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The Facts Don’t Speak For Themselves:
AHRQ 2007 HS KaplanR LevitanB Rabin FastmanCUMC/NYPH
Getting the Story from Aggregate Data
AHRQ 2007
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Event Reports as Rumble Strips
• Both can help increase safety by revealing danger
• Neither is reliably quantitative
• Both may create some unwanted noise in the environment
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Counting: A Means to an End
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Steinbeck on Counting
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Focus of Today’s Presentation Discuss real-time database queries using built-in
tools:
• RAI: Risk Assessment Index
• “Single Click” Standard reports
• QBF: Query by Field
• Data Mining: Clustering, CBR, and HAWK –
Constraint - Access: Permissions and roles
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Access: Permissions and Roles• User-based
– Role within organization– Role within MERS system
• Overarching access rule set – Location– Service line/Department– Employee events/Patient complaints– Type: falls, meds, transfusion, equip, etc.– Read-only
• HIPAA
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Filter no-harm reports to improve signal-to-Filter no-harm reports to improve signal-to-noise ratio (SNR)noise ratio (SNR)
Risk Assessment Index
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Two Filters to Enhance SNR
ImpactImpact
Freq
uency
Freq
uency LowLow
HighHigh
MediumMedium
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“Single Click” Standard Reports
• Ad-hoc reporting in real-time
• MERS has a comprehensive list of reports
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“Single Click” Standard Reports
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Medication Events by Specific Type
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Query By Field (QBF): Exact field matches
QBF’s filtered results can be fed into any report, graph, or spreadsheet
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Generation of Graphicson data subset using QBF filter
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Medication Drill-Down: Categories of Ordering Errors
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Benchmark Against Total and Other Reporting Sites/Hospitals
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Real-Time, Formatted SpreadsheetsExample: Fall outcomes report, breakdown by unit, opened in Excel
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User-Customized Spreadsheets
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Mining Association Rules
Decision Trees
IDClustering
Statistical Clustering
Data Mining
CBR
Similarity Matching
Textual
Numeric
Semantic
Neural Networks
© 2007 by The Trustees of Columbia University in the City of New York.
CBR
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Why Cluster?• Clusters show us event reports that are similar
across predefined dimensions
• They may represent:– frequency of a type of event– event trends in time – potential prevention, etc
© 2007 by The Trustees of Columbia University in the City of New York.
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Case-Based Reasoning (CBR)
What is case-based reasoning?• Case-based reasoning is another methodology
for, among other things, identifying clusters of similar events in large databases
© 2007 by The Trustees of Columbia University in the City of New York.
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Clustering/CBR
Clustering divides large data sets into coherent subsets that can be studied more easily
• Given an event report, CBR will – go through all event reports in database
– compute similarity between them
– find all reports within a certain distance or similarity (defined by the user)
• These reports form a cluster
© 2007 by The Trustees of Columbia University in the City of New York.
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CBR and Similarity Matching• Using CBR, the computer system can establish
the closest matches to any target event
• It can cluster based on similarity
• It can also identify unique events
© 2007 by The Trustees of Columbia University in the City of New York.
CBR and Similarity Matching
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HAWK• MERS’ similarity function, HAWK,
uses a vector of pre-assigned weights that corresponds to the vector of variables in an event report record.
• HAWK provides information that can be used to evaluate trends
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CBR: Another Use
• CBR can be extended to provide solutions to problems based on past experiences in the database– e.g., a help desk
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The Facts Don’t Speak For Themselves:
“Knowledge resides in the user and not in the collection.”
C. West Churchman in The Design of Inquiring Systems