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 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

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 unevaluated Cough (AND split) evaluate ACE as cause treat presumed PNDS order chest radiograph cough gone? abnormal CXR? (AND join) cough after ACE = yes presumed PNDS = yes (AND split) cough gone =no cough gone?

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 hypertensive no medication initiate med continue lifestyle change hypertensive w/ medication substitute med add drug increase dose ?BP_controlled.eq(“true”)

Review of past work Proposal of using WfMC process model as basis for “flowchart” ( Minneapolis) Visio stencil and template ( San Diego) Review of literature ( Baltimore) Mapping of guideline process to WfMC process and then to Petri net ( Atlanta) Reformulation of work item as model of “recommendation set” ( Baltimore) Partial reconciliation with HL7 RIM ( San Diego) Incorporation into SAGE guideline model ( 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 YEARS THEN 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 vaccine is a Allergy 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: contaminated 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?