Modeling Recommendation Sets of Clinical Guidelines

Samson TuStanford Medical Informatics

Stanford University School of Medicine

HL7 Working Group MeetingMemphis, TN

September 11, 2003

Outline Original work-item goal Reformulated work item Review of past work SAGE implementation experience Next steps?

Original work-item goal To develop standardized “flowchart”

model for human understanding and computer encoding Expressive process model

allows sequencing, repetition, and concurrency (branching and synchronization)

Integration of decision making and activity specification

Visual clarity Well-understood semantics

Background: problems What is a flowchart?

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 “Flowcharts” viewed as organized sets of

guideline recommendations Recommendation consists of

context: clinical setting, patient state, current therapy, provider role, triggering event

decision: encapsulate decision-making knowledge action: computer or clinical actions

Two classes of “recommendation set” 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 Used to specify

processes computational and care

processes branching and

synchronization (split/join) Directed graph of

context decision step action step route: purely for branching

and synchronization Adapted from Workflow

Management Coalition process model

unevaluatedCough (AND

split)

evaluate ACEas cause

treat presumedPNDS

order chestradiograph

coughgone?

abnormalCXR?

(AND join)

cough afterACE = yes

presumed PNDS = yes

(ANDsplit)

cough gone=no

coughgone?

Decision map Used to model

If/then statements Augmented transition network

Connected graph One alternative allowed at decision

point Decision tree

An action is followed by a set of possible context nodes

Collection of decision points context, decision, action nodes differ from activity graph in the

underlying computational model

hypertensiveno medication

initiatemed

continuelifestylechange

hypertensivew/ medication

substitutemed

adddrug

increasedose

?BP_controlled.eq(“true”)

Review of past work 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 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 Immunization guideline selected for first

end-to-end experiment Four usage scenarios

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

Interpretation and operationization of guideline recommendations

Formulation of guideline logic sensitive to scenarios

guideline documents 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 YEARSTHEN ADVISE MONOVALENT HEP B VACCINE IM DUE

clinical scenarios

Specification of data model

A data model constrains how data is viewed and used in computable guideline

Detailed data models correspond to constraints on vMR classes

vMR classes

Concepts in guideline logic

Anaphylactic reaction to influenza vaccineis aAllergy where code is vaccines allergy allergen is influenza vaccine reaction is anaphylaxis

Specification of guideline terminology

Make reference to standard terminologies whenever possible

Need to define new terms

concepts in guideline logic

reference terminology

‘anaphylaxis’ = SNOMED CT:39579001contaminated wound lesion = ‘wound lesion’ with associated morphology ‘contaminated laceration’chronic pulmonary disease = ‘Chronic respiratory disease’ AND ‘Disease of lower respiratory system’

Encoding of guideline in Protégé-2000: top-level processes

Top-level activity graphs model DSS interactions with providers through clinical information system

Highly dependent on workflow

Immunization guideline logic as decision map

A decision involve choice of assertion to make about an immunization

Due and can be given Deferred Contraindicated or

not due Assertion about

immunizations tested at top-level activity graph

Lessons from SAGE experiment Top-level processes model guideline DSS

reactions/interactions with CIS 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 dependence on workflow dependence on formulation of recommendations

Limitations of SAGE experiment Multi-level multi-choice

recommendations not tested Refinement of medication recommendations Alternative classes of medications with

individual indications and contraindications Complex medical processes not tested

Concurrent processes that require branching and synchronization

Multi-encounter processes

Next step? Need for RIM harmonization? Specification for balloting? Date for closure?