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Modeling Recommendation Sets of Clinical Guidelines

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

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Modeling Recommendation Sets of Clinical Guidelines

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  1. 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

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

  3. 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

  4. 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

  5. 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

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

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

  8. 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)

  9. 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)

  10. 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

  11. 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

  12. 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 Anaphylactic reaction to influenza vaccine is a Allergy where code is vaccines allergy allergen is influenza vaccine reaction is anaphylaxis vMR classes Concepts in guideline logic

  13. Specification of guideline terminology • Make reference to standard terminologies whenever possible • Need to define new terms ‘anaphylaxis’ = SNOMED CT:39579001 contaminated wound lesion = ‘wound lesion’ with associated morphology ‘contaminated laceration’ chronic pulmonary disease = ‘Chronic respiratory disease’ AND ‘Disease of lower respiratory system’ reference terminology concepts in guideline logic

  14. 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

  15. 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

  16. 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

  17. 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

  18. Next step? • Need for RIM harmonization? • Specification for balloting? • Date for closure?

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