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 &currentcurrent

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