Clinical Decision Support SystemsClinical Decision Support Systems
HIMA 4160HIMA 4160
Fall 2009Fall 2009
OutlineOutline
DefinitionsDefinitions
MethodologiesMethodologies
ApplicationsApplications
Probabilistic reasoningProbabilistic reasoning
Decision treeDecision tree22
CDSSCDSS
Providing clinicians or patients with Providing clinicians or patients with clinical knowledge and patient-clinical knowledge and patient-related information, intelligently related information, intelligently filtered or presented at appropriate filtered or presented at appropriate times, to enhance patient caretimes, to enhance patient care
• NOT just physicians …NOT just physicians …• Not just rules and alerts …Not just rules and alerts …• (NOT just computer-based …)(NOT just computer-based …)
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CategoriesCategories
Generating alerts and remindersGenerating alerts and reminders
Diagnostic assistanceDiagnostic assistance
Therapy critiquing and planningTherapy critiquing and planning
Image recognition and interpretationImage recognition and interpretation
And many others …And many others …
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Need for CDSSNeed for CDSS
Limited resources - increased demandLimited resources - increased demand
Need for systems that can improve health Need for systems that can improve health care processes and their outcomes in this care processes and their outcomes in this scenarioscenario
The marriage of medical and technological The marriage of medical and technological advances - to produce a child called Frugal advances - to produce a child called Frugal Efficiency?Efficiency?
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Generalized StructureGeneralized Structure
Knowledge Base
Inference Engine
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Knowledge base, inference Knowledge base, inference engine, and interfaceengine, and interface
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Application AreasApplication Areas
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Workflow OpportunitiesWorkflow Opportunities
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Possible Disadvantages of Possible Disadvantages of CDSSCDSS
Changing relation between patient and the Changing relation between patient and the physicianphysician
Limiting professionals’ possibilities for Limiting professionals’ possibilities for independent problem solvingindependent problem solving
Legal implications - with whom does the Legal implications - with whom does the onus of responsibility lie?onus of responsibility lie?
Information fatigueInformation fatigue1010
Issues for success or failureIssues for success or failure Evaluation of User NeedsEvaluation of User Needs
Top management supportTop management support
Commitment of expertCommitment of expert
Integration IssuesIntegration Issues
Human Computer InterfaceHuman Computer Interface
Incorporation of domain knowledgeIncorporation of domain knowledge
Consideration of social and organizational context of Consideration of social and organizational context of the CDSthe CDS
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Evaluation of Clinical Decision Evaluation of Clinical Decision Support SystemsSupport Systems
Criteria for success of CDSSCriteria for success of CDSS Aspects for consideration during Aspects for consideration during
evaluationevaluation
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Criteria for a clinically useful Criteria for a clinically useful DSSDSS
Knowledge based on best evidenceKnowledge based on best evidence Knowledge fully covers problemKnowledge fully covers problem Knowledge can be updatedKnowledge can be updated Data actively used drawn from existing Data actively used drawn from existing
sources sources Performance validated rigorouslyPerformance validated rigorously System improves clinical practiceSystem improves clinical practice Clinician is in controlClinician is in control The system is easy to useThe system is easy to use The decisions made are transparentThe decisions made are transparent
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Aspects for Evaluation of a Aspects for Evaluation of a CDSSCDSS
The clinician need that the CDSS is intended The clinician need that the CDSS is intended to addressto address
The process used to develop the systemThe process used to develop the system The system’s intrinsic structureThe system’s intrinsic structure Evidence of accuracy, generality and Evidence of accuracy, generality and
clinical effectivenessclinical effectiveness The impact of the resource on patients and The impact of the resource on patients and
other aspects of the health care other aspects of the health care environmentenvironment
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Methodology Major Use Key developments
Information Retrieval Finding information, answering questions
Taxonomies, ontologies, text-based methods, automatic invocation
Evaluation of logical conditions
Alerts, reminders, constraints, inference system
Decision tables, event-condition-action-rules, production rules
Probabilistic and data driven classification or prediction
Diagnosis, technology assessment, treatment selection, classification and prediction, prognosis estimation, evidence-based medicine
Bayes theorem, decision theory, ROC analysis, data mining, logistic regression, artificial neural networks, belief networks, meta-analysis.
Heuristic modeling and export systems
Diagnostic and therapeutic reasoning, capturing nuances of human expertise
Rule-based systems, frame-based reasoning
Calculations, algorithms and multistep processes
Execution of computational processes, flow-chart-based guideline and consultations, interactive dialogue control, biomedical image and signal processing
Process flow and workflow modeling, guideline formalisms and modeling languages
Associative groupings of elements
Structured data entry, structured reports, order sets, other specialized presentations and data views
Report generators and document construction tools, document architectures, templates, markup languages, ontology tools, ontology languages
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Computerized Computerized Physician/Provider Physician/Provider
Order EntryOrder Entry
The Two Sides of ErrorsThe Two Sides of Errors• 44,000+ hospital deaths due to
medical error• 50 adverse events/1000
outpatient pt-years (Gurwitz 2003)
• Patients receive 55% of recommended care (McGlynn, 2003)
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Our Solution to SafetyOur Solution to Safety
BMJ 2000;320:768–70
physician
nurse
pharmacist
Bedside team
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What is CPOE?What is CPOE?
Computer application Computer application which replaces which replaces traditional paper order traditional paper order sheetssheets
Care / computerized Care / computerized provider is a key part provider is a key part of the nameof the name
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Key Advantages to CPOEKey Advantages to CPOE
Data aggregated for clinical useData aggregated for clinical use Clinician can interact with medical Clinician can interact with medical
record away from the bedsiderecord away from the bedside Immediate routing of orders and Immediate routing of orders and
requisitions to ancillary departmentsrequisitions to ancillary departments Smart prompts and checks can Smart prompts and checks can
enhance safety and quality of careenhance safety and quality of care
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Important OE work
1. El Camino Hospital, 1971 First clinician order entry system, (TDS)
2. Warner, Pryor, Clayton, Gardner, et alHELP System, LDS Hospital, 1970++ (3M)
3. McDonald, Tierney et al, 1974++ Regenstrief order entry / reminders / (~SMS)
4. Glaser, Teich, Bates, Kuperman et al, 1994++ Brigham & Women’s order entry (~Eclipsys)
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Commercial Order Entry (80s)Commercial Order Entry (80s)
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CPOE IntegrationCPOE Integration
Decisionsupport
Pharmacy
ExternalKnowledge
Sources
Terminology LabSystem
ADT
Data ServerOr Interface
CPOE SystemCPOE System
EHR (documents)+
InternalKnowledge
Sources
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Copyright (C) 2003 Vanderbilt University Medical Center
WizOrder Main Screen Layout: Simple, fixed format: functionally oriented, designed with users
Physician enters order for antibiotic,Gentamicin, by partially typing its name
1) Active orders 2) Common usefulorders based onpatient location
3) What to do next in WizOrder
4) Buttons forcommonly usedfeatures
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TimeTimeSavings:Savings:
NewNewmethodmethod
forforsummarizingsummarizing
““active”active”orders &orders ¤tcurrent
information information
““What you What you need to know need to know
about patient” about patient” printed on one printed on one piece of paperpiece of paper
Active orders
RecentLabs
Copyright (C) 2003 Vanderbilt University Medical Center
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IssuesIssues
PeoplePeople
ProcessProcess
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Bayesian NetworkBayesian NetworkBayesian NetworkBayesian Network
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Review of ProbabilityReview of Probability
P(A) = p, P(not A) = 1 – pP(A) = p, P(not A) = 1 – p
P(A, B) = P (A | B)* P(B)P(A, B) = P (A | B)* P(B)
P(A, B) = P (A | B) * P(B) = P(A) * P(B)P(A, B) = P (A | B) * P(B) = P(A) * P(B)
P(A) = P(A) =
B
BA )|(
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ProbabilityProbability
FrequentistFrequentist BayesianBayesian
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Bayes’ TheoremBayes’ Theorem
)(
)()/()/(
eP
hPhePehP
Posterior
Prior
Probability of Evidence
Likelihood
Probability of an hypothesis, h, can be updated when evidence, e, has been obtained.
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A Simple ExampleA Simple ExampleConsider two related variables:1. Disease (D) with values y or n2. Test (T) with values +ve or –ve
And suppose we have the following probabilities:P(D = y) = 0.001P(T = +ve | D = y) = 0.8P(T = +ve | D = n) = 0.01
These probabilities are sufficient to define a joint probability distribution.
Suppose an athlete tests positive. What is the probability that he has the disease?
074.09990010001080
001080)()|()()|(
)()|(
....
..nDPnDveTPyDPyDveTP
yDPyDveTPve)y|TP(D
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Sensitivity, Specificity, Sensitivity, Specificity, Prevalence and ProbabilitiesPrevalence and Probabilities
Consider two related variables:1. Disease (D) with values y or n2. Test (T) with values +ve or –ve
And suppose we have the following probabilities:P(D = y) = 0.001 (Prevalence)P(T = +ve | D = y) = 0.8 (Sensitivity)P(T = +ve | D = n) = 0.01(1-specificity)
These probabilities are sufficient to define a joint probability distribution.
Suppose an athlete tests positive. What is the probability that he has taken the drug?
074.09990010001080
001080)()|()()|(
)()|(
....
..nDPnDveTPyDPyDveTP
yDPyDveTPve)y|TP(D
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Bayesian Network DemoBayesian Network Demo
Decision TreeDecision Tree