In the information sciences the definitions below are the very foundation of informatics: p. 13
Data consists of facts. Facts are observations or measurements about the world. For example, ‘today is Tuesday’
Knowledge defines relationships between data. The rule ‘tobacco smoking causes lung cancer’ is an example of knowledge. Such knowledge is created by identifying recurring patterns in data, for example across many different patients. We learn that events usually occur in a certain sequence, or that an action typically has a specific effect. Through the process of model abstraction, these observations are then codified into general rules about how the world works.
As well as learning such generalized ‘truths’ about the world, once can also learn knowledge that is specific to a particular circumstance. For example, we can create patient specific knowledge by observing a patient’s state over time. By abstracting away patterns in what is observed, one can arrive at specific knowledge such as ‘following treatment with anti-hypertensive medication, there has been no decrease in patient’s blood pressure over the last 2 months.
Information is obtained by the application of knowledge to data. Thus, the datum that ‘the patient’s blood pressure is 125/70 mmHg’ yields information if it tells us something new. In the context of managing a patient’s high blood pressure, using our general knowledge of medicine, and patient specific knowledge, the datum may allow us to draw the inference that the patient’s blood pressure is now under control.
How variations in the structure of clinical messages affect the way in which they are interpreted: p.36-43
What a message is meant to say when it is created, and what the receiver of a message understands, may not be the same. This is because what we humans understand is profoundly shaped by the way data are presented to us, and by the way we react to different data presentations. Thus it is probably as important to structure data in a way so that they can be best understood, as it is to ensure that the data are correct in the first place. What a clinician understands after seeing the data in a patient record and what the data actually show are very different things.
When sending a message, we have to make assumptions about the knowledge that the receiver has, and use that to shape our message. There is no point in explaining what is already known, but is equally important not to miss out important details that the receiver should know to draw the right conclusions. The knowledge share between individuals is sometimes called common ground.
The structure of a message determines how it will be understood. The way clinical data are structured can alter the conclusions a clinician will draw from data.
The message that is sent may not be the message that is received. The effectiveness of communication between two agents is dependent upon:
- the communication channel which will vary in capacity to carry data and noise which distorts the message
- the knowledge possessed by the agents, and the common ground between them
- the resource limitations of agents including cognitive limits on memory and attention
- the context within which the agents find themselves which dictate which resources are available and the competing tasks at hand.
Grice’s conversational maxims provide a set of rules for conducting message examples:
- maximum of quantity: say on what is needed.
- maximum of quality: make you contribution one that is true.
- maximum of relevance: say only what is pertinent to the context of the conversation at the moment.
- maximum of manner: avoid obscurity of expression, ambiguity, be brief and orderly.
Medical record’s basic functions: p.112
- provides means of communicating between staff who are actively managing a patient.
- during the period of active management of a patient’s illness, the record strives to be the single data access point for workers managing a patient. All test results, observations and so forth should be accessible through it.
- the record offers and informal ‘working space’ to record ideas and impressions that help build up a consensus view, over the period of care, of what is going on with the patient.
- once an episode of care has been completed, the record ultimately forms the single point at which all clinical data are archived, for long-term use.
The traditional way the EMR – record used in care is to be a passive supporter of clinical activity. An active EMR may suggest what patient information needs to be collected, or it might assemble clinical data in a way that assists a clinician in the visualization of a patient’s clinical condition. p.119
There are two quite separate aspects to record systems:
- the physical nature of the way individuals interact with it
- the way information is structured when entered into or retrieved from the system.
A summative evaluation can be made in three broad categories:
- a user’s satisfaction with the service
- clinical outcome changes resulting from using the service
- any economic benefit of the service
Technology can be applied to a problem in a technology-drive or a problem-driven manner. Information systems should be created in a problem-driven way, starting with an understanding of user information problems. Only then is it appropriate to identify if an how technology should be used.
Providing access methods that are optimized to local needs can enlarge the range of clinical context s in which evidence is used. p.177
AI systems are limited by the data they have access to, and the quality of the knowledge captured withing their knowledge base.
An expert system is a program that captures elements of human expertise and performs reasoning tasks that normally rely on specialist knowledge. Expert systems perform best in straightforward tasks, which have a predefined and relatively narrow scope, and perform poorly on ill-defined tasks that rely on general or common sense knowledge.
An expert system consists of:
- a knowledge base, which contains the rules necessary for the completion of its task
- a working memory in which data and conclusions can be stored
- an inference engine, which matches rules to data to derive its conclusions.