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 Samson Tu Susana Martins Csongor Nyulas 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 guidelines

7. Examples To manage uncontrolled blood pressure, non- dihydropyridines calcium channel blocker (NCCB) is a possible alternative if the patient had a recent myocardial infarction (MI) In the heart-failure guideline, MI is a contraindication for the use of NCCB in patient with low left ventricular ejection fraction (LVEF) A patient whose blood pressure is not controlled, who had a recent MI and who has low LVEF will have contradictory recommendations on the use of NCCB

8. Overview of Approach Adapt our previously developed BioSTORM agent architecture Task decomposition Problem-solving method Reuse our extensive experience with ATHENA CDS Clinical domains: Hypertension (HTN), diabetes mellitus (DM), heart failure (HF), hyperlipidemia (Lipid), chronic kidney disease (CKD) 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 Myocardial Infarction (MI) or Coronary Artery Disease (CAD) Anonymized data extraction specification Demographics, vital signs, problems, medications, adverse reactions, selected blood and urine test results All dates converted to time-since-birthday 2455 cases https://clinicalinformatics.stanford.edu/research/stride.html

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. Representation of EARS C- Family Algorithms Obtain Current Observation Binary Alarm Transform Data Forecast Compute Test Value Estimate Model Parameters Obtain Baseline Data Evaluate Test Value Compute Expectation Empirical Forecasting Partial Summation Mean, StDev Database Query (7 days) Database Query (single day) Aberrancy Detection (Temporal) Obtain Current Observation-1 Compute Test Value-1 Estimate Model Parameters-1 Obtain Baseline Data-1 Evaluate Test Value-1 baseline mean, SD current observation 7 days baseline data current date partial sum alarm value a. Task structureb. Algorithmic flow

18. GLINDA Agents and Algorithmic Flow

19. Example of Agent Configuration: Get-Data Agent Task Method

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

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

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 Goldstein MK, et al. Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers. J Am Med Inform Assoc2004 Sep-Oct;11(5):368-76.

24. SYNTHETIC PATIENT DATA ONLY; no PHI

25. 25 SYNTHETIC PATIENT DATA

26. ATHENA-HTN Implementation San Francisco VA Palo Alto VA Durham VAMC, North Carolina 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 Information displayed to providers for….

27. Electronic Medical Record System Patient Data Simplified ATHENA Architecture ATHENA Guideline Knowledge Bases Guideline Interpreter Treatment Recommendation SQL Server: Relational database Data Mediator Method input output

28. Configuration of ATHENA CDS Agent: Class Definitions ATHENA Method Apply Guideline Task 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 Cumulative number of recommendations

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 importance of goals? 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?

41. Funding Support Protégé : National Institutes of Health (NLM LM007885) BioSTORM: Centers for Disease Control and Prevention (RFA-PH-05-126) EON: National Institutes of Health (NLM LM05708) GLINDA: National Institutes of Health (NLM HHSN276201000027C)

42. Funding Support ATHENA-CDS ATHENA-CDS supported in part by: VA HSR&D IMV 04-062-2: VISN Collaborative for Improving Hypertension Management with ATHENA-HTN VA HSR&D CPI 99-275: Guidelines for Drug Therapy of Hypertension: Multi-site Implementation Project VA HSR&D CPG 97-006: Guidelines for Drug Therapy of Hypertension: Closing the Loop VA HSR&D RRP 09-119: ATHENA-HF: Integrating Computable Guidelines for Complex Co-Morbidities PAIRE at VA Palo Alto, Pilot Project for ATHENA-DM VA HSR&D SDR 98-004 and VA HSR&D IMA 04-372; PI: Denise Hynes. ATHENA-CKD knowledge base.