Electronic Consumer Health Information: Where Has It Been? Where Is It Going?

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 (jburkell@uwo.ca)Grant Campbell (gcampbel@uwo.ca)

Faculty of Information and Media StudiesUniversity of Western OntarioLondon, OntarioN6A 5B7