 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. e Improving the Usability of HL7 Models by Filtering 4/ 63
  5. 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. e Improving the Usability of HL7 Models by Filtering 5/ 63
  6. 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. e Improving the Usability of HL7 Models by Filtering 6/ 63
  7. 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. e Improving the Usability of HL7 Models by Filtering 6/ 63
  8. 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. 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. e Improving the Usability of HL7 Models by Filtering 8/ 63
  10. 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. e Improving the Usability of HL7 Models by Filtering 8/ 63
  11. 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. e Improving the Usability of HL7 Models by Filtering 8/ 63
  12. 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. e Improving the Usability of HL7 Models by Filtering 8/ 63
  13. 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. e Improving the Usability of HL7 Models by Filtering 8/ 63
  14. 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. e Improving the Usability of HL7 Models by Filtering 8/ 63
  15. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 9/ 63
  16. 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. e Improving the Usability of HL7 Models by Filtering 10/ 63
  17. 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. 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. 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. 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. 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. 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. e Improving the Usability of HL7 Models by Filtering 11/ 63
  23. 23. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Reference Models Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 12/ 63
  24. 24. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Reference Models Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 12/ 63
  25. 25. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Reference Models Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 12/ 63
  26. 26. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Reference Models Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 12/ 63
  27. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 13/ 63
  28. 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. e Improving the Usability of HL7 Models by Filtering 13/ 63
  29. 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. e Improving the Usability of HL7 Models by Filtering 13/ 63
  30. 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. e Improving the Usability of HL7 Models by Filtering 13/ 63
  31. 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. e Improving the Usability of HL7 Models by Filtering 13/ 63
  32. 32. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Reference Information Model (RIM) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 14/ 63
  33. 33. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Reference Information Model (RIM) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 14/ 63
  34. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 15/ 63
  35. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 16/ 63
  36. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 17/ 63
  37. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 18/ 63
  38. 38. Introduction Healthcare Services Health Level Seven Reference Models Overview Filtering HL7 Models RIM Evaluation D-MIM Conclusions R-MIM Interchanging Messages Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 19/ 63
  39. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 20/ 63
  40. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 21/ 63
  41. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 21/ 63
  42. 42. Introduction Overview Health Level Seven User Preferences Filtering HL7 Models Filtering Measures Evaluation Interest Set Conclusions Filtered Information Model Method Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 22/ 63
  43. 43. Introduction Overview Health Level Seven User Preferences Filtering HL7 Models Filtering Measures Evaluation Interest Set Conclusions Filtered Information Model Method Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 23/ 63
  44. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 24/ 63
  45. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 24/ 63
  46. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 24/ 63
  47. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 24/ 63
  48. 48. Introduction Overview Health Level Seven User Preferences Filtering HL7 Models Filtering Measures Evaluation Interest Set Conclusions Filtered Information Model Method Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 25/ 63
  49. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 26/ 63
  50. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 26/ 63
  51. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 26/ 63
  52. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 27/ 63
  53. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 28/ 63
  54. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 28/ 63
  55. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 29/ 63
  56. 56. Introduction Overview Health Level Seven User Preferences Filtering HL7 Models Filtering Measures Evaluation Interest Set Conclusions Filtered Information Model Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 30/ 63
  57. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 31/ 63
  58. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 32/ 63
  59. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 33/ 63
  60. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 33/ 63
  61. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 34/ 63
  62. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 35/ 63
  63. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 35/ 63
  64. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 36/ 63
  65. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 37/ 63
  66. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 37/ 63
  67. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 38/ 63
  68. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 39/ 63
  69. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 40/ 63
  70. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 41/ 63
  71. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 41/ 63
  72. 72. Introduction Overview Health Level Seven User Preferences Filtering HL7 Models Filtering Measures Evaluation Interest Set Conclusions Filtered Information Model Method Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 42/ 63
  73. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 43/ 63
  74. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 43/ 63
  75. 75. Introduction Overview Health Level Seven User Preferences Filtering HL7 Models Filtering Measures Evaluation Interest Set Conclusions Filtered Information Model Method Overview Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 44/ 63
  76. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 45/ 63
  77. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 45/ 63
  78. 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. e Improving the Usability of HL7 Models by Filtering 45/ 63
  79. 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. e Improving the Usability of HL7 Models by Filtering 45/ 63
  80. 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. e Improving the Usability of HL7 Models by Filtering 45/ 63
  81. 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. e Improving the Usability of HL7 Models by Filtering 45/ 63
  82. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 46/ 63
  83. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 46/ 63
  84. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 46/ 63
  85. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 47/ 63
  86. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 47/ 63
  87. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 48/ 63
  88. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 49/ 63
  89. 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) Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 50/ 63
  90. 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. e Improving the Usability of HL7 Models by Filtering 50/ 63
  91. 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. e Improving the Usability of HL7 Models by Filtering 50/ 63
  92. 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. e Improving the Usability of HL7 Models by Filtering 50/ 63
  93. 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}| Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 51/ 63
  94. 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}| Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 51/ 63
  95. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 52/ 63
  96. 96. Introduction Health Level Seven Precision Analysis Filtering HL7 Models Time Analysis Evaluation Conclusions Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 53/ 63
  97. 97. Introduction Health Level Seven Precision Analysis Filtering HL7 Models Time Analysis Evaluation Conclusions Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 53/ 63
  98. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 54/ 63
  99. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 54/ 63
  100. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 55/ 63
  101. 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 Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 55/ 63
  102. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 56/ 63
  103. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 57/ 63
  104. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 57/ 63
  105. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 58/ 63
  106. 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. Villegas A., Oliv´ A., Vilalta J. e Improving the Usability of HL7 Models by Filtering 58/ 63
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