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Lecture 3: Informatics skills (Part 1) –
communicating, structuring, and questioning
Dr. Martin Chapman
Principles of Health Informatics (7MPE1000). https://martinchapman.co.uk/teaching
Lecture structure
1. How can communication go wrong? (Communicating)
2. How can we improve communication? (Structuring and
Questioning)
3. How can we find the data we need? (Searching)
4. What do we do with data once we have it? (Making decisions)
3. and 4. will be covered in Lecture 4 (Informatics Skills Part 2)
Learning outcomes
1. To understand how principles from informatics can guide the way we
work.
Informatics; the study of information: representation, processing, and how
it is communicated.
It all comes back to interventions…
2. To understand how principles from informatics can influence clinical
practice.
In a way, Lecture 2 was ‘Informatics Skills Part 0’.
Principles from Informatics we will touch upon
1. Grice’s maxims
2. Knowledge vs. task vs. placeholder-oriented structure
3. Information design (only briefly!)
4. Set theory and queries
5. Query performance measures
Communicating
Recall: 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
How do inputs
actually reach our
system?
Data channels
Sinks
1 + 2 X, Y
X=1,
Y=2
Data model (language) Labelled data
Knowledge base
Input
Output
3
Z = X + Y
First-hand (verbal)
Second-hand (verbal)
Written (data or
literature)
Device
Data channels
Sinks
X=1;
Y=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
We’ll assume our data
is pre-labelled.
?
Let’s instead imagine
there are two people on
the end of this channel…
How can communication go wrong?
How can communication go wrong? (1)
Sinks
X=1;
Y=2
X=1;
Y=2
X=1;
Y=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
?
How can communication go wrong? (1)
1. Distortion
How can communication go wrong? (2)
Sinks
X=1;
Y=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
How can communication go wrong? (2)
1. Distortion
2. Unsuitable communication method
How can communication go wrong? (3)
Sinks
A=1;
B=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
A, B
Data model (language)
A=1;
B=2
A=1;
B=2
A=1;
B=2
?
How can communication go wrong? (3)
1. Distortion
2. Unsuitable communication method
3. Lack of shared data model
How can communication go wrong? (4)
Sinks
X=1;
Y=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
X, Y
Data model (language)
Knowledge base
Z = X - Y
-1 3
X=1;
Y=2
How can communication go wrong? (4)
1. Distortion
2. Unsuitable communication method
3. Lack of shared data model
4. Lack of shared knowledge base
How can communication go wrong? (5)
Sinks
X=1;
Y=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
X, Y
Data model (language)
Knowledge base
Z = X + Y
3
X=1;
Y=2
4
4
4
4
perceptual
bias
attention
How can communication go wrong? (5)
1. Distortion
2. Unsuitable communication method
3. Lack of shared data model
4. Lack of shared knowledge base
5. Fundamental model issues
How can communication go wrong? (6)
Sinks
X=1;
Y=2
X, Y
Data model (language)
Knowledge base
Z = X + Y
X, Y
Data model (language)
Knowledge base
Z = X + Y
?
How can communication go wrong? (6)
1. Distortion
2. Unsuitable communication method
3. Lack of shared data model
4. Lack of shared knowledge base
5. Fundamental model issues
6. Interruptions
Aside: Interruptions quantified
How can we improve communication?
Structuring and Questioning
Grice’s cooperative principle
From logic, a branch of informatics:
‘Make your contribution such as is required, at the stage at
which it occurs, by the accepted purpose or direction of the
talk exchange in which you are engaged.’
In other words, each participant in a conversation should do their
best to make it succeed.
Grice, H. P. (1975)
‘Logic and Conversation’
Grice’s maxims
From this principle follows four maxims, or rules to achieve the
principle in practice:
1. Manner – be orderly in the way you communicate
2. Quantity – be sufficiently informative
3. Relevance – only say what is pertinent to the conversation at
hand
4. Quality – be truthful in what you say
How do we implement Grice’s maxims, and which
communication issues can they help us address?
To structure the remainder of this lecture, we will consider how to
implement Grice’s maxims, and which of the communication issues
seen previously are solved by doing so.
Note 1: We won’t look at all the maxims. Similarly, we will not solve
all our communication issues. Communicating effectively is hard!
Note 2: This is only a very loose pairing to provide us with some
structure.
Manner
Maxim 1
Maxim 1: Manner
There is a clear link between adhering to the first Gricean maxim of
manner – ‘be orderly in the way you communicate’ – and reducing
interruptions (Issue 6).
To adhere to this maxim, there are a number of strategies that can
be employed, including consulting alternative information sources or
delaying the proposed information.
First, a simple example…
Quantity
Maxim 2
Maxim 2: Quantity
A lack of both a shared data model (Issue 3) and a shared knowledge
base (Issue 4) can be addressed by implementing Grice’s maxim of
quantity – ‘be sufficiently informative’.
We can be sufficiently informative by communicating not only data
but the models needed to interpret that data.
In other words, we need to think about the structure of our
communications.
Structure: (1) Additional information
To understand how to structure of our communications, and the
supplementary information that needs to be shared, we need to
balance an assessment of the knowledge of our recipient with the
effort associated with sharing that knowledge.
The informatics concepts of knowledge-oriented, task-oriented and
placeholder-based approaches can help us with this.
Structure: (1) Additional information (Knowledge-
oriented)
If we think the recipient knows how to complete a task (in this case
processing X and Y), we can simply send our models as they are.
This knowledge-oriented approach
represents the least effort to the
sender, but the most to the receiver as,
in this instance, they need to know that
an equation is required.
Z = X - Y
Structure: (1) Additional information (Task-oriented)
If we don’t think the recipient knows how to complete a task (in this
case processing X and Y), we can label the models according to how
they are used.
Z = X - Y
This task-oriented approach represents
more effort to the sender, but much
less to the receiver, as they can simply
follow the label.
In general though, this approach is
restrictive as we are limiting the models
to a specific range of tasks.
To process X=1 and Y=2:
Structure: (1) Additional information (Placeholder-
oriented)
The task-oriented approach also results in duplication.
To process X=1 and Y=2:
Z = X - Y
To process X=3 and Y=4:
If we want to send through multiple
task descriptions to help the receiver
(potentially for future messages), we
may end up sending multiple copies of
the model.
Z = X - Y
Structure: (1) Additional information (Placeholder-
oriented)
The task-oriented approach also results in duplication.
To process X=1 and Y=2:
Z = X - Y
To process X=3 and Y=4:
If we want to send through multiple
task descriptions to help the receiver
(potentially for future messages), we
may end up sending multiple copies of
the model.
Instead, we can combine the data and
task-oriented approaches into a
placeholder-based approach.
We now pass
two things
Structure: (2) Information presentation
Communication supplementary information is no good if the
information itself is not correctly presented.
The concept of information design, from informatics, can guide us
with this:
Good information design also helps with distortion (Issue 1)
X=1; Y=2; Z = X
+ Y
Z = X + Y
X=1; Y=2
Equation
Operands
Structure: (3) Standard template
We can take this idea further by considering a standard template for
the information we communicate, which embodies good information
design.
If this template becomes recognisable, then this expected structure
makes it easier to interpret information.
Knowledge
Data
If someone knows that knowledge always comes
before data in our messages, that will help them
to interpret them quickly.
Standard templates are particularly
important for patient handover, to provide
a clear record of what the former clinician
said to the current clinician.
Relevance
Maxim 3
Maxim 3: Relevance
Remaining relevant in our communications is a good way to address
issues around human attention (Issue 5).
A point at which it is easy to lose relevance in our communications
is when asking questions.
As such, we need to consider carefully how we formulate questions.
We can do this by being precise in our questioning and learning from
questions we have asked previously.
Questioning: Precision
Boolean logic, taken from informatics, can provide us with a precise
way to relate concepts when we are asking questions.
Note 1: I am not suggesting that questions between two humans
should literally be formulated this way, however this is an example of
the type of thinking concepts from informatics provide us with, and
teaches us the power and importance of precision.
Note 2: Conversely, this is a very powerful way to ‘question’
databases.
Questioning: Precision (Boolean logic - NOT)
This allows us to ask for all results
containing the term ‘health’ but not
‘informatics’ (the not here would be implied,
broadly, if we excluded it).
health
informatics
Questioning: Precision (Boolean logic – OR and AND)
Ask for results containing either term, or
both. Note that, in a sense, this is the default
behaviour when entering multiple terms.
Ask for results containing only both terms.
Note that putting quotes around both terms
would also ask for exact matches
health informatics
health informatics
health informatics
health informatics
Questioning: Precision (Boolean logic – XOR)
At the time of writing, I do not believe there
is an equivalent of this within a search
engine.
health informatics
health
informatics
Questioning: Evaluation
We can use query performance measures (from statistics, which has
lots of overlaps with informatics) to determine how good our search
queries were once we have issued them.
Note: Again, I am not suggesting we actually quantitively evaluate
our questions in this way (!), however this is, again, an example of
the type of thinking concepts from informatics provide us with, and
the importance of evaluating the questions asked.
Questioning: Evaluation (Query Performance
Measures)
‘KCL
Principles of
Health Inf’
‘KCL History’
‘UCL Institute
of Health Inf’
Q: ‘Health Informatics’
‘KCL Physics’
Questioning: Evaluation (Query Performance
Measures – TP and FN)
‘KCL
Principles of
Health Inf’
‘UCL Institute
of Health Inf’
Q: ‘Health Informatics’
The items that we want that are
included in our search are called True
Positives.
Any items that are missed by our
search (i.e. we wanted them but they
were not retrieved) are called False
Negatives.
‘KCL History’
‘KCL Physics’
Search results
Questioning: Evaluation (Query Performance
Measures – TP and FN)
‘KCL
Principles of
Health Inf’
‘UCL Institute
of Health Inf’
Q: ‘Health Informatics’
The items that we want that are
included in our search are called True
Positives.
Any items that are missed by our
search (i.e. we wanted them but they
were not retrieved) are called False
Negatives.
We can calculate a true-positive rate
(or sensitivity) as the proportion of
true positives from amongst those
results that we wanted, or
TP/(TP+FN).
‘KCL History’
‘KCL Physics’ 50%, in this case.
Questioning: Evaluation (Query Performance
Measures – TN and FP)
‘KCL
Principles of
Health Inf’
‘KCL History’
‘UCL Institute
of Health Inf’
Q: ‘Health Informatics’
The items that we don’t want that are
not included in our search are called
True Negatives.
Any unwanted items that are included
by our search (i.e. we didn’t want
them but they were retrieved anyway)
are called False Positives.
‘KCL Physics’
Search results
Questioning: Evaluation (Query Performance
Measures – TN and FP)
‘KCL
Principles of
Health Inf’
‘KCL History’
‘UCL Institute
of Health Inf’
Q: ‘Health Informatics’
The items that we don’t want that are
not included in our search are called
True Negatives.
Any unwanted items that are included
by our search (i.e. we didn’t want
them but they were retrieved anyway)
are called False Positives.
We can calculate a true-negative rate
(or specificity) as the proportion of
true negatives from amongst those
results that we did not want, or
TN/(TN+FP).
‘KCL Physics’
50%, again.
Questioning: Evaluation (Query Performance
Measures – Other)
False-positive rate: FP/(FP+TP) – incorrectly detected
False-negative rate: FN/(FN+TN) – incorrectly excluded
Precision or Positive Predictive Value (PPV): TP/(TP+FP) - correct
matches from all matches Or just True-positive rate
Summary
To understand how principles from informatics can guide the way we
work.
• Communication is hard, and many things can go wrong.
• We can look to ideas from informatics to help.
• We can use ideas from logic, such as Grice’s maxims and Boolean
logic, to guide how we structure our communications or ask questions.
• Similar ideas, such as knowledge organisation and information design,
help us with structure; and query performance measures can help us
evaluate our communications.
• While we may not apply these ideas directly, they can still inform how
we act.
It all comes back to interventions…
To understand how principles from informatics can influence clinical
practice.
• If there are communication problems, it is difficult for a clinician
to deliver interventions.
• Using principles from informatics, we can improve communication
and thus intervention delivery.
• Because our information systems also need to leverage
communication channels to gather data, then these improvements
also impact our ability to automate the delivery of interventions.
References and Images
Enrico Coiera. Guide to Health Informatics (3rd ed.). CRC Press, 2015.
https://www.riomed.com/electronic-patient-records-impact-on-healthcare-industry/
https://en.wikipedia.org/wiki/Alice_and_Bob
https://nulab.com/learn/design-and-ux/funny-venn-diagrams-to-inspire-you/

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Principles of Health Informatics: Informatics skills - communicating, structuring, and questioning

  • 1. Lecture 3: Informatics skills (Part 1) – communicating, structuring, and questioning Dr. Martin Chapman Principles of Health Informatics (7MPE1000). https://martinchapman.co.uk/teaching
  • 2. Lecture structure 1. How can communication go wrong? (Communicating) 2. How can we improve communication? (Structuring and Questioning) 3. How can we find the data we need? (Searching) 4. What do we do with data once we have it? (Making decisions) 3. and 4. will be covered in Lecture 4 (Informatics Skills Part 2)
  • 3. Learning outcomes 1. To understand how principles from informatics can guide the way we work. Informatics; the study of information: representation, processing, and how it is communicated. It all comes back to interventions… 2. To understand how principles from informatics can influence clinical practice. In a way, Lecture 2 was ‘Informatics Skills Part 0’.
  • 4. Principles from Informatics we will touch upon 1. Grice’s maxims 2. Knowledge vs. task vs. placeholder-oriented structure 3. Information design (only briefly!) 4. Set theory and queries 5. Query performance measures
  • 6. Recall: 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 How do inputs actually reach our system?
  • 7. Data channels Sinks 1 + 2 X, Y X=1, Y=2 Data model (language) Labelled data Knowledge base Input Output 3 Z = X + Y First-hand (verbal) Second-hand (verbal) Written (data or literature) Device
  • 8. Data channels Sinks X=1; Y=2 X, Y Data model (language) Knowledge base Z = X + Y We’ll assume our data is pre-labelled. ? Let’s instead imagine there are two people on the end of this channel…
  • 10. How can communication go wrong? (1) Sinks X=1; Y=2 X=1; Y=2 X=1; Y=2 X, Y Data model (language) Knowledge base Z = X + Y ?
  • 11. How can communication go wrong? (1) 1. Distortion
  • 12. How can communication go wrong? (2) Sinks X=1; Y=2 X, Y Data model (language) Knowledge base Z = X + Y
  • 13. How can communication go wrong? (2) 1. Distortion 2. Unsuitable communication method
  • 14. How can communication go wrong? (3) Sinks A=1; B=2 X, Y Data model (language) Knowledge base Z = X + Y A, B Data model (language) A=1; B=2 A=1; B=2 A=1; B=2 ?
  • 15. How can communication go wrong? (3) 1. Distortion 2. Unsuitable communication method 3. Lack of shared data model
  • 16. How can communication go wrong? (4) Sinks X=1; Y=2 X, Y Data model (language) Knowledge base Z = X + Y X, Y Data model (language) Knowledge base Z = X - Y -1 3 X=1; Y=2
  • 17. How can communication go wrong? (4) 1. Distortion 2. Unsuitable communication method 3. Lack of shared data model 4. Lack of shared knowledge base
  • 18. How can communication go wrong? (5) Sinks X=1; Y=2 X, Y Data model (language) Knowledge base Z = X + Y X, Y Data model (language) Knowledge base Z = X + Y 3 X=1; Y=2 4 4 4 4 perceptual bias attention
  • 19. How can communication go wrong? (5) 1. Distortion 2. Unsuitable communication method 3. Lack of shared data model 4. Lack of shared knowledge base 5. Fundamental model issues
  • 20. How can communication go wrong? (6) Sinks X=1; Y=2 X, Y Data model (language) Knowledge base Z = X + Y X, Y Data model (language) Knowledge base Z = X + Y ?
  • 21. How can communication go wrong? (6) 1. Distortion 2. Unsuitable communication method 3. Lack of shared data model 4. Lack of shared knowledge base 5. Fundamental model issues 6. Interruptions
  • 23. How can we improve communication? Structuring and Questioning
  • 24. Grice’s cooperative principle From logic, a branch of informatics: ‘Make your contribution such as is required, at the stage at which it occurs, by the accepted purpose or direction of the talk exchange in which you are engaged.’ In other words, each participant in a conversation should do their best to make it succeed. Grice, H. P. (1975) ‘Logic and Conversation’
  • 25. Grice’s maxims From this principle follows four maxims, or rules to achieve the principle in practice: 1. Manner – be orderly in the way you communicate 2. Quantity – be sufficiently informative 3. Relevance – only say what is pertinent to the conversation at hand 4. Quality – be truthful in what you say
  • 26. How do we implement Grice’s maxims, and which communication issues can they help us address? To structure the remainder of this lecture, we will consider how to implement Grice’s maxims, and which of the communication issues seen previously are solved by doing so. Note 1: We won’t look at all the maxims. Similarly, we will not solve all our communication issues. Communicating effectively is hard! Note 2: This is only a very loose pairing to provide us with some structure.
  • 28. Maxim 1: Manner There is a clear link between adhering to the first Gricean maxim of manner – ‘be orderly in the way you communicate’ – and reducing interruptions (Issue 6). To adhere to this maxim, there are a number of strategies that can be employed, including consulting alternative information sources or delaying the proposed information. First, a simple example…
  • 30. Maxim 2: Quantity A lack of both a shared data model (Issue 3) and a shared knowledge base (Issue 4) can be addressed by implementing Grice’s maxim of quantity – ‘be sufficiently informative’. We can be sufficiently informative by communicating not only data but the models needed to interpret that data. In other words, we need to think about the structure of our communications.
  • 31. Structure: (1) Additional information To understand how to structure of our communications, and the supplementary information that needs to be shared, we need to balance an assessment of the knowledge of our recipient with the effort associated with sharing that knowledge. The informatics concepts of knowledge-oriented, task-oriented and placeholder-based approaches can help us with this.
  • 32. Structure: (1) Additional information (Knowledge- oriented) If we think the recipient knows how to complete a task (in this case processing X and Y), we can simply send our models as they are. This knowledge-oriented approach represents the least effort to the sender, but the most to the receiver as, in this instance, they need to know that an equation is required. Z = X - Y
  • 33. Structure: (1) Additional information (Task-oriented) If we don’t think the recipient knows how to complete a task (in this case processing X and Y), we can label the models according to how they are used. Z = X - Y This task-oriented approach represents more effort to the sender, but much less to the receiver, as they can simply follow the label. In general though, this approach is restrictive as we are limiting the models to a specific range of tasks. To process X=1 and Y=2:
  • 34. Structure: (1) Additional information (Placeholder- oriented) The task-oriented approach also results in duplication. To process X=1 and Y=2: Z = X - Y To process X=3 and Y=4: If we want to send through multiple task descriptions to help the receiver (potentially for future messages), we may end up sending multiple copies of the model. Z = X - Y
  • 35. Structure: (1) Additional information (Placeholder- oriented) The task-oriented approach also results in duplication. To process X=1 and Y=2: Z = X - Y To process X=3 and Y=4: If we want to send through multiple task descriptions to help the receiver (potentially for future messages), we may end up sending multiple copies of the model. Instead, we can combine the data and task-oriented approaches into a placeholder-based approach. We now pass two things
  • 36. Structure: (2) Information presentation Communication supplementary information is no good if the information itself is not correctly presented. The concept of information design, from informatics, can guide us with this: Good information design also helps with distortion (Issue 1) X=1; Y=2; Z = X + Y Z = X + Y X=1; Y=2 Equation Operands
  • 37. Structure: (3) Standard template We can take this idea further by considering a standard template for the information we communicate, which embodies good information design. If this template becomes recognisable, then this expected structure makes it easier to interpret information. Knowledge Data If someone knows that knowledge always comes before data in our messages, that will help them to interpret them quickly. Standard templates are particularly important for patient handover, to provide a clear record of what the former clinician said to the current clinician.
  • 39. Maxim 3: Relevance Remaining relevant in our communications is a good way to address issues around human attention (Issue 5). A point at which it is easy to lose relevance in our communications is when asking questions. As such, we need to consider carefully how we formulate questions. We can do this by being precise in our questioning and learning from questions we have asked previously.
  • 40. Questioning: Precision Boolean logic, taken from informatics, can provide us with a precise way to relate concepts when we are asking questions. Note 1: I am not suggesting that questions between two humans should literally be formulated this way, however this is an example of the type of thinking concepts from informatics provide us with, and teaches us the power and importance of precision. Note 2: Conversely, this is a very powerful way to ‘question’ databases.
  • 41. Questioning: Precision (Boolean logic - NOT) This allows us to ask for all results containing the term ‘health’ but not ‘informatics’ (the not here would be implied, broadly, if we excluded it). health informatics
  • 42. Questioning: Precision (Boolean logic – OR and AND) Ask for results containing either term, or both. Note that, in a sense, this is the default behaviour when entering multiple terms. Ask for results containing only both terms. Note that putting quotes around both terms would also ask for exact matches health informatics health informatics health informatics health informatics
  • 43. Questioning: Precision (Boolean logic – XOR) At the time of writing, I do not believe there is an equivalent of this within a search engine. health informatics health informatics
  • 44. Questioning: Evaluation We can use query performance measures (from statistics, which has lots of overlaps with informatics) to determine how good our search queries were once we have issued them. Note: Again, I am not suggesting we actually quantitively evaluate our questions in this way (!), however this is, again, an example of the type of thinking concepts from informatics provide us with, and the importance of evaluating the questions asked.
  • 45. Questioning: Evaluation (Query Performance Measures) ‘KCL Principles of Health Inf’ ‘KCL History’ ‘UCL Institute of Health Inf’ Q: ‘Health Informatics’ ‘KCL Physics’
  • 46. Questioning: Evaluation (Query Performance Measures – TP and FN) ‘KCL Principles of Health Inf’ ‘UCL Institute of Health Inf’ Q: ‘Health Informatics’ The items that we want that are included in our search are called True Positives. Any items that are missed by our search (i.e. we wanted them but they were not retrieved) are called False Negatives. ‘KCL History’ ‘KCL Physics’ Search results
  • 47. Questioning: Evaluation (Query Performance Measures – TP and FN) ‘KCL Principles of Health Inf’ ‘UCL Institute of Health Inf’ Q: ‘Health Informatics’ The items that we want that are included in our search are called True Positives. Any items that are missed by our search (i.e. we wanted them but they were not retrieved) are called False Negatives. We can calculate a true-positive rate (or sensitivity) as the proportion of true positives from amongst those results that we wanted, or TP/(TP+FN). ‘KCL History’ ‘KCL Physics’ 50%, in this case.
  • 48. Questioning: Evaluation (Query Performance Measures – TN and FP) ‘KCL Principles of Health Inf’ ‘KCL History’ ‘UCL Institute of Health Inf’ Q: ‘Health Informatics’ The items that we don’t want that are not included in our search are called True Negatives. Any unwanted items that are included by our search (i.e. we didn’t want them but they were retrieved anyway) are called False Positives. ‘KCL Physics’ Search results
  • 49. Questioning: Evaluation (Query Performance Measures – TN and FP) ‘KCL Principles of Health Inf’ ‘KCL History’ ‘UCL Institute of Health Inf’ Q: ‘Health Informatics’ The items that we don’t want that are not included in our search are called True Negatives. Any unwanted items that are included by our search (i.e. we didn’t want them but they were retrieved anyway) are called False Positives. We can calculate a true-negative rate (or specificity) as the proportion of true negatives from amongst those results that we did not want, or TN/(TN+FP). ‘KCL Physics’ 50%, again.
  • 50. Questioning: Evaluation (Query Performance Measures – Other) False-positive rate: FP/(FP+TP) – incorrectly detected False-negative rate: FN/(FN+TN) – incorrectly excluded Precision or Positive Predictive Value (PPV): TP/(TP+FP) - correct matches from all matches Or just True-positive rate
  • 51. Summary To understand how principles from informatics can guide the way we work. • Communication is hard, and many things can go wrong. • We can look to ideas from informatics to help. • We can use ideas from logic, such as Grice’s maxims and Boolean logic, to guide how we structure our communications or ask questions. • Similar ideas, such as knowledge organisation and information design, help us with structure; and query performance measures can help us evaluate our communications. • While we may not apply these ideas directly, they can still inform how we act.
  • 52. It all comes back to interventions… To understand how principles from informatics can influence clinical practice. • If there are communication problems, it is difficult for a clinician to deliver interventions. • Using principles from informatics, we can improve communication and thus intervention delivery. • Because our information systems also need to leverage communication channels to gather data, then these improvements also impact our ability to automate the delivery of interventions.
  • 53. References and Images Enrico Coiera. Guide to Health Informatics (3rd ed.). CRC Press, 2015. https://www.riomed.com/electronic-patient-records-impact-on-healthcare-industry/ https://en.wikipedia.org/wiki/Alice_and_Bob https://nulab.com/learn/design-and-ux/funny-venn-diagrams-to-inspire-you/