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Landscape Specifications record clinical facts in two patterns:

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Presentation on theme: "Landscape Specifications record clinical facts in two patterns:"— Presentation transcript:

1 Landscape Specifications record clinical facts in two patterns:
Binary Question/answer pairs. Blood pressure? 119 mmHg Unary assertions. Diabetes. Many kinds of facts are easy to assign consistently to one pattern or the other. Unary Diagnoses Complaints Binary Questionnaires Measurements FHIR: Condition & Observation. V2: Problem & Observation. C-CDA: code/value & Assertion/value.

2 Issue Sometimes, the choice is not obvious.
Conceptual questions “Blue skin” or “Skin color? Blue” “Tachycardia” or “Pulse rate kind? Fast” If you look hard enough, it’s possible to transform the “obvious” examples as well. This means information may be recorded in different formats, and it won’t be available for consistent search or reasoning.

3 Semantic & Structural Criteria
Persistence: An observation is true at the time observed; a condition is a persistent state. Concern: A condition is a physiological state of clinical concern; an observation may provide evidence for the existence of the state. Objectivity: An observation is objective; a condition is asserted as a result of clinical deliberation. Structure: An observation consists of two parts: a question, which can be asked of anyone, and a result value. A condition is a single concept that is only pertinent to a specific class of people.

4 Cases Case Assertion Pattern Evaluation Result Pattern Problem
Diabetes mellitus (finding) DM presence (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Measurement SBP of 120 (finding) Systolic Blood Pressure (observable): 120 mmHg Concept evaluation Blue skin (finding) Skin color (observable): blue (qualifier value) Skin color (observable): blue skin (finding) Assertion: blue skin (finding) More examples here

5 Cases Case Assertion Pattern Evaluation Result Pattern Problem Diabetes mellitus (finding) DM presence (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Problem: Diabetes mellitus (finding) Diabetes mellitus (finding): poorly controlled (qualifier value) Note: we avoid inclusion of qualifying elements already present elsewhere in the model for the purposes of this analysis.

6 Case 1 Assertion pattern preferred
Evaluation Result Pattern Problem Diabetes mellitus (finding) DM presence (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Measurement SBP of 120 (finding) Systolic Blood Pressure (observable): 120 mmHg Concept evaluation Blue skin (finding) Skin color (observable): blue (qualifier value) Skin color (observable): blue skin (finding) Assertion: blue skin (finding) Assertion pattern preferred For Evaluation Result pattern, Finding Context is handled explicitly in the model & would be redundant here.

7 Case 2 Evaluation Result pattern preferred
Assertion Pattern Evaluation Result Pattern Problem Diabetes mellitus (finding) DM presence (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Measurement SBP of 120 (finding) Systolic Blood Pressure (observable): 120 mmHg Concept evaluation Blue skin (finding) Skin color (observable): blue (qualifier value) Skin color (observable): blue skin (finding) Assertion: blue skin (finding) Evaluation Result pattern preferred No identified use cases to encourage combinatorial explosion of semantic findings

8 Case 3 Assertion pattern preferred?
Evaluation Result Pattern Problem Diabetes mellitus (finding) DM presence (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Measurement SBP of 120 (finding) Systolic Blood Pressure (observable): 120 mmHg Concept evaluation Blue skin (finding) Skin color (observable): blue (qualifier value) Skin color (observable): blue skin (finding) Assertion: blue skin (finding) Assertion pattern preferred? How to structure semantics of Evaluation . . .

9 CIMI options for maximal consistency
Case Assertion Pattern Evaluation Result Pattern Problem Diabetes mellitus (finding) DM presence (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Measurement SBP of 120 (finding) Systolic Blood Pressure (observable): 120 mmHg Concept evaluation Blue skin (finding) Skin color (observable): blue (qualifier value) Skin color (observable): blue skin (finding) Assertion: blue skin (finding) Align on Finding For Problem, this is simply a choice of convention For Concept Evaluation, this may imply a realignment of semantic assumptions

10 Evaluation Pattern Options: Semantics
Data Semantics Question / Answer Skin color (observable): Blue (qualifier value) Skin color observable has value blue Context / Finding Skin color (observable): Blue skin (finding) Skin color context: Finding of Blue skin In the first pattern (which aligns with LOINC questions & answers), both question and answer constitute semantics of statement. In the second pattern, the question is merely a context in which to make the semantic assertion of the finding. Classification could be supported with observables, with some work I.e., authoring definitions: Blue skin (Finding)  Interprets Skin color (observable) + Has interpretation Blue(qualifier value)

11 Annex: Skin Color Case Assertion Pattern Evaluation Result Pattern Concept evaluation Jaundice (finding) Yellow skin (finding) Etc. Skin color (observable): Yellow (qualifier value) Skin color (observable): Jaundice (finding) Skin color (observable): Yellow skin (finding) Cyanosis (finding) Blue skin (finding) Skin color (observable): Blue (qualifier value) Skin color (observable): Cyanosis of skin (finding) Skin color (observable): Blue skin (finding) Clinical context assumptions + variety of existing concepts introduce additional complexity These represent a different problem.

12 Alternate Pattern: Observable + Qualifier
|Urine color abnormal (finding)| +      |Discolored urine (finding)| :             { |Has interpretation (attribute)| = |Blue color (qualifier value)|,                |Interprets (attribute)| = |Color of urine (observable entity)| }

13 Further definition required

14 Wrinkle Case Assertion Pattern Evaluation Result Pattern Problem Diabetes mellitus (finding) Diabetes mellitus (observable): known present (qualifier value) Diabetes mellitus (finding): known present (qualifier value) Assertion: Diabetes mellitus (finding) Measurement SBP of 120 (finding) Systolic Blood Pressure (observable): 120 mmHg Concept identification E coli O157:H7 present (finding) Escherichia coli O157:H7 [Presence] in Isolate by Organism specific culture: Positive Concept evaluation Blue skin (finding) Skin color (observable): blue (qualifier value) Skin color (observable): blue skin (finding) Assertion: blue skin (finding) In Presence tests, is the “positive” value synonymous with the finding context “present”?

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