Automated Reasoning for Application of Clinical Guidelines

1. Automated Reasoning for Application of Clinical Guidelines BMIR Research-in-Progress Presentation May 26, 2011 Csongor Nyulas, Research Software Engineer Samson Tu, Senior Research Scientist

2. GLINDA: Guideline Interaction Detection Architecture • Funder: National Library of Medicine • Project Members • Mark Musen • Mary Goldstein • Susana Martins • Hyunggu Jung • Pamela Kum

3. Agenda • Background and goals • Method • Status and future work

4. Problem Statement • Populations are aging worldwide • Older adults tend to have multiple chronic conditions • 75 million in US have 2 or more concurrent chronic conditions [1] • Management of multiple comorbidities presents challenging problems • Multiple competing goals • Variability in priorities [2] • Different risk profiles [3] [1] Anand K. Parekh, Mary B. Barton, The Challenge of Multiple Comorbidity for the US Health Care System, JAMA. 2010;303(13):1303-1304. [2] Tinetti ME, McAvay GJ, Fried TR, Allore HG, Salmon JC, Foody JM, et al. Health outcome priorities among competing cardiovascular, fall injury, and medication-related symptom outcomes. J Am Geriatr Soc. 2008 Aug;56(8):1409-16. [3] Fraenkel L, Fried TR. Individualized Medical Decision Making: Necessary, Achievable, but Not Yet Attainable. Arch Intern Med. 2010 March 22, 2010;170(6):566-9.

5. Role of Clinical Practice Guidelines • Clinical practice guidelines define standard of care • Almost all clinical practice guidelines focus on the management of single diseases • Simultaneous application of multiple guidelines leads to suboptimal care [1] • Hypothetical 79-year-old woman with chronic obstructive pulmonary disease, type 2 diabetes, osteoporosis, hypertension, and osteoarthritis • If the relevant CPGs were followed, the hypothetical patient would be prescribed 12 medications and a complicated nonpharmacological regimen [1] Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005 Aug 10;294(6):716-24

6. Long-Term Research Goals • Develop a modular and extensible platform for exploring informatics and clinical issues • Integrate and reuse best-of-breed knowledge resources and applications • Enumerate the ways that guideline recommendations interact and develop a theory on how to accommodate the interactions • Create methods for detecting, repairing, prioritizing, and integrating treatment recommendations from multiple guideline

7. Examples

8. Method • Adapt BioSTORM agent architecture • Task decomposition • Problem-solving method • Reuse ATHENA CDS • Clinical domains: Hypertension, diabetes mellitus, heart failure, hyperlipidemia, chronic kidney disease • Develop ontology of guideline interactions • Develop new agents for detecting, repairing, prioritizing, and integrating treatment recommendations • Apply methods on anonymized patient cases from the Stanford STRIDE database

9. Outline of Method Section • STRIDE patient selection and preparation • BioSTORM agent architecture and its application to GLINDA • ATHENA CDS agents • Integrated view of CDS recommendations • Ontology of guideline interactions • New agents for detecting, repairing and integrating guideline recommendations

10. Outline of Method Section • STRIDE patient selection and preparation • BioSTORM agent architecture and its application to GLINDA • ATHENA CDS agents • Integrated view of CDS recommendations • Ontology of guideline interactions • New agents for detecting, repairing and integrating guideline recommendations

11. STRIDE Data Extraction • Stanford Translational Research Integrated Database Environment (STRIDE) • Structured clinical information on over 1.4 million pediatric and adult patients cared for at Stanford University Medical Center since 1995 • Inclusion criteria • Adults who have ICD 9 codes for 2 or more of HTN, HF, DM, Lipid disorder, CKD, acute MI or CAD • Anonymized data extraction specification • Demographics, vitals, problems, medications, adverse reactions, blood and urine test results • All dates converted to time-since-birthday • 2455 cases

12. Test Patients Selection

13. Data Preparation • Map DB terms to ATHENA KB terms • Process data (e.g., compute daily doses) Note: Compute “date” by assuming everyone’s birthday to be 1900-01-01

14. Outline of Method Section • STRIDE patient selection and preparation • BioSTORM agent architecture and its application to GLINDA • ATHENA CDS agents • Integrated view of CDS recommendations • Ontology of guideline interactions • New agents for detecting, repairing and integrating guideline recommendations

15. BioSTORM: A Test Bed for Configuring and Evaluating Biosurveillance Methods • Task-method decomposition of biosurveillance algorithms and evaluations • Ontology of task and methods • Instances of specific biosurveillance configuration • Agent-based architecture for configuration and implementation of tasks and methods Buckeridge DL, Okhmatovskaia A, Tu S, O'Connor M, Nyulas C, Musen MA. Understanding detection performance in public health surveillance: modeling aberrancy-detection algorithms. J Am Med Inform Assoc2008 Nov-Dec;15(6):760-9

16. Task-Method Decomposition • Tasks are defined by inputs and output. • Methods are specified by semantic properties • characterized as configuration parameters, input data, or computed results.

17. current date Obtain Baseline Data Database Query (7 days) Obtain Baseline Data Obtain Current Observation Aberrancy Detection (Temporal) Transform Data 7 days baseline data current observation Compute Expectation Estimate Model Parameters Empirical Forecasting Mean, StDev baseline mean, SD Estimate Model Parameters Compute Test Value Obtain Current Observation Database Query (single day) Forecast Compute Test Value partial sum Partial Summation Evaluate Test Value Evaluate Test Value Binary Alarm alarm value a. Task structure b. Algorithmic flow Representation of EARS C-Family Algorithms

18. GLINDA Agents and Data-Flow

19. Example of Agent Configuration: Get-Data Agent

20. Operation of Get-Data Agent Task-method ontology Configurator agent Get-data agent Data GLINDA agent configuration Blackboard agent Monitor agent Controller agent

21. System Architecture Task-method ontology ATHENA agents Configurator agent Get-data agent Consolidator agent Data Interaction agents GLINDA agent configuration ATHENA KBs Repair & prioritize agents Blackboard agent Select guideline agent Controller agent Monitor agent Prioritized Integrated Recommendations

22. Outline of Method Section • STRIDE patient selection and preparation • BioSTORM agent architecture and its application to GLINDA • ATHENA CDS agents • Integrated view of CDS recommendations • Ontology of guideline interactions • New agents for detecting, repairing and integrating guideline recommendations

23. What is ATHENA CDS? • Automated clinical decision support system (CDSS) • Knowledge-based system automating guidelines • Built with EON technology for guideline-based decision support, developed at Stanford Medical Informatics • Initially for patients with primary hypertension who meet eligibility criteria • Extended to patients with chronic pain, heart failure, diabetes mellitus, chronic kidney disease and hyperlipidemia • Patient specific information and recommendations at the point of care * Athena in Greek mythology is a symbol of good counsel, prudent restraint, and practical insight

24. SYNTHETIC PATIENT DATA ONLY; no PHI

25. SYNTHETIC PATIENT DATA

26. Simplified ATHENA Architecture Electronic Medical Record System Patient Data VISTA hierarchical Database in M Treatment Recommendation Guideline Interpreter ATHENA Guideline Knowledge Bases SQL Server: Relational database Data Mediator

27. ATHENA-HTN Implementation Information displayed to providers for…. VISN 1 sites: Bedford, MA Boston, MA Manchester, NH Providence, RI West Haven, CT Three-Site Study: 50+ Providers 5,000+ Patients Almost 10,000 clinic visits VISN 1 Study: 50+ Providers 7,000+ Patients 11,000+ clinic visits San Francisco VA Palo Alto VA Durham VAMC, North Carolina

28. Configuration of ATHENA CDS Agent: Class Definitions Apply Guideline Task “EON” Method Annotations on property types Properties of method

29. Configuration of ATHENA CDS Agent: Instances ATHENA CDS Method Configuration Apply HTN Guideline Task

30. Outline of Method Section • STRIDE patient selection and preparation • BioSTORM agent architecture and its application to GLINDA • ATHENA CDS agents • Integrated view of CDS recommendations • Ontology of guideline interactions • New agents for detecting, repairing and integrating guideline recommendations • Presentation for review

31. Single Guideline CDS Recommendation

32. Consolidating Recommendations from Multiple Guidelines

33. Outline of Method Section • STRIDE patient selection and preparation • BioSTORM agent architecture and its application to GLINDA • ATHENA CDS agents • Integrated view of CDS recommendations • Ontology of guideline interactions • New agents for interaction detection, repair and for integrating guideline recommendations

34. Ontology of Guideline Interactions: Approaches • Types of interactions • Goals • Recommended interventions • Guideline abstractions • Cumulative effects • Quantitative Approach • Decision analysis • Quality-Adjusted Life Years (QALY) • Qualitative Approach • Leverage existing guidelines • Focus on integration and prioritization of guideline recommendations

35. Structure of Recommendations

36. Taxonomy of Cross-Guideline Relationships Among Recommendations • For each intervention, given a patient’s condition • Consistently positive • Consistently negative • Collateral effect • Indicated in one guideline, no specific indication in second • Contradictory • Indicated in one guideline, relative contraindications in another • Contraindicated • Strong contraindication in one guideline • Mixed • Indications and relative contraindications in both guidelines

37. Detecting Interactions (defrule Contradictory-benefit-risk-detection-1 (object (is-a Advisory) (evaluated_interventions $? ?ev $?)) (object (is-a Evaluated_Intervention) (activity ?intervention) (evaluations $? ?g1-evaluation $? ?g2-evaluation $?)(OBJECT ?ev)) (object (is-a Intervention_Evaluation)(add ?g1-addEval)(OBJECT ?g1-evaluation)) (object (is-a Intervention_Evaluation)(add ?g2-addEval)(OBJECT ?g2-evaluation)) (object (is-a Add_Evaluation)(OBJECT ?g1-addEval) (compelling_indication $?ci)(relative_indication $?ri) (contraindication nil)(relative_contraindication nil)) (object (is-a Add_Evaluation)(OBJECT ?g2-addEval) (compelling_indication nil)(relative_indication nil) (contraindication nil)(relative_contraindication $?rc)) => (make-instance Contradiction-Interaction (indication-evaluations ?g1-addEval) (contraindication-evaluation ?g2-addEval)) )

38. Prioritizing Guidelines and Recommendations • Prioritizing guidelines • Manual selection • Silence guideline if guideline targets (e.g., BP) satisfied • Prioritizing recommendations • Ranking based on weighted average of indications/contraindications?? • Constrain the total number of recommended interventions?? • ???

39. Current Status

40. Future Work • Extend the ontology of guideline interactions e.g., • Timing of interventions • Dosing differences • Reasoning based on drug properties • Develop quantitative methods?? • Grant proposal?