Principles of Health Informatics: Models, information, and information systems
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The lecture explores fundamental concepts in health informatics, emphasizing the roles of models, information, and information systems. It discusses how models serve as representations of reality tailored for specific purposes, aiding in understanding and diagnosing patient conditions. Furthermore, it outlines the structure and implications of systems, particularly information systems, in transforming data into actionable knowledge for clinical interventions.
Principles of Health Informatics: Models, information, and information systems
- 1. Lecture 2: Models, information, and information systems Dr. Martin Chapman Principles of Health Informatics (7MPE1000). https://martinchapman.co.uk/teaching
- 2. Learning outcomes 1. To understand three basic theoretical concepts in Informatics: models, information and systems. Informatics; the study of information: representation, processing, and how it is communicated. It all comes back to interventions… 2. To determine how models and systems, and the information they produce when applied to data, allow us to understand a patient’s condition, and act accordingly.
- 3. Lecture structure Use models as our primary framing: 1. What is a model? (Models) 2. Using models to understand the world (Information) 3. Models with multiple entities (Information Systems)
- 4. What is a model? Models
- 5. Abstraction • An abstract model is simpler than the real thing. • The model represents a snapshot of the real thing, which will become more inaccurate over time. • Choices have been made about what to include in the model. • As a consequence of the above, lots of different models can exist of the same thing. The above shows us that an (abstract) model is built for a particular purpose.
- 6. Instantiation • In contrast to abstraction, we add more details when we build something from a template model. • The model will become dated over time, and less relevant to the real world. • A model is altered when we build something from it. • As a result of the above, no two instantiations are exactly the same. This reinforces the idea that models are always built for a particular purpose, as there is no such thing as a general-purpose (template) model.
- 7. Abstraction + Instantiation A model is both informed by the world (abstraction) and can be realised in the world (instantiation). This may be a literal realisation (e.g. building an aeroplane) or a realisation that allows us to understand the world. Abstraction Instantiation
- 8. 2. Using models to understand the world Information Why might we build a model?
- 9. Understanding how our model works We may have built a model with the purpose of understanding whether planes float on water. We can test our model and come up with some observations: 1. With pontoons, our model floats. 2. Without pontoons, our model sinks.
- 10. Understanding how the world works Knowledge, from our model: If a plane does not have pontoons, then it will sink. Data: The plane does not have pontoons. Information = Knowledge (model) + Data: The plane will sink.
- 11. Information model types Knowledge, from our model: If a plane does not have pontoons, then it will sink. We refer to this type of model as an inference procedure. It is supported by three other (sub-) information models: (1) A data model (2) A knowledge base (3) An ontology We can break down the actual content of our model…
- 12. (1) Data model Thing B? Thing C? Thing A? By themselves, things in our model are really just symbols.
- 13. (1) Data model Propeller Plane 1. Plane terminology: ‘Pontoon, propeller, plane…’ Pontoon We need a language to describe planes:
- 14. (1) Data model 1. Plane terminology: ‘Pontoon, propeller, plane…’ 2. Plane grammar: ‘Pontoons attach to planes, propellers attach to planes…’ We need a language to describe planes: Propeller Plane Pontoon
- 15. (1) Data model This language, our data model, then allows us to label and quantify the data we have in the real world. This is often referred to as a database. Plane Pontoon = 0 Propeller = 1 Language Data Propeller Plane Pontoon
- 16. (2) Knowledge base Planes without pontoons will sink. Planes without propellers cannot fly. The difference between inference rules and a knowledge base is subtle. Inference rules are often of the form ‘If X then Y’, whereas our knowledge base consists of statements. Inference rules formulate our knowledge so that it can be applied to data.
- 17. Planes without pontoons will sink. Planes without propellers cannot fly. (3) Ontology To keep order in our knowledge base, we introduce an ontology (a formal conceptualisation of the world). A data model for our knowledge base
- 18. Planes without propellers cannot fly. Planes without pontoons will sink. (3) Ontology To keep order in our knowledge base, we introduce an ontology (a formal conceptualisation of the world). Our ontology tells us the concepts we can have in our knowledge base… Plane parts State
- 19. Planes without propellers cannot fly. Planes without pontoons will sink. (3) Ontology To keep order in our knowledge base, we introduce an ontology (a formal conceptualisation of the world). Our ontology tells us the concepts we can have in our knowledge base… …and the relationship between those concepts, e.g. ‘Plane parts affect state’ …this stops invalid knowledge, such as ‘Planes without pontoons cannot propellers’ (Plane parts affect plane parts).
- 20. Knowledge acquisition and application In this way, we see how models can capture knowledge that has been gained from the world, and in turn apply that knowledge to the world. Abstraction + Knowledge Acquisition Instantiation + Knowledge Application Instantiation is not now building from the model, but instead applying knowledge from it
- 21. Knowledge Limitations Recall that models always capture particular parts of a domain depending on their purpose. In our running example, we are interested in determining whether planes float, and included pontoons in our model accordingly.
- 22. Knowledge Limitations However, these decisions often limit the knowledge a model can contain, and thus its applicability. For example, as it includes pontoons, we cannot use or model to capture knowledge that can be used to determine whether a plane with wheels can land on the ground. NB. We also need to consider the language used in our model, and how widely it can be interpreted.
- 23. Aside Models are more than just physical replicas
- 24. Models are more than just physical replicas So far we’ve been thinking of physical models as real, physical replicas we can interact with in the world to gain some understanding (e.g. a Lego plane). It is good to anchor your understanding to this idea. In reality, models can be more than this, and everything we know about them so far still applies…
- 25. Example 1: A computer program As a simple advancement, we could consider a plane we realise in a computer as a model rather than a physical plane. We still abstract, use that abstraction to gain some knowledge, and apply that knowledge to understand the world (with limitations). Abstraction Instantiation
- 26. Example 2: Clinical guidelines Clinical guidelines are also an example of a model. They capture knowledge about the world based on clinical understanding, which can then be applied back to new situations. Note that a model like this may be purely held in a clinician’s head. These are known as mental models. Abstraction Instantiation More to come on guidelines!
- 27. 3. Models with multiple entities Information Systems
- 28. Models so far… So far we’ve only considered our model as consisting of a single entity, such as one plane.
- 30. Systems Abstraction Instantiation If we capture multiple entities, we are instead creating a type of model known as a system.
- 31. Features of a system Systems (models): 1. Have inputs and outputs 2. Have emergent behaviour 3. Are impacted by their environment 4. Have component parts 5. Can operate on feedback 6. Are arbitrary and purposive, like regular models We’re still going to use physical models for these examples, but remember models are more than just physical replicas…
- 32. 1. Inputs and outputs Passengers are the input to our system, and their arrival at their destination is the output. There is a transformation. The state of the entities in the system is also affected by these inputs, such as the location of the planes.
- 33. 2. Emergent behaviour Planes have individual behaviours that we know about and distinguish them from the environment around them, such as flying. However some behaviours, such as the route taken, we cannot know in advance, as they are dependent on the behaviour of other entities in the system (e.g. other routes taken). This is known as emergent behaviour.
- 34. 3. Environment impact A significant factor in emergent behaviour is the impact of the system’s environment. A plane having to change its altitude mid-flight, for example—thus affecting the routes of other planes (emergent behaviour)—might be due to the weather.
- 35. 4. Sub-systems The entities in a system can often be grouped together to form sub- systems. In our example, we can consider different airspaces as different sub-systems. The outputs from one sub- system often form the inputs to another. For example, information about having one less plane in a given airspace is input as one additional plane in another.
- 36. 5. Feedback When the same sub-system both gives output and receives this as input, we call this feedback. Feedback is typically mediated by a central control sub-system Positive feedback moves a system from one state to another, while negative feedback restricts a system to operating within a certain state. Our controller might combine the two to allow a plane to move from one airspace to another, but not to return, so the journey progresses. The plane has left, now do not let it return.
- 37. 6. Systems are purposive and arbitrary We explored several properties of models earlier. Two key observations were that (1) abstract models are built for a particular purpose (there is no such thing as a general-purpose model) and, as a consequence, (2) lots of different models can exist of the same thing. The same is true of systems (models): 1. They are built for a purpose, e.g. exploring optimal approaches to air traffic control (purposive). 2. They is no such thing as a correct system definition; many exist (arbitrary). Many different ways to model air traffic control.
- 39. What about knowledge? Sinks Data model (language) Labelled data Knowledge base Input Output
- 40. Information systems What we were really doing earlier when we were understanding whether a plane was going to sink was applying a special type of system where the interacting components are models themselves (!). Much like the systems we’ve seen, there are inputs and outputs, components interact, etc. This type of system is known as an information system. The notion of an information system being a ‘model of models’ is a little tough, so here’s another example:
- 41. A calculator as an information system Sinks 1 + 2 X, Y X=1, Y=2 Data model (language) Labelled data Knowledge base Input Output 3 Z = X + Y
- 42. A calculator as an information system Sinks Abstraction Instantiation
- 43. Automation Capturing knowledge in this way is useful, because we can then provide it to a computer in order to automate its application. If we cannot fully represent the model in a computer, then human intervention may be required (semi-automated). Similarly, computers may play more of a supportive role, organising data or providing visualisation of that data. Sinks
- 44. It all comes back to interventions… If we can automate the application of knowledge to health data, then we can automate (the introduction of) interventions. If we can’t fully use information systems to automate this application, then they can assist clinicians in the delivery of interventions. Diabetes
- 45. Summary • ‘To understand three basic theoretical concepts in Informatics: models, information and systems.’ • Models are (imperfect) representations of the world, built for a particular purpose. • Models allow us to transform data into information • A system is a model with multiple components, and an information system is a type of model that, as stated, transforms data into information, using component parts that are, themselves, models.
- 46. Summary • ‘To determine how models and systems, and the information they produce when applied to data, allow us to understand a patient’s condition, and act accordingly.’ • (Mental) models support diagnosis and treatment in clinical practice. • Information systems present the opportunity to automate these processes.
- 47. References and Images Enrico Coiera. Guide to Health Informatics (3rd ed.). CRC Press, 2015. https://www.lego.com/en-gb/service/buildinginstructions/3178 https://lemonbin.com/difference-between-seaplane-floatplane/ https://www.youtube.com/watch?v=EeY77RoNwz8 https://www.nbcnews.com/science/science-news/largest-electric-plane-yet-completed-its-first-flight-it-s-n1221401 https://www.cnet.com/tech/gaming/microsoft-flight-simulator-takes-flight-on-xbox-game-pass-tuesday/ https://www.healthline.com/health/ozone-therapy https://www.toypro.com/en/product/43771/eileen