Transcript Document 707425

Modeling Recommendation Sets of
Clinical Guidelines
Samson Tu
Stanford Medical Informatics
Stanford University School of Medicine
HL7 Working Group Meeting
Memphis, TN
September 11, 2003
Outline
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Original work-item goal
Reformulated work item
Review of past work
SAGE implementation experience
Next steps?
Original work-item goal
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To develop standardized “flowchart” model for
human understanding and computer encoding
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Expressive process model
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allows sequencing, repetition, and concurrency
(branching and synchronization)
Integration of decision making and activity
specification
Visual clarity
Well-understood semantics
Background: problems
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What is a flowchart?
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Different usages: not all diagrams represent
processes
Workflow process model may not be
appropriate for all
Study of guideline diagramming
conventions
Tu, SW, Johnson, PD, Musen, MA (2002). A Typology for Modeling Processes in Clinical
Guidelines and Protocols, Stanford Medical Informatics Report SMI-2002-0911
Reformulated work item
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“Flowcharts” viewed as organized sets of
guideline recommendations
Recommendation consists of
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Two classes of “recommendation set”
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context: clinical setting, patient state, current
therapy, provider role, triggering event
decision: encapsulate decision-making knowledge
action: computer or clinical actions
activity graph
decision map
Common ground for various guideline models
Tu, SW, Campbell, J, Musen, MA, The Structure of Guideline Recommendations: A Synthesis,
AMIA 2003 Symposium
Activity graph
evaluate ACE
as cause
cough after
ACE = yes
unevaluated
Cough (AND
split)
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Used to specify processes
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cough
gone?
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cough
gone?
(AND
split)
presumed PNDS = yes
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cough gone
=no
Directed graph of
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treat presumed
PNDS
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abnormal
CXR?
(AND join)
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order chest
radiograph
computational and care
processes
branching and
synchronization (split/join)
context
decision step
action step
route: purely for branching
and synchronization
Adapted from Workflow
Management Coalition
process model
Decision map
continue
?BP_controlled.eq(“true”)
lifestyle
change
hypertensive
no medication
initiate
med
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Used to model
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If/then statements
Augmented transition network
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Decision tree
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substitute
med
hypertensive
w/ medication
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add
drug
An action is followed by a set of possible
context nodes
Collection of decision points
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increase
dose
Connected graph
One alternative allowed at decision point
context, decision, action nodes
differ from activity graph in the
underlying computational model
Review of past work
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Proposal of using WfMC process model as basis for
“flowchart” (2001-05 Minneapolis)
Visio stencil and template (2002-01 San Diego)
Review of literature (2002-05 Baltimore)
Mapping of guideline process to WfMC process and
then to Petri net (2002-05 Atlanta)
Reformulation of work item as model of
“recommendation set” (2002-10 Baltimore)
Partial reconciliation with HL7 RIM (2003-01 San
Diego)
Incorporation into SAGE guideline model (2003-04
San Antonio)
Recommendation set in use: SAGE
implementation experiment
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Selection of guideline
Development of usage scenarios
Specification of guideline logic, data
model, and terminology
Encoding of guideline in Protégé-2000
Simulation of implementation in IDX
clinical information system (CIS)
Selection of guideline and
development of usage scenarios
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Immunization guideline selected for first endto-end experiment
Four usage scenarios
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Neonatal orders for immunization immediately after
birth
Primary care immunization with standard care
protocol in place
Primary care immunization, physician confirmation
Population-based reminders to patients or providers
Specification of guideline logic
guideline documents
clinical scenarios
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IF NO CONTRAINDICATION TO HEP B AND
NO REASON FOR DEFERRAL AND
NUMBER OF HEP B VACCINE DOSES = 3 AND
DOSE GIVEN WITHIN 7 DAYS OF BIRTH AND
3RD DOSE GIVEN BEFORE 6 MONTHS AGE AND
TIME FROM LAST DOSE IS >= 8 WEEKS AND
AGE < 19 YEARS
THEN
ADVISE MONOVALENT HEP B VACCINE IM DUE
Interpretation and operationization of guideline
recommendations
Formulation of guideline logic sensitive to
scenarios
Specification of data model
vMR classes
Concepts in guideline logic
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Anaphylactic reaction to
influenza vaccine
is a
Allergy where
code is vaccines allergy
allergen is influenza vaccine
reaction is anaphylaxis
A data model constrains how data is viewed
and used in computable guideline
Detailed data models correspond to constraints
on vMR classes
Specification of guideline
terminology
‘anaphylaxis’ = SNOMED CT:39579001
contaminated wound lesion =
reference terminology
‘wound lesion’ with
associated morphology
‘contaminated laceration’
concepts in guideline logic chronic pulmonary disease =
‘Chronic respiratory disease’ AND
‘Disease of lower respiratory system’
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Make reference to standard terminologies
whenever possible
Need to define new terms
Encoding of guideline in Protégé2000: top-level processes
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Top-level activity
graphs model DSS
interactions with
providers through
clinical information
system
Highly dependent
on workflow
Immunization guideline logic as
decision map
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A decision involve
choice of assertion to
make about an
immunization
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Due and can be given
Deferred
Contraindicated or not
due
Assertion about
immunizations tested
at top-level activity
graph
Lessons from SAGE experiment
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Top-level processes model guideline DSS
reactions/interactions with CIS
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guideline DSS not in control of workflow
separation of workflow and medical knowledge
Decision map (coupled with a decision model)
provides good cognitive correspondence with
rule-like recommendations
Guideline encoding dependent on scenarios
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dependence on workflow
dependence on formulation of recommendations
Limitations of SAGE experiment
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Multi-level multi-choice recommendations
not tested
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Refinement of medication recommendations
Alternative classes of medications with
individual indications and contraindications
Complex medical processes not tested
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Concurrent processes that require branching
and synchronization
Multi-encounter processes
Next step?
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Need for RIM harmonization?
Specification for balloting?
Date for closure?