Data Standards Comparison via:

Data Standards Expressions The Purpose of Data Modeling Domain Information Models (DIM) The Canonical DIM Model Components Closing Thoughts 2

Data standards are expressed in many formats: messages, documents, guides, transactions, schemas, templates, and others. Common features among these expressions are the assignment of names, data types, and value constraints to data elements. Comparisons between data standards are complicated by the variation in their expression. Data models can be a useful as a common expression of data standards. 3

HL7 v2 Segment HL7 v3 HMD HL7 v3 XSD 4

To aid in understanding data in a particular domain To communicate the modelers understanding of data and allow that understanding to be assessed by others To aid in reconciling multiple perspectives of data by combining the perspectives into a single specification To document a data structure design (existing or planned) so that the design may be evaluated 5

Revealing assumptions is an essential component of effective communication. Data models are an effective means of documenting our assumptions about data Yes, I do play football. Do you play football? 6

Data modeling provides a language that allows us to unambiguously express our understanding and assumptions about the information in a particular domain. A C B 0..* 1 7

Sharing data models provides an opportunity to identify and reconcile conflicts in our understanding and to validate our assumptions about information. A C B 0..* 1 X C B 1 8

Sharing data models provides an opportunity to identify gaps in our understanding. No one of us has the complete view of the public health information domain. AB (0,M) (0,1) D (1,1) (0,M) A C B 0..* 1 9

Reveal Assumptions Reduce Ambiguity Reconcile Conflicts Expand Understanding 10

A domain information model is an expression of a proposed or existing data standard (or data requirement). It is a faithful rendering of the standard in the form of a UML class diagram with an accompanying data dictionary. Any data structure, regardless of format, can be expressed as a domain information model. Once a data structures expression is re-expressed as a domain information model it can then be compared and harmonized with related data structures. 11

A C B 0..* 1 X C B 1 AB (0,M) (0,1) D (1,1) (0,M) HL7 v2 Segment HL7 v3 HMD HL7 v3 XSD 12

Domain Information Model GB (0,M) (0,1) AD (0,M) E (1,1) C (0,M) X Domain Information Model A C B 0..* 1 X C B 1 AB (0,M) (0,1) D (1,1) (0,M) 13

Class Name Description Attributes Relationships Attribute Name Description Datatype Value Set Binding Relationship Name Type Source Cardinality Target Cardinality Source Role Name Target Role Name Constraint Structural Constraint Semantic Constraint 14

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Person -lastName: char -firstName: char -birthDate: dateTime -homePhone: char -workPhone: char Model One Person -name: PersonName -birthDate: dateTime -phone: PersonPhone [0..2] (list) constraints {PersonPhone(1) is Home Phone} {PersonPhone(2) is Work Phone} «datatype» PersonName -lastName: char -firstName: char «datatype» PersonPhone -phoneKind: PhoneKind -phoneText: char «enumeration» PhoneKind «enum» Home Work Model Two Person -birthDate: dateTime PersonName -personNameKind: PersonNameKind -personNameText: char constraints {PersonNameKind is Unique} PersonPhone -phoneKind: PersonPhoneKind -phoneText: char constraints {PersonPhoneKind is Unique} «enumeration» PersonPhoneKind «enum» Home Work «enumeration» PersonNameKind «enum» lastName firstName 0..2 Model Three 16

Salient: Since no model can represent everything, it must selectively represent those things most relevant to the task at hand. Accurate: The model should precisely encode the actual state of affairs and not an erroneous or biased view. Complete yet Parsimonious: The model should be as simple as possible, but no simpler. It should concisely capture all the relevant dimensions of the problem without squeezing out the opportunity for serendipitous or creative insight. Perceptible: Models should be appropriately displayed in high fidelity as they won't be much use if we can't clearly see, hear, or feel them. Understandable: Once we perceive the model we must be able to make sense of it; it mustn't be too complicated or unfamiliar for us to understand. Descriptive: The model should clearly and objective describe the true situation. Emotive: In addition, the model should convey a subjective feel for the emotional and value-laden connotations of the situation being modeled. Inspiring: Because people are drawn to and inspired by thoughtful design, models should be elegant, i.e. they should synergistically combine style and substance. Memorable: Models are not of much use if they pass quickly from the mind, or if they cannot be used as a mnemonic device. Models should be easily accessible for future reference and to refresh our understanding. Flexible: As all models are, to some degree, inaccurate, irrelevant, mistaken, time-sensitive etc., they should be open to recursive revision to reflect new data, our growing understanding, or our evolving needs. Coherent: Models do not exist in isolation but in interlocking systems, thus any particular model should be coherent with other related models. Productive: Ultimately, the model has a purpose: the production of effective action. A good model should help define our goals and then specify the actions necessary to reach them. Useful: Usefulness is the sum of the above properties and the degree to which they combine to promote understanding and effective action. It is important to note that the most accurate, or the most complete, or the most elegant model is not necessarily the most useful. All models are incomplete. All models a compromise. The model maker's art lies in making those shrewd trade-offs that will render the model most useful to the problem at hand. 17

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Abdul-Malik Shakir Principal Consultant Shakir Consulting 1407Foothill Blvd., Suite 145 La Verne, CA Office: (909) Mobile: (626) of 115