Models of use and Model of Meaning towards a Model Driven Architecture for Data Entry & decision support Alan Rector, Tom Marley, and Rahil Qamar University of Manchester with acknowledgements to the PEN&PAD and GALEN Teams OpenGALEN BioHealth Informatics Group

2 Three Resources reusable reference information resources Metadata interface Concept System Model (‘Ontology’) Information Model (EHR Model, Archetypes) Inference Model (Guideline Model) ‘Contingent’ Domain Knowledge General Domain Knowledge Individual Patient Records ►Each with ►Model ►Knowledge/ content ►Metadata ►Interfaces to the others

3 Model of meaning Models of use ►Model of meaning ►Our conceptualisation of the world ►The natural domain of ‘ontologies’ and universals ►All human organisms have a height, weight, and BMI ►How information is to be retrieved ►All patients with pneumonia, regardless of how or where recorded ►Models of use ►What we record and use What needs to be ‘to hand’ or ‘handy’ [Winograd & Flores; Pel Ehn] ►The natural domain of prototypes and contingencies ►In this study we record Weight on everyone and BMI only on patients over 80Kg for men and 65Kg for women ►The realm of business rules, forms, etc.

4 Ontology Indexed reusable resources: example of data collection forms for trials Renin dependent Hypertension at St Stevens Hospitals for the National Hypertension Survey Hypertension Renin Dependent Hypertension` In St Stevens Hospital National Hypertension Survey systolic & diastolic pressure Serum Potassium foot pulses

5 Now much easier than for PEN&PAD ►Form fragments naturally bits of XML ►Presentation naturally a matter for XSLT, HTML forms, Xforms, … … ►Transformations of XML straightforward ►(But don’t attempt to deal with raw OWL/RDF syntax if you can avoid it)

6 Original PEN&PAD Design ►Drove the interface directly from the ontology ►Attempted to conflate model of use with model of meaning ►Second design ►Separated notion of form (use) from meaning

7 …but the model of structure is not the model of the domain ►A form for data entry about a chest X-Ray interpreted as showing pneumonia is not the same thing as pneumonia or even an X-Ray of pneumonia ►No way to get from one to the other by logic ►Requires a “lens” or “view” ►In implementation terms, the structures on either side of the lens really are different ►Negation in the model of the domain becomes a field in the data model ►Reports can be about things ►Things are not about themselves

8 … and “Aboutness” is only about forms not meaning ►The forms in the model of use are “about” something ►‘has_topic’ in our demo ontology ►The entities in ontology represent things, they are not about them ►The difference shows up most obviously in negation ►A statement that the patient does not have diabetes is a statement about diabetes ►But ‘not diabetes’ is not a kind of diabetes in the model of meaning ►Any statement about the negation of a parent or the assertion of a child concept is potentially about the concept ►A second order/espistemic notion

9 Therefore create a separate ‘ontology’ of forms and form elements ►Model of meaning ►All patients have a body temperature ►Model of use ►Only some forms about patients have an entry for body temperature ►And on some it is optional and on some mandatory ►But the choice is the same on whole families of forms ►We don’t have to make the choices one at a time ►Or make the same change in many places ►We do not want to distort our model of patients to say that only some have a body temperature! ►But we don’t want to record it except when it is relevant

10 From model of meaning to model of use ►The model of meaning represents “What it is sensible to say about…” ►The basic unit is the thing being described ►The model of use represents “What is it is relevant and useful to say about…” ►And the priorities and work flows of the user ►The basic unit is the task being performed

11 Transformation from model of meaning to use ►Which things it is sensible to say are relevant cannot be determined automatically ►Some day we may have a sufficiently strong model of medicine to infer it, but not likely ►Local and personal as well as scientific and logical ►THERE IS NO ONE WAY ►People work differently ►Therefore they need different mechanisms ►But somehow we need to keep them all consistent ►And propagate changes through them smoothly ►Fractal tailoring ►Smooth evolution

12 But forms and tasks also have logical forms ►Managing forms and uses through their logical forms is a powerful tool for maintaining conssitency ►Model driven architecture - but a model of forms and tasks?

13 Class hierarchy for forms and use/task structure: “Unfolding” Ontology Class diagram view Contents Use/Activity View

14 A simple example Managing types of forms ►Fractal indexing: a simple example ►Organise forms by ►Setting ►User ►Task ►Condition ►Medium (e.g. Browser, XForms, Thick client, PDA, …)

15 Even for a trivial example there are many possibilities (A variant of the “exploding bicycle”) Number of potential forms: 11 x 3 x 6 x 8 x 5 = 7920 A very few are actually relevant A sparse subset of a combinatorially large space

16 An inferred lattice of a few relevant form types

17 Can add a dimension for clinical trials ►Trial_1 is a trial for obese patients ►All patients on the trial must be obese ►All patients are to have a fasting cholesterol at all contacts ►In this case inference is simple

18 Before & after clasification

19 Form assembled by inference / “inheritance”

20 Digression on metadata ►A much abused term ►Two forms of metadata rarely distinguished ►Data about the data - and specifically about classes ►“Whales are an endangered specieis” ►Is not about any individual whale, but the class of whales ►Data about the representation ►The class in this ontology for whales was authored by Alan Rector on the basis of Wikipedia ►Is not about whales, or the class of whales - ►Is about the artifact - “Thiks ontology for whales” ►An information artefact ►Data about the form of the representation ►The data about whales is held an RDF data store following Schema X

21 Plan for the next sessions ►Our experience in dealing with models of use in different contexts ►GALEN ►Drug ontology ►IOTA Anesthesia Patient Safety Association terminology ►Possibly a bit more about Clinergy ►None quite fit your needs