Clinical Decision Support Systems in Biomedical Informatics and their Limitations
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Transcript Clinical Decision Support Systems in Biomedical Informatics and their Limitations
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Clinical Decision Support Systems in
Biomedical Informatics
and their Limitations
Alberto De la Rosa Algarín
Computer Science & Engineering
University of Connecticut, Storrs
[email protected]
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Overview
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Clinical Decisions
What types of clinical decisions exist?
Requirements for excellent decision-making
Definition of Decision Support Systems
History
First possibility of a CDSS
First prototype and the shortcomings
Better CDSS (MYCIN, HELP, Leeds System)
Existing Systems
Pathfinder, Iliad, DiagnosisPro, CKS, HDP, etc.
Limitations
Patient’s Role, Usability (and performance),
Knowledge sharing and maintenance and Security
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Clinical Decisions
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Two types of clinical decisions:
Diagnosis decisions
Diagnosis process
Diagnosis decisions
Done analyzing to determine the cause of sickness
Diagnosis process
Used to determine which questions to ask in order
to make better diagnosis decisions
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Requirements for excellent decision-making
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Accurate data:
Bad data is useless obviously
Good data is equally useless if there is no
knowledge on how to apply it.
Pertinent knowledge
The overload of information affects the process of
decision making in a negative way.
Overload of information can be seen in the ICU
Appropriate problem-solving skills
The glue between the correct use of accurate and
pertinent knowledge.
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Goal
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The goal of clinical decision support systems (CDSS)
is to emulate the clinician’s thought process during
diagnosis.
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Definition of Decision Support Systems
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A decision support system is a system in which one or
more computers and computer programs assist in
decision making by providing information.
They can exist as hardware-software solutions or stand
alone software applications.
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History
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The possibility first appeared in 1959 [Ledley &
Lusted]
With the use of symbolic logic, probability theory
and value theory, the foundations of medical
diagnosis could be understood.
The first prototype appeared in 1964 [Walker et al.]
Issues with logistics, scientific shortcomings
related to medical diagnosis, and the lack of
integration to the workflow made the widespread
use and adoption virtually impossible.
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History
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After this, several CDSS appeared that tackled the
previous pitfalls (MYCIN, Leeds System and HELP)
MYCIN [Shortliffe, 1976]
A consultation system for patients with infections
Leeds Abdominal Pain System [De Dombal et al.,
1972]
A system for the diagnosis of acute abdominal pain
HELP [Warner, 1979]
A system to alert clinicians in case of
abnormalities in patient records
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Types
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Management Systems
Provide an environment for the storage and
retrieval of information.
Decision is left to the clinician.
Focusing
Attention Systems
Alert clinicians when a conflict arises.
Follow simple logic.
Patient-specific
Recommendation Systems
Offer advice to a single patient using the patient’s
medical history.
Can use simple logic, decision theory, cost-benefit
analysis, etc.
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Requirements of a CDSS
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Clinical decision support systems must satisfy the
following requirements in order to be widely accepted
and used:
Patient Data Acquisition and Validation
Medical Knowledge Modeling, Elicitation,
Representation and Reasoning
System Performance
Integration to the Workflow
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Requirements: Patient Data Acquisition
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There is no standard way to acquire data.
Current methods range from keyboard to natural
language processing.
Some health care professionals even use
intermediaries like nurses or secretaries.
The end goal is to capture patient data without
disrupting the workflow.
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Requirements: Patient Data Validation
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Tons of coding systems exist for the validation of
patient data.
Sadly none of the existing coding systems capture the
subtle differences and the high details of the patient’s
health care.
A clinical decision support system should be able to
work with both detailed and general patient data.
And the system’s performance should not be
affected by the type of data.
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Requirements: Medical Knowledge Modeling
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Knowledge modeling is necessary for the
identification of relationships and concepts.
Modeling is also used to decide what patient data is
pertinent and what strategies to use.
These tasks require a large amount of modeling.
Luckily several methods exist that do a pretty good job
regarding medical knowledge modeling.
Common KADS [De Hoog et al., 1994]
CASNET [Weiss et al.]
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Requirements: Medical Knowledge Elicitation
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Current clinical decision support systems obtain
knowledge and then work directly with the clinician.
But a clinical decision support system should be able
to evoke useful knowledge seamlessly.
But this implies methods that facilitate the use of
knowledge-bases.
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Requirements: Medical Knowledge Representation
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The interpretation of trends is intuitive for clinicians.
For example, trends of sickness, trends of the
results of medical treatments.
Clinical decision support systems must be able to
represent the knowledge like trends.
But to achieve this, the clinical decision support
system must emulate the clinicians intuition.
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Requirements: Medical Knowledge Reasoning
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Computer systems have the capability of storing large
amounts of factual knowledge.
Clinical decision support systems should be able to
Discern which knowledge is useful for the task at
hand.
Know how to apply the knowledge in order to
obtain worthy results.
The solution for this requirement is in the realm of
artificial intelligence.
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Requirements: System Performance
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Clinical decision support systems should be able to
use ALL the pertinent data and knowledge available.
At the same time, the systems should be able to use
the most updated data and knowledge.
This implies a lot when we talk about the use of
knowledge-bases.
On top of it all, decision support should appear in an
instant manner while maintaining high accuracy.
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Requirements: Integration to the Workflow
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The most difficult of the requirements to fulfill.
Integration to the workflow requires fulfilling a couple
of previous requirements:
Patient Data Acquisition
Knowledge Representation
System Performance
If a clinical decision support system is able to fulfill
these previous three requirements, integration is given.
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Existing Systems
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There has been a surge of clinical decision support
systems from the 1980’s to the present day.
Their applications range from infectious disease
diagnosis to cardiovascular treatment predictions.
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Pathfinder (1992)
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Explains, acquires, represents and manipulates
uncertain medical knowledge.
Uses probability and decision theory as strategies
Deductive reasoning is used to provide diagnosis
But the system is designed so that no
recommendations are done
The user interface is menu based and mouse driven
Feature category, observed features and differential
diagnosis are the windows in the initial screen.
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Pathfinder’s Deductive Reasoning Model
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Iliad (1988)
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Uses Boolean and Bayesian frames to represent
knowledge.
The system has four basic components:
Inference engine
User interface
Data driver
Best information algorithm
Currently used as a teaching tool for medical students.
Particular cases are simulated so that students learn
how to diagnose.
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DiagnosisPro (1993)
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Uses differential diagnosis to remind the user of
possible diagnoses in an effort to reduce medical
errors.
The knowledge-base is huge:
11,000 diseases
30,000 findings
300,000 relationships
Information for the knowledge-base is taken from
medical sources such as JAMA, Oxford Textbook of
Medicine and others.
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DiagnosisPro’s User Interface
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Heart Disease Program (HDP) (1980’s – 90’s)
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Assists the clinician in anticipating the effects of
therapy in cardiovascular disorders.
Uses strategies as:
Knowledge-base and physiologic model
Probabilities
Constraints
Differential Diagnosis
The user interface is menu driven
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Heart Disease Program’s Differential Summary
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Clinical Knowledge Summaries (CKS) (2007)
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Helps clinicians make decisions about a patient’s
health and provides strategies on how to use those
decisions.
Provides knowledge on topics about common acute
and chronic diseases and their prevention
Offers quick answers on how to manage common
clinical scenarios
Built on the existing PRODIGY knowledge-base.
It is a web-based clinical decision support system,
accessible from around the world.
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Clinical Knowledge Summaries’ User Interface
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Dxplain (1987)
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Combines characteristics of an electronic medical
textbook with characteristics of a medical reference
system.
Provides information on different diseases
Emphasizes in signs and symptoms
The knowledge-base includes:
2,400+ diseases
5,000+ symptoms, signs, lab data and clinical
findings
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VisualDx (2006)
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Java-based and image driven
Designed for point-of-care reference
One of the main functions is the facilitation of image
matching for the end user, achieved with:
Graphical search tools
Knowledge-base of relationships
Thousands of digital images
Used to develop differential diagnoses based on
morphologic and patient driven search.
Its focus is on infectious diseases.
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VisualDx’s User Interface
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INTERNIST-1 / QMR Project (1974 - 80’s)
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Designed to provide assistance in general internal
medicine
Both INTERNIST-1 and QMR rely on the
INTERNIST-1 knowledge-base
INTERNIST-1 works as a high-powered diagnostic
consultant tool.
QMR acts as an information tool
Provides ways to manipulate and review diagnostic
information for the knowledge-base
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EON System (1996)
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Consists of four general purpose components:
Constructs patient-specific treatment plans
Infers high level abstract components
Performs time-oriented queries in time-oriented
patient database
Allows the acquisition of protocol knowledge
The design principles that create a base for the EON
system are problem-solving methods and domain
ontologies.
Because of the difficulties of long-term maintenance
of knowledge-bases, PROTÉGÉ-II is used.
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EON System Architecture
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Snapshot of our clinical decision support systems
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Limitations
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Existing clinical decision support systems suffer from
limitations difficult to overcome.
Patient’s Role
Usability
System Performance
Knowledge Sharing and Maintenance
Security
Such limitations slow the adoption rate of clinical
decision support systems.
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Limitations: Patient’s Role
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The patient’s role is not defined in clinical decision
support systems.
Patients are just the source of data for the clinical
decision support system to work on.
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Limitations: Patient’s Role
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The answers to those questions do not only have
implications in a moral or ethical sense, but can also
provide the patient evidence for legal matters.
The patient will want to know every detail regarding
his health.
After all, patients provide every bit of their
personal information in order to get the best care.
Clinicians would like to withhold information for
different matters.
For example, the clinician would like to be the one
to break the news in case of a serious disease.
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Limitations: Usability
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Biggest hurdle current clinical decision support
systems have to overcome.
Health care professionals don’t like change.
No current system integrates in the workflow
seamlessly.
This is the result of shortcomings in system
performance and human-computer interaction.
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Limitations: Usability
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A busy clinician would only want pertinent
information.
A less busy clinician, or one who needs every detail to
reach a diagnosis, would appreciate a high level of
detail.
Clinicians do not like to modify the usual workflow to
input data.
New methods aim to bridge the gap between nondigital and digital data acquisition.
For example: TIMOS LINK
Preference on data input changes by person.
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Limitations: System Performance
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Limitations: System Performance
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Accurate support is the purpose of clinical decision
support systems.
Current methods are not accurate enough to be widely
used.
QMR’s accuracy being % in ED scenarios.
Iliad’s accuracy being % in ED scenarios.
At the same time, no matter how accurate, if a
decision support takes to long to appear, it is useless.
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Limitations: Knowledge Sharing
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Knowledge-bases are specific to each clinical decision
support system.
Its actually one of the “selling points” of current
solutions.
Used to differentiate existing systems from others
in an effort to stand above.
The bigger the knowledge-base, the more decision
support (and more accurate) the system is able to
offer.
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Limitations: Knowledge Sharing
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Having a centralized knowledge-base, or at least a
framework that allows for current knowledge-bases to
be shared, would improve reliability and accuracy
across different clinical decision support systems.
Standards exist in an attempt to consolidate.
The problem is that there are so many standards,
everyone uses a different one.
We need a standard of standards.
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Limitations: Knowledge Maintenance
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Maintaining knowledge and managing pieces of the
clinical decision support systems are critical for
successful delivery of decision support.
Knowledge-base maintenance requires a lot of work.
Current methods rely on periodical update by humans.
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Limitations: Knowledge Maintenance
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Periodical updates by human intervention is a
primitive approach to knowledge maintenance.
The latest knowledge and information could be put on
hold for months until the knowledge-base’s update is
due.
This goes against one of the original requirements:
Clinical decision support systems should utilize the
latest knowledge available.
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Limitations: Security
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Clinical decision support systems provide an equal
level of recommendations to whoever has access to the
system.
Clinical decision support systems that exist as part of
an EMR have some level of security.
Systems that exist as stand alone solutions do not.
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Limitations: Security
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We have to remember that other professionals (such as
nurses, pharmacists, etc.) are an equal part of the
patient’s well-being.
It is natural to think that clinical decision support
systems should have some level of role-based access
control.
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Concluding Remarks
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A long road lies ahead of CDSS.
Improvements must be made in order to increase the
adoption of clinical decision support systems.
Usability
System Performance
Knowledge Handling
Existing technologies and ideas offer possibilities to
resolve several of the limitations.
Other limitations require a compromise in order to be
solved.
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