Analyzing Medical Processes - UMass Amherst: Laboratory

26 Pages·2009·1.76 MB·English

by  Deborah Bergeron

Analyzing Medical
Processes
Bin Chen, George S. Avrunin, Lori A. Clarke, Leon J. Osterweil
Department of Computer Science, University of Massachusetts, Amherst
Elizabeth A. Henneman
School of Nursing, University of Massachusetts, Amherst
Philip L. Henneman
Tufts-Baystate Medical Center, Springfield, MA
Problem: Medical Errors
 According to a 1999 IOM study, medical errors are a
leading cause of death in the U.S.
• 5 years after the original study, no substantial reduction
 Resulted in many efforts to mandate and/or model critical
processes
• Guildelines
• In English, with imprecise and ambiguous terminology
• Usually incomplete
• E.g. no indication of how to handle unusual situations
• Modeling notations
• Data flow diagrams
• Also, UML, Petri Nets, finite-state machines
• Again, no indication on how to handle unusual situations
• Some of these notations lack semantics for specifying them
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Goal: Detecting and Reducing Medical Errors
 Hypothesis: Finite-state verification can be used
to help detect defects in medical processes that
are modeled using a semantically rich, rigorously
defined process-definition language
• And to verify that efforts to remove the defects have
(or have not) been successful
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Approach
 Create detailed models of medical processes
• using the Little-JIL process definition language
 Represent medical guidelines as property specifications
• using the Propel property specification system
 Use finite-state verification techniques to detect violations
of these properties by the process definitions
• Using FLAVERS and SPIN
 Working with medical professionals, modify the processes
and reapply finite-state verification
An approach to
Continuous Process Improvement
for complex, human-intensive processes
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Desiderata for a Process Definition Language
 Well-defined semantics
• Support analysis
• Support execution
 Rich set of constructs for supporting (e.g.)
• Hierarchy
• Abstraction
• Scoping
• Exception handling
• Concurrency (synchronous and asynchronous)
• Iteration and conditionals
• Resource utilization
• Human-desired flexibility (E.g., choice operators)
 Facilities for elaboration of details
 Sufficient clarity to facilitate validation of process
definitions by domain experts
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Little-JIL Process Definition Language
• Three major components
• artifact specification
• resource specification
• coordination specification
 Visual Representation
 Supported by an Environment with capabilities for
• Editing
• Execution
• Analysis
• Static (e.g. FSV, FTA)
• Dynamic (e.g. discrete event simulation)
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Coordination Diagram
Cardinality and artifact flow
Artifact flow via channels
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An Example Little-JIL Process
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An Example Property; Defined with Propel
• Example property: Once the nurse notifies the
blood bank to prepare the blood product,the
nurse will eventually pick up the blood product
Notify_Bloodbank Pick_Up_Blood
Notify_Bloodbank
About 60 such were properties derived from
requirements stated in standard nursing text.
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Overview of our Verification Approach
 Construct a finite model that represents all
sequences of events, relevant to the property to
be evaluated, that can arise during system’s
execution
 Use reasoning methods (e.g., model checking,
data flow analysis) to determine whether the
property holds for all executions
• Used FLAVERS and SPIN
• represent distinct modeling and reasoning
approaches
• Could build on our existing technology to
automatically generate the models needed by them
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