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Clinical Decision Support Systems in Clinical Decision Support Systems in Biomedical Informatics Biomedical Informatics

and their Limitationsand their Limitations

Alberto De la Rosa AlgarínComputer Science & EngineeringUniversity of Connecticut, Storrs

alberto.delarosa.algarin@engr.uconn.edu

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Overview Overview

Clinical Decisions Clinical Decisions What types of clinical decisions exist? Requirements for excellent decision-making Definition of Decision Support Systems

HistoryHistory First possibility of a CDSS First prototype and the shortcomings Better CDSS (MYCIN, HELP, Leeds System)

Existing SystemsExisting Systems Pathfinder, Iliad, DiagnosisPro, CKS, HDP, etc.

LimitationsLimitations Patient’s Role, Usability (and performance),

Knowledge sharing and maintenance and Security

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Clinical DecisionsClinical Decisions

Two types of clinical decisions:Two types of clinical decisions: Diagnosis decisions Diagnosis process

Diagnosis decisionsDiagnosis decisions Done analyzing to determine the cause of sickness

Diagnosis processDiagnosis process Used to determine which questions to ask in order

to make better diagnosis decisions

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Requirements for excellent decision-makingRequirements for excellent decision-making

Accurate data:Accurate data: Bad data is useless obviously Good data is equally useless if there is no

knowledge on how to apply it.

Pertinent knowledgePertinent 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 skillsAppropriate problem-solving skills The glue between the correct use of accurate and

pertinent knowledge.

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GoalGoal

The goal of clinical decision support systems (CDSS) The goal of clinical decision support systems (CDSS) is to emulate the clinician’s thought process during is to emulate the clinician’s thought process during diagnosis.diagnosis.

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Definition of Decision Support SystemsDefinition of Decision Support Systems

A decision support system is a system in which one or A decision support system is a system in which one or more computers and computer programs assist in more computers and computer programs assist in decision making by providing information.decision making by providing information.

They can exist as hardware-software solutions or stand They can exist as hardware-software solutions or stand alone software applications.alone software applications.

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HistoryHistory

The possibility first appeared in 1959 [Ledley & The possibility first appeared in 1959 [Ledley & Lusted]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.]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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HistoryHistory

After this, several CDSS appeared that tackled the After this, several CDSS appeared that tackled the previous pitfalls (MYCIN, Leeds System and HELP)previous pitfalls (MYCIN, Leeds System and HELP)

MYCIN [Shortliffe, 1976]MYCIN [Shortliffe, 1976] A consultation system for patients with infections

Leeds Abdominal Pain System [De Dombal et al., Leeds Abdominal Pain System [De Dombal et al., 1972]1972] A system for the diagnosis of acute abdominal pain

HELP [Warner, 1979]HELP [Warner, 1979] A system to alert clinicians in case of

abnormalities in patient records

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TypesTypes

Information Management SystemsInformation Management Systems Provide an environment for the storage and retrieval

of information. Decision is left to the clinician.

Focusing Attention SystemsFocusing Attention Systems Alert clinicians when a conflict arises. Follow simple logic.

Patient-specific Recommendation SystemsPatient-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 CDSSRequirements of a CDSS

Clinical decision support systems must satisfy the Clinical decision support systems must satisfy the following requirements in order to be widely accepted following requirements in order to be widely accepted and used: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 AcquisitionRequirements: Patient Data Acquisition

There is no standard way to acquire data.There is no standard way to acquire data.

Current methods range from keyboard to natural Current methods range from keyboard to natural language processing.language processing.

Some health care professionals even use Some health care professionals even use intermediaries like nurses or secretaries.intermediaries like nurses or secretaries.

The end goal is to capture patient data without The end goal is to capture patient data without disrupting the workflow.disrupting the workflow.

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Requirements: Patient Data ValidationRequirements: Patient Data Validation

Tons of coding systems exist for the validation of Tons of coding systems exist for the validation of patient data.patient data.

Sadly none of the existing coding systems capture the Sadly none of the existing coding systems capture the subtle differences and the high details of the patient’s subtle differences and the high details of the patient’s health care.health care.

A clinical decision support system should be able to A clinical decision support system should be able to work with both detailed and general patient data.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 ModelingRequirements: Medical Knowledge Modeling

Knowledge modeling is necessary for the Knowledge modeling is necessary for the identification of relationships and concepts.identification of relationships and concepts.

Modeling is also used to decide what patient data is Modeling is also used to decide what patient data is pertinent and what strategies to use.pertinent and what strategies to use.

These tasks require a large amount of modeling.These tasks require a large amount of modeling.

Luckily several methods exist that do a pretty good Luckily several methods exist that do a pretty good job regarding medical knowledge modeling.job regarding medical knowledge modeling. Common KADS [De Hoog et al., 1994] CASNET [Weiss et al.]

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Requirements: Medical Knowledge ElicitationRequirements: Medical Knowledge Elicitation

Current clinical decision support systems obtain Current clinical decision support systems obtain knowledge and then work directly with the clinician.knowledge and then work directly with the clinician.

But a clinical decision support system should be able But a clinical decision support system should be able to evoke useful knowledge seamlessly.to evoke useful knowledge seamlessly.

But this implies methods that facilitate the use of But this implies methods that facilitate the use of knowledge-bases.knowledge-bases.

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Requirements: Medical Knowledge RepresentationRequirements: Medical Knowledge Representation

The interpretation of trends is intuitive for clinicians.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 Clinical decision support systems must be able to represent the knowledge like trends.represent the knowledge like trends.

But to achieve this, the clinical decision support But to achieve this, the clinical decision support system must emulate the clinicians intuition.system must emulate the clinicians intuition.

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Requirements: Medical Knowledge ReasoningRequirements: Medical Knowledge Reasoning

Computer systems have the capability of storing large Computer systems have the capability of storing large amounts of factual knowledge.amounts of factual knowledge.

Clinical decision support systems should be able toClinical 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 The solution for this requirement is in the realm of artificial intelligence.artificial intelligence.

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Requirements: System PerformanceRequirements: System Performance

Clinical decision support systems should be able to Clinical decision support systems should be able to use ALL the pertinent data and knowledge available.use ALL the pertinent data and knowledge available.

At the same time, the systems should be able to use At the same time, the systems should be able to use the most updated data and knowledge.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 On top of it all, decision support should appear in an instant manner while maintaining high accuracy.instant manner while maintaining high accuracy.

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Requirements: Integration to the WorkflowRequirements: Integration to the Workflow

The most difficult of the requirements to fulfill.The most difficult of the requirements to fulfill.

Integration to the workflow requires fulfilling a couple Integration to the workflow requires fulfilling a couple of previous requirements:of previous requirements: Patient Data Acquisition Knowledge Representation System Performance

If a clinical decision support system is able to fulfill If a clinical decision support system is able to fulfill these previous three requirements, integration is given.these previous three requirements, integration is given.

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Existing SystemsExisting Systems

There has been a surge of clinical decision support There has been a surge of clinical decision support systems from the 1980’s to the present day.systems from the 1980’s to the present day.

Their applications range from infectious disease Their applications range from infectious disease diagnosis to cardiovascular treatment predictions.diagnosis to cardiovascular treatment predictions.

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Pathfinder (1992)Pathfinder (1992)

Explains, acquires, represents and manipulates Explains, acquires, represents and manipulates uncertain medical knowledge.uncertain medical knowledge. Uses probability and decision theory as strategies

Deductive reasoning is used to provide diagnosisDeductive 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 drivenThe 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 ModelPathfinder’s Deductive Reasoning Model

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Iliad (1988)Iliad (1988)

Uses Boolean and Bayesian frames to represent Uses Boolean and Bayesian frames to represent knowledge.knowledge.

The system has four basic components:The system has four basic components: Inference engine User interface Data driver Best information algorithm

Currently used as a teaching tool for medical students.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)DiagnosisPro (1993)

Uses differential diagnosis to remind the user of Uses differential diagnosis to remind the user of possible diagnoses in an effort to reduce medical possible diagnoses in an effort to reduce medical errors.errors.

The knowledge-base is huge:The knowledge-base is huge: 11,000 diseases 30,000 findings 300,000 relationships

Information for the knowledge-base is taken from Information for the knowledge-base is taken from medical sources such as JAMA, Oxford Textbook of medical sources such as JAMA, Oxford Textbook of Medicine and others.Medicine and others.

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DiagnosisPro’s User InterfaceDiagnosisPro’s User Interface

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Heart Disease Program (HDP) (1980’s – 90’s)Heart Disease Program (HDP) (1980’s – 90’s)

Assists the clinician in anticipating the effects of Assists the clinician in anticipating the effects of therapy in cardiovascular disorders.therapy in cardiovascular disorders.

Uses strategies as:Uses strategies as: Knowledge-base and physiologic model Probabilities Constraints Differential Diagnosis

The user interface is menu drivenThe user interface is menu driven

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Heart Disease Program’s Differential SummaryHeart Disease Program’s Differential Summary

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Clinical Knowledge Summaries (CKS) (2007)Clinical Knowledge Summaries (CKS) (2007)

Helps clinicians make decisions about a patient’s Helps clinicians make decisions about a patient’s health and provides strategies on how to use those health and provides strategies on how to use those decisions.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.Built on the existing PRODIGY knowledge-base.

It is a web-based clinical decision support system, It is a web-based clinical decision support system, accessible from around the world.accessible from around the world.

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Clinical Knowledge Summaries’ User InterfaceClinical Knowledge Summaries’ User Interface

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Dxplain (1987)Dxplain (1987)

Combines characteristics of an electronic medical Combines characteristics of an electronic medical textbook with characteristics of a medical reference textbook with characteristics of a medical reference system.system.

Provides information on different diseasesProvides information on different diseases Emphasizes in signs and symptoms

The knowledge-base includes:The knowledge-base includes: 2,400+ diseases 5,000+ symptoms, signs, lab data and clinical

findings

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VisualDx (2006)VisualDx (2006)

Java-based and image drivenJava-based and image driven Designed for point-of-care reference

One of the main functions is the facilitation of image One of the main functions is the facilitation of image matching for the end user, achieved with: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 Used to develop differential diagnoses based on morphologic and patient driven search.morphologic and patient driven search.

Its focus is on infectious diseases.Its focus is on infectious diseases.

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VisualDx’s User InterfaceVisualDx’s User Interface

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INTERNIST-1 / QMR Project (1974 - 80’s)INTERNIST-1 / QMR Project (1974 - 80’s)

Designed to provide assistance in general internal Designed to provide assistance in general internal medicinemedicine Both INTERNIST-1 and QMR rely on the

INTERNIST-1 knowledge-base

INTERNIST-1 works as a high-powered diagnostic INTERNIST-1 works as a high-powered diagnostic consultant tool.consultant tool.

QMR acts as an information toolQMR acts as an information tool Provides ways to manipulate and review diagnostic

information for the knowledge-base

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EON System (1996)EON System (1996)

Consists of four general purpose components: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 The design principles that create a base for the EON system are problem-solving methods and domain system are problem-solving methods and domain ontologies.ontologies.

Because of the difficulties of long-term maintenance Because of the difficulties of long-term maintenance of knowledge-bases, PROTÉGÉ-II is used.of knowledge-bases, PROTÉGÉ-II is used.

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EON System ArchitectureEON System Architecture

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Snapshot of our clinical decision support systemsSnapshot of our clinical decision support systems

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LimitationsLimitations

Existing clinical decision support systems suffer from Existing clinical decision support systems suffer from limitations difficult to overcome.limitations difficult to overcome. Patient’s Role Usability System Performance Knowledge Sharing and Maintenance Security

Such limitations slow the adoption rate of clinical Such limitations slow the adoption rate of clinical decision support systems.decision support systems.

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Limitations: Patient’s RoleLimitations: Patient’s Role

The patient’s role is not defined in clinical decision The patient’s role is not defined in clinical decision support systems.support systems.

Patients are just the source of data for the clinical Patients are just the source of data for the clinical decision support system to work on.decision support system to work on.

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Limitations: Patient’s RoleLimitations: Patient’s Role

The answers to those questions do not only have The answers to those questions do not only have implications in a moral or ethical sense, but can also implications in a moral or ethical sense, but can also provide the patient evidence for legal matters.provide the patient evidence for legal matters.

The patient will want to know every detail regarding The patient will want to know every detail regarding his health.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 Clinicians would like to withhold information for different matters.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: UsabilityLimitations: Usability

Biggest hurdle current clinical decision support Biggest hurdle current clinical decision support systems have to overcome.systems have to overcome. Health care professionals don’t like change.

No current system integrates in the workflow No current system integrates in the workflow seamlessly.seamlessly. This is the result of shortcomings in system

performance and human-computer interaction.

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Limitations: UsabilityLimitations: Usability

A busy clinician would only want pertinent A busy clinician would only want pertinent information.information.

A less busy clinician, or one who needs every detail to A less busy clinician, or one who needs every detail to reach a diagnosis, would appreciate a high level of reach a diagnosis, would appreciate a high level of detail.detail.

Clinicians do not like to modify the usual workflow to Clinicians do not like to modify the usual workflow to input data.input data.

New methods aim to bridge the gap between non-New methods aim to bridge the gap between non-digital and digital data acquisition.digital and digital data acquisition. For example: TIMOS LINK

Preference on data input changes by person.Preference on data input changes by person.

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Limitations: System PerformanceLimitations: System Performance

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Limitations: System PerformanceLimitations: System Performance

Accurate support is the purpose of clinical decision Accurate support is the purpose of clinical decision support systems.support systems.

Current methods are not accurate enough to be widely Current methods are not accurate enough to be widely used.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 At the same time, no matter how accurate, if a decision support takes to long to appear, it is useless.decision support takes to long to appear, it is useless.

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Limitations: Knowledge SharingLimitations: Knowledge Sharing

Knowledge-bases are specific to each clinical decision Knowledge-bases are specific to each clinical decision support system.support system.

Its actually one of the “selling points” of current Its actually one of the “selling points” of current solutions.solutions. Used to differentiate existing systems from others

in an effort to stand above. The bigger the knowledge-base, the more decision The bigger the knowledge-base, the more decision

support (and more accurate) the system is able to support (and more accurate) the system is able to offer.offer.

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Limitations: Knowledge SharingLimitations: Knowledge Sharing

Having a centralized knowledge-base, or at least a Having a centralized knowledge-base, or at least a framework that allows for current knowledge-bases to framework that allows for current knowledge-bases to be shared, would improve reliability and accuracy be shared, would improve reliability and accuracy across different clinical decision support systems.across different clinical decision support systems.

Standards exist in an attempt to consolidate.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.We need a standard of standards.

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Limitations: Knowledge MaintenanceLimitations: Knowledge Maintenance

Maintaining knowledge and managing pieces of the Maintaining knowledge and managing pieces of the clinical decision support systems are critical for clinical decision support systems are critical for successful delivery of decision support.successful delivery of decision support.

Knowledge-base maintenance requires a lot of work.Knowledge-base maintenance requires a lot of work.

Current methods rely on periodical update by humans.Current methods rely on periodical update by humans.

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Limitations: Knowledge MaintenanceLimitations: Knowledge Maintenance

Periodical updates by human intervention is a Periodical updates by human intervention is a primitive approach to knowledge maintenance.primitive approach to knowledge maintenance.

The latest knowledge and information could be put on The latest knowledge and information could be put on hold for months until the knowledge-base’s update is hold for months until the knowledge-base’s update is due.due.

This goes against one of the original requirements: This goes against one of the original requirements: Clinical decision support systems should utilize the

latest knowledge available.

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Limitations: SecurityLimitations: Security

Clinical decision support systems provide an equal Clinical decision support systems provide an equal level of recommendations to whoever has access to the level of recommendations to whoever has access to the system.system.

Clinical decision support systems that exist as part of Clinical decision support systems that exist as part of an EMR have some level of security.an EMR have some level of security.

Systems that exist as stand alone solutions do not.Systems that exist as stand alone solutions do not.

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Limitations: SecurityLimitations: Security

We have to remember that other professionals (such as We have to remember that other professionals (such as nurses, pharmacists, etc.) are an equal part of the nurses, pharmacists, etc.) are an equal part of the patient’s well-being.patient’s well-being.

It is natural to think that clinical decision support It is natural to think that clinical decision support systems should have some level of role-based access systems should have some level of role-based access control.control.

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Concluding RemarksConcluding Remarks

A long road lies ahead of CDSS.A long road lies ahead of CDSS.

Improvements must be made in order to increase the Improvements must be made in order to increase the adoption of clinical decision support systems.adoption of clinical decision support systems. Usability System Performance Knowledge Handling

Existing technologies and ideas offer possibilities to Existing technologies and ideas offer possibilities to resolve several of the limitations.resolve several of the limitations.

Other limitations require a compromise in order to be Other limitations require a compromise in order to be solved.solved.