Principles of Health Informatics: Clinical decision support systems. Last delivered in 2024. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.

1. Lecture 13: Clinical decision support systems
Dr. Martin Chapman
Principles of Health Informatics (7MPE1000). https://martinchapman.co.uk/teaching

2. Recall: Where are we?
Lecture 2 – models, information and information
systems – informatics definitions.
Lectures 3 + 4 – informatics skills – how can principles
from informatics guide the way we work in healthcare?
Lectures 5 + 6 – everything you need to know about
information systems (designing them, evaluating them,
implementing them…)
Foundation
Built on
Built on

3. Where are we?
Lecture 5 – electronic health records
Lectures 7 + 8 – computable guidelines
Lectures 9 + 10 – communication systems
Lectures 11 + 12 – terminology systems
…
Examples, broadly, of
information systems, which
could be explored in any
order.
Let’s also not forget the
connection with
interventions.
(protocol systems)

4. Lecture structure
1. What is a clinical decision support system (CDSS)?
2. What can a CDSS do?
3. What are the benefits of CDSSs?
4. What might stop a CDSS from being successful?
This is very much the ‘what’ before the ‘how’. The ‘how’ will come
in Lecture 14.

5. Learning outcomes
1. Be able to define, at a high level, a clinical decision support
system (CDSS).
2. Understand the different features offered by a CDSS, and rank
them in terms of their complexity and level of automation.
3. Understand the benefits of a CDSS.
4. Be able to critique the notion of a CDSS, and understand why
the implementation of one may not be successful.

6. What is a clinical decision support system
(CDSS)?

7. Recall: 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
involvement may be required (semi-automated).
Similarly, computers may play more of a supportive role, organising
data or providing visualisation of that data.
Sinks
A computer, as an
information system, could
automatically determine
whether a plane will sink or
not.

8. Recall: It all comes back to public health 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
A computer, as an
information system, could
automatically determine
whether a patient has
diabetes and act accordingly

9. This second example from Lecture 2 was really hinting towards the
concept of a CDSS, which, based on these examples, we can define as:
Assisting human decision-making and improving decision outcomes
in a clinical setting
We later saw an example of a CDSS fitting this description in Lecture
10…
What is a CDSS?

10. Recall: Mobile applications – CONSULT
Sensors (wearables) help to
monitor a patient’s state remotely. We store our data
centrally using a
standard, and
communicate in the
same standard.
We have a reasoning
engine, providing the
DSS component.
Patients (and clinicians) can view
the data collected by the system,
and the recommendations
(interventions) suggested.
The EHR can also
be referenced to
contextualise
sensor readings
and interventions
A real focus on taking
telemonitoring to the next
level in terms of
promoting self-
management.
Martin Chapman, Abigail G-Medhin, et al. Using microservices to design patient-facing
research software. In Proceedings of the IEEE 18th International Conference
on e-Science (e-Science), 2022.

11. This second example from Lecture 2 were really hinting towards the
concept of a CDSS, which, based on these examples, we can define as:
Assisting human decision-making and improving decision outcomes
in a clinical setting
We later saw an example of a CDSS fitting this description in Lecture
10…
To go beyond this simple definition and help us better understand
what a CDSS is, we can consider the types of tasks CDSSs assist with.
What is a CDSS?

12. What can a CDSS do?

13. Automated
Assistive
Complex
Simple
Ranked
diagnoses
MRI
interpretation
Vital
monitoring
What can a CDSS do?
Diagnosis
We’ll categorise examples of
CDSS features as follows:

14. Example: CDSS diagnosis

15. What can a CDSS do?
Therapy planning
Guiding the treatment of a
patient
Automated
Assistive
Activity
templates
Patient-
specific plans
Radiation
dosage
calculation
Complex
Simple

16. What can a CDSS do?
Prescribing
Automated
Assistive
Dosage
calculators
Gene-drug
advice Drug-drug
interaction
identification
Complex
Simple

17. What can a CDSS do?
Process support
The organisation of wider
clinical activities beyond
just diagnosis
Automated
Assistive
Patient flow in
secondary care
Screening
reminders
Guideline
adherence
monitoring
Complex
Simple

18. What can a CDSS do?
Automated
Assistive
Data-based
disease
likelihood
Clinical process
simulation
Risk
predictor
collection
Risk assessment
Complex
Simple

19. What can a CDSS do?
Image recognition
Automated
Assistive
Past case
matching
(case-based
reasoning)
Pattern to
disease
matching
Dynamic
organ
rendering
Complex
Simple

20. What can a CDSS do?
Evidence retrieval
Software shapes our
decisions by selecting the
information we use to
make them
Automated
Assistive
Query
formulation
Search
agents
EHR-
contextualised
search
Complex
Simple

21. What can a CDSS do?
Lab support
Computer support when
ordering or receiving the
results of lab tests
Automated
Assistive
Generating
reports
Addition of
diagnostic
hypothesis
Workflow
guidance
Complex
Simple

22. What are the benefits of CDSSs?
Now we understand what a CDSS
is, let’s discuss its benefits…

23. What are the benefits of CDSSs?
Patient safety
Reduction in
medication errors and
adverse drug events
Enhanced prescribing
behaviour
‘Incorrect dosage
decreased from 2% to
<0.3%’
‘A 55% reduction in
adverse drug events’

24. What are the benefits of CDSSs?
Improved patient
outcomes
Improved clinical
process measures
Time released for
patient care
Quality of care
‘Reduced time to achieve
control of a disease’
‘Issue resolution 29%
shorter when alerts received’
‘Orders completed 63
minutes faster’

25. What are the benefits of CDSSs?
A reduction in cost due to many
of the CDSS features, and their
associated benefits, seen, e.g.
fewer medication errors = fewer
liability costs.
Efficiency of
healthcare delivery

26. What might stop a CDSS being
successful?

27. What might stop a CDSS being successful?
Clinician resistance
Poor technology
infrastructure
Such as limited availability of
EHR data, upon which CDSSs
typically operate
Because a CDSS may not fit
into their workflows.
Complexity of
conditions
Some conditions may not be
amenable to automated
support

28. Summary
A clinical decision support system (CDSS) is defined as ‘assisting
human decision-making and improving decision outcomes in a clinical
setting’.
We have seen how a CDSS assists human decision-making by helping
with tasks such as medication dosage, and how, in practice, this
improves outcomes like patient safety.
While a CDSS has lots of benefits, in practice its impact may be
limited by factors such as the attitudes of clinicians.

29. References and Images
Enrico Coiera. Guide to Health Informatics (3rd ed.). CRC Press, 2015.
Brendan C. Delaney, Vasa Curcin, Anna Andreasson, Theodoros N. Arvanitis, Hilde Bastiaens, Derek Corrigan, Jean
Francois Ethier, Olga Kostopoulou, Wolfgang Kuchinke, Mark McGilchrist, Paul Van Royen, and Peter Wagner.
Translational Medicine and Patient Safety in Europe: TRANSFoRm–Architecture for the Learning Health System in
Europe. BioMed research international, 2015, 2015.
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