1. Alberto De la Rosa Algarín
Clinical Decision Support Systems in Biomedical Informatics and their Limitations
Alberto De la Rosa Algarín
Computer Science & Engineering
University of Connecticut, Storrs

2. Overview 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

3. Clinical Decisions Two types of clinical decisions:
Diagnosis decisions
Diagnosis process
Done analyzing to determine the cause of sickness
Used to determine which questions to ask in order to make better diagnosis decisions

4. Requirements for excellent decision-making
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.

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

6. Definition of Decision Support Systems
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.

7. History 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.

8. History
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

9. Types Information 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.

10. Requirements of a CDSS
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

11. Requirements: Patient Data Acquisition
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.

12. Requirements: Patient Data Validation
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.

13. Requirements: Medical Knowledge Modeling
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.]

14. Requirements: Medical Knowledge Elicitation
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.

15. Requirements: Medical Knowledge Representation
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.

16. Requirements: Medical Knowledge Reasoning
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.

17. Requirements: System Performance
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.

18. Requirements: Integration to the Workflow
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.

19. Existing Systems
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.

20. Pathfinder (1992)
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.

21. Pathfinder’s Deductive Reasoning Model

22. Iliad (1988) 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.

23. DiagnosisPro (1993)
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.

24. DiagnosisPro’s User Interface

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

26. Heart Disease Program’s Differential Summary

27. Clinical Knowledge Summaries (CKS) (2007)
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.

28. Clinical Knowledge Summaries’ User Interface

29. Dxplain (1987)
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

30. VisualDx (2006) 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.

31. VisualDx’s User Interface

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

33. EON System (1996) 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.

34. EON System Architecture

35. Snapshot of our clinical decision support systems

36. Limitations
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.

37. Limitations: Patient’s Role
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.

38. Limitations: Patient’s Role
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.

39. Limitations: Usability
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.

40. Limitations: Usability
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 non-digital and digital data acquisition.
For example: TIMOS LINK
Preference on data input changes by person.

41. Limitations: System Performance

42. Limitations: System Performance
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.

43. Limitations: Knowledge Sharing
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.

44. Limitations: Knowledge Sharing
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.

45. Limitations: Knowledge Maintenance
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.

46. Limitations: Knowledge Maintenance
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.

47. Limitations: Security
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.

48. Limitations: Security
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.

49. Concluding Remarks 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.