Health Sciences 4700 Spring 2009 - Chapter 9

Health Sciences 4700
Spring 2009
Chapter 9
Decision Support Systems


What are decision support
systems (DSSs)?


Computer systems that assist decision
makers




Combine “machine” or “artificial intelligence” with
information about the domain of interest
Intended to make knowledge more readily
available at the point of care


Simple DSSs







CPOE system with drug interaction alerts

EHR with alerts for missing or unknown
information
Database with structured queries to find
relevant information
Differential diagnosis systems (e.g.,
FirstConsult)


Components of an advanced
DSS


Data management system




Model management system




Computations that represent domain models

Knowledge-based management system




Database and query system

“Captured” knowledge and reasoning

User interface


Input/output, documentation


Types of advanced DSSs
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Expert systems
Neural networks
Intelligent agents
Knowledge management systems
Information systems
Group support systems
Enterprise planning and management
systems


Characteristics of DSS








Used in un-/semi-structured decision
contexts
Support decision makers, not replace them
Rely on data and models
Generally developed using an evolutionary,
iterative process
Focus should be on complete system
(including people and procedures, not just
computers)


Application domains


Administrative decision support

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Access and organization of data
Analysis of data
Analysis of multiple data sources
Accounting and modeling of data
Forecasting from data
Optimization and comparison of alternatives
Suggestions of action based on comparisons


Application domains


Clinical decision support





Access to information (EHR)
Analysis of data (single or multiple sources)
Diagnosis
Recommendations for treatments/procedures


Why DSSs?


Information overload




Experience and expertise is learned over
many years




Current research and evidence far outstrips any
individual’s capacity to absorb it

Senior clinicians are often not involved from direct
care

Knowledge and reasoning can help prevent
medical errors


Applications of DSS


Reminders and alerts



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Prescribing systems
Therapy planning and critiquing




Provide complete information at point of care

Identify inconsistencies, errors, omissions

Image recognition


Identify potential abnormalities, changes


Applications of DSS


Diagnostic systems


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Help identify conditions based on symptoms
Among earliest examples of DSS
Support evidence-based practice

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May provide structured access to research
Elsevier’s MD/FirstConsult (www.mdconsult.com)

Are DSSs effective?


In principle, they should be!

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Improved patient safety
Improved quality of care
Improved efficiency


Are DSSs effective?


However …


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Research isn’t completely convincing, particularly
in terms of patient outcomes
Design may not have user in mind


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Reminders and alerts are valuable, but if they interfere

with care, they won’t be used (pop-ups)

May be perceived as de-humanizing care




Neither caregivers nor patients are willing to turn over
decision making to a “machine”
DSS can reduce non-data driven interaction


Understanding how DSSs work


Clinical decision making generally involves
“semi-structured” decisions


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How can we represent knowledge in a DSS?
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Some but not all information is known
Descriptive knowledge: facts and data

Practical knowledge: steps and instructions
Inferential knowledge: reasoning from theory and
facts (“intelligence”)

How do we maintain currency of knowledge?


Artificial intelligence (AI)


AI is field of computer science that attempts
to make computers “reason”




Study of intelligent behavior and attempts to create
computer systems that behave that way

Spectrum of AI ranges from “weak” to
“strong”

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Weak AI assumes that some reasoning can be
approximated through programming
Strong AI believes that computers can think like
humans (or vice versa)

Areas of AI


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Robotics
Pattern recognition
Expert systems*


Most applicable to decision support systems


Types of expert systems

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Rule-based systems
Case-based reasoning
Fuzzy logic
Bayesian networks (belief systems)
Neural networks


Rule-based systems


Rule-based systems are based on “expert
knowledge” rather than data




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Attempt to recreate the thought processes of a
diagnostician or other knowledgeable person
How would that person respond to a given set of
circumstances or conditions?
Components include knowledge base, inference
engine, and user interface


Rule-based systems


A patient complains of difficulty breathing


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No fever means the patient probably doesn’t have
a respiratory infection

A normal chest X-ray will probably preclude
pneumonia (and corroborates the lack of a
respiratory infection)
A TB skin test and a normal chest X-ray also
strengthens the lack of infection hypothesis
Patient reports problem occurs after exercise


Rule-based systems


Patient reports problem occurs after exercise

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

No history of cardiac or pulmonary disease
Nature of the problem is wheezing and shortness of
breath, which is characteristic of asthmatics

Most likely conclusion: exercise-induced
asthma

How do you get a computer system to act like
an expert?


Rule-based systems


Knowledge in expert systems is represented
as rules or IF-THEN statements




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Programming takes inputs through the user
interface
Inputs are passed to the inference engine, which
applies the rules in a logical pattern
Result is returned through the user interface


Rule-based systems
IF (condition) THEN (result)
ELSE alternative result
--------------------------------------------------------------“condition” is a Boolean value that compares inputs to
knowledge
IF “condition” is true THEN “result” is executed
ELSE [“condition” is false] “alternative result” is
executed
Alternative can be another IF-THEN, and so forth


Rule-based systems (example)
IF dipstick = pink THEN test <- normal;
IF dipstick = lilac AND symptoms = none

THEN test <- normal;
…
Note that if “dipstick” has the value “pink” then the first
condition is true and “test” is assigned the value
“normal”
If “dipstick” is “pink”, then the second condition cannot
be true, no matter what the value of “symptoms” is!
User interface would return the value of “test”


Rule-based systems (example)
IF dipstick = pink THEN test <- normal
ELSE IF dipstick = lilac AND
symptoms = none
THEN test <- normal
ELSE …
Note that once “test” is assigned the value “normal” no
other execution is necessary!
In previous example, you would have to ensure that
“dipstick” was NOT pink for every subsequent
statement in order to make a correct diagnosis


Rule-based systems


Problems with rule-based systems


Extracting complete knowledge is difficult

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There is always some measure of uncertainty in a rule

Complex systems require a large number of rules
Domains tend to be narrowly focused
Maintaining currency is difficult
Input range is often large
Interaction with system tends to be difficult