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Electronic Consumer Health Information: Where Has It Been? Where Is It Going?. Jacquelyn Burkell Grant Campbell Faculty of Information and Media Studies University of Western Ontario OLA Super Conference 2004. Outline of Presentation:. Consumer Health Decisions Screening Tests - PowerPoint PPT Presentation
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Electronic Consumer Health Information:
Where Has It Been?Where Is It Going?
Jacquelyn BurkellGrant Campbell
Faculty of Information and Media StudiesUniversity of Western OntarioOLA Super Conference 2004
Outline of Presentation: Consumer Health Decisions
Screening Tests The Sensitivity / Specificity Paradox Allowing for the Decision
Electronic Consumer Health Information Previous and Current Consumer Health
Information The Use of Metadata to Retrieve Documents The Use of Metadata to Retrieve Information Where We’re Headed
SensitivityThe percentage of real cases that test positive
Sensitivity = True PositivesTrue Positives + False
Negatives
SpecificityThe percentage of negative cases that test
negative
Specificity = True NegativesTrue Negatives + False
Positives
Base Rate Incidence of the condition in the
population being tested.
Example: The Maternal Serum Screening Test for Down’s Syndrome
Base rate: 1%
Sensitivity of test: 90%
Specificity of test: 60%
ConditionPresent
ConditionAbsent
Positive True Pos. False Pos.
Negative False Neg. True Neg.
1,000cases
Presence or Absence of Condition
TestResult
ConditionPresent
ConditionAbsent
Positive True Pos. False Pos.
Negative False Neg. True Neg.
10 990 1,000cases
Presence or Absence of Condition
TestResult
Base Rate = 1%Incidence = 10 / 1000
ConditionPresent
ConditionAbsent
Positive True Pos.9
False Pos.
Negative False Neg.1
True Neg.
10 990 1,000cases
Presence or Absence of Condition
TestResult
Sensitivity = 90 %
ConditionPresent
ConditionAbsent
Positive True Pos.9
False Pos.396
Negative False Neg.1
True Neg.594
10 990 1,000cases
Presence or Absence of Condition
TestResult
Specificity = 60%
ConditionPresent
ConditionAbsent
Positive True Pos.9
False Pos.396
405
Negative False Neg.1
True Neg.594
595
10 990 1,000cases
Presence or Absence of Condition
TestResult
In any given 1,000 tests:595 are likely to test negative, of which 1 will be a false negative.405 are likely to test positive, of which 396 will be false positives.
Predictive Values:
Positive Predictive Value:
Negative Predictive Value:
9/405 = 2.2 %
594/595 = 99.8 %
Positive Predictive Value: 0.25%
Age Down Syndrome Risk
30 1/885
35 1/465
40 1/100
45 1/32
Positive Predictive Value: 0.48%
Age Down Syndrome Risk
30 1/885
35 1/465
40 1/100
45 1/32
Positive Predictive Value: 2.2%
Age Down Syndrome Risk
30 1/885
35 1/465
40 1/100
45 1/32
Positive Predictive Value: 6.7%
Age Down Syndrome Risk
30 1/885
35 1/465
40 1/100
45 1/32
The State of Things In the Past The “Vertical File”
Collections of articles, pamphlets and other ephemeral information sources, generally written for the lay person, containing practical advice on a variety of consumer health issues.
The State of Things In the Present The “Web Resource Guide”
Subject-oriented electronic pathfinders, providing organized access to Web resources on consumer health issues
Example
Improvements: Stage One Database Searching Assistance
The design of preformulated queries of medical databases based on anticipated decision-making needs
Example
Improvements: Stage Two Metadata to Retrieve Web Documents
The use of a standard metadata element set to facilitate the retrieval of Web-based objects
The Dublin Core Expansions
Improvements: Stage Three Metadata to Retrieve Data Elements Within
Documents
The use of metadata elements to mark parts of documents for subsequent retrieval and assembly into new documents
Automatic indexing or Manual indexing Controlled vocabulary searching (MeSH)
MeSH:Diagnostic Imaging--Radiography --Mammography
MeSH:Investigative Techniques--Epidemiological Methods --Statistics --Sensitivity and Specificity
MeSH:Psychological Phenomena --Mental Processes --Thinking --Decision Making
What do we need to watch for in this brave new world? Where can the information that people
need be found?
Are the knowledge structures that we use sufficiently flexible for consumer health?
Is the information being presented in a way that promotes comprehension, and minimizes the risk of misinformation?
Please contact us for further information!Jacquelyn Burkell ([email protected])Grant Campbell ([email protected])
Faculty of Information and Media StudiesUniversity of Western OntarioLondon, OntarioN6A 5B7