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CDSS-1 CSE 5095 Clinical Decision Support Systems in Biomedical Informatics and their Limitations Alberto De la Rosa Algarín Computer Science & Engineering.

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Presentation on theme: "CDSS-1 CSE 5095 Clinical Decision Support Systems in Biomedical Informatics and their Limitations Alberto De la Rosa Algarín Computer Science & Engineering."— Presentation transcript:

1 CDSS-1 CSE 5095 Clinical Decision Support Systems in Biomedical Informatics and their Limitations Alberto De la Rosa Algarín Computer Science & Engineering University of Connecticut, Storrs

2 CDSS-2 CSE 5095Overview  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 CDSS-3 CSE 5095 Clinical Decisions  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

4 CDSS-4 CSE 5095 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 CDSS-5 CSE 5095Goal  The goal of clinical decision support systems (CDSS) is to emulate the clinician’s thought process during diagnosis.

6 CDSS-6 CSE 5095 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 CDSS-7 CSE 5095History  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 CDSS-8 CSE 5095History  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 CDSS-9 CSE 5095Types  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 CDSS-10 CSE 5095 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 CDSS-11 CSE 5095 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 CDSS-12 CSE 5095 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 CDSS-13 CSE 5095 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 CDSS-14 CSE 5095 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 CDSS-15 CSE 5095 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 CDSS-16 CSE 5095 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 CDSS-17 CSE 5095 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 CDSS-18 CSE 5095 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 CDSS-19 CSE 5095 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 CDSS-20 CSE 5095 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 CDSS-21 CSE 5095 Pathfinder’s Deductive Reasoning Model

22 CDSS-22 CSE 5095 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 CDSS-23 CSE 5095 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 CDSS-24 CSE 5095 DiagnosisPro’s User Interface

25 CDSS-25 CSE 5095 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 CDSS-26 CSE 5095 Heart Disease Program’s Differential Summary

27 CDSS-27 CSE 5095 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 CDSS-28 CSE 5095 Clinical Knowledge Summaries’ User Interface

29 CDSS-29 CSE 5095 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 CDSS-30 CSE 5095 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 CDSS-31 CSE 5095 VisualDx’s User Interface

32 CDSS-32 CSE 5095 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 CDSS-33 CSE 5095 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 CDSS-34 CSE 5095 EON System Architecture

35 CDSS-35 CSE 5095 Snapshot of our clinical decision support systems

36 CDSS-36 CSE 5095Limitations  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 CDSS-37 CSE 5095 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 CDSS-38 CSE 5095 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 CDSS-39 CSE 5095 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 CDSS-40 CSE 5095 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 CDSS-41 CSE 5095 Limitations: System Performance

42 CDSS-42 CSE 5095 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 CDSS-43 CSE 5095 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 CDSS-44 CSE 5095 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 CDSS-45 CSE 5095 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 CDSS-46 CSE 5095 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 CDSS-47 CSE 5095 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 CDSS-48 CSE 5095 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 CDSS-49 CSE 5095 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.

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