Health Sciences 4700 Spring 2009 - Chapter 9

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Health Sciences 4700 Spring 2009 - Chapter 9

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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

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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  Database and query system  Model management system  Computations that represent domain models  Knowledge-based management system  “Captured” knowledge and reasoning  User interface  Input/output, documentation
Types of advanced DSSs  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  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  Current research and evidence far outstrips any individual’s capacity to absorb it  Experience and expertise is learned over many years  Senior clinicians are often not involved from direct care  Knowledge and reasoning can help prevent medical errors
Applications of DSS  Reminders and alerts  Provide complete information at point of care  Prescribingsystems  Therapy planning and critiquing  Identify inconsistencies, errors, omissions  Image recognition  Identify potential abnormalities, changes
Applications of DSS  Diagnostic systems  Help identify conditions based on symptoms  Among earliest examples of DSS  Support evidence-based practice  May provide structured access to research  Elsevier’s MD/FirstConsult (www.mdconsult.com)
Are DSSs effective?  In principle, they should be!  Improved patient safety  Improved quality of care  Improved efficiency
Are DSSs effective?  However …  Research isn’t completely convincing, particularly in terms of patient outcomes  Design may not have user in mind  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  Clinicaldecision making generally involves “semi-structured” decisions  Some but not all information is known  How can we represent knowledge in a DSS?  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)  AIis 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”  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  Robotics  Pattern recognition  Expert systems*  Most applicable to decision support systems
Types of expert systems  Rule-based systems  Case-based reasoning  Fuzzy logic  Bayesian networks (belief systems)  Neural networks
Rule-based systems  Rule-basedsystems are based on “expert knowledge” rather than data  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  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  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?

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