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Automated Reasoning for Application of Clinical Guidelines BMIR Research-in-Progress PresentationMay 26, 2011Csongor Nyulas, Research Software EngineerSamson Tu, Senior Research Scientist
GLINDA: Guideline Interaction Detection Architecture
Funder: National Library of Medicine
Project MembersMark Musen
Mary Goldstein
Samson Tu
Susana Martins
Csongor Nyulas
Hyunggu Jung
Pamela Kum
Agenda
Background and goals
Method
Status and future work
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.
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
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
Overview of ApproachAdapt 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
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
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
STRIDE Data ExtractionStanford 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 criteriaAdults 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 specificationDemographics, 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
Test Patients Selection
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
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
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
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.
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 structure b. Algorithmic flow
GLINDA Agents and Algorithmic Flow
Example of Agent Configuration: Get-Data Agent
Task
Method
Operation of Get-Data Agent
Blackboard agent
Controller agent
Task-method ontology
GLINDA agentconfiguration
Configurator agent
Monitor agent
Get-data agentData
Blackboard agent
Controller agent
Task-method ontology
GLINDA agentconfiguration
Configurator agent
Monitor agent
Get-data agentData
System Architecture
ATHENA agents
ATHENAKBs
Select guideline agent
Consolidator agent
Prioritized IntegratedRecommendations
Interaction agentsRepair & prioritize
agents
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
What is ATHENA CDS?
Automated clinical decision support system (CDSS)
Knowledge-based system automating guidelinesBuilt with EON technology for guideline-based decision support, developed at Stanford Medical Informatics
Initially for patients with primary hypertension who meet eligibility criteriaExtended 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.
SYNTHETIC PATIENT DATA ONLY; no PHI
25
SYNTHETIC PATIENT DATA
ATHENA-HTN Implementation
San Francisco VA
Palo Alto VADurham VAMC, North Carolina
VISN 1 sites:Bedford, MABoston, MAManchester, NHProvidence, RIWest Haven, CT
Three-Site Study: 50+ Providers5,000+ PatientsAlmost 10,000 clinic visits
VISN 1 Study:50+ Providers7,000+ Patients11,000+ clinic visits
Information displayed to providers for….
Electronic Medical RecordSystem Patient Data
Simplified ATHENA Architecture
ATHENA Guideline
Knowledge Bases
GuidelineInterpreter
Treatment Recommendation
SQL Server: Relational database
Data Mediator
Method
input
output
Configuration of ATHENA CDS Agent: Class Definitions
ATHENA Method
Apply Guideline Task
Annotations on property types
Properties of method
Configuration of ATHENA CDS Agent: Instances
ATHENA CDS Method ConfigurationApply HTN Guideline
Task
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
Single Guideline CDS Recommendation
Consolidating Recommendations from Multiple Guidelines
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
Ontology of Guideline Interactions: Approaches
Types of interactionsGoals
Recommended interventions
Guideline abstractions
Cumulative effects
Quantitative ApproachDecision analysis
Quality-Adjusted Life Years (QALY)
Qualitative ApproachLeverage existing guidelines
Focus on integration and prioritization of guideline recommendations
Structure of Recommendations
Taxonomy of Cross-Guideline Relationships Among Recommendations
For each intervention, given a patient’s conditionConsistently positive
Consistently negative
Collateral effectIndicated in one guideline, no specific indication in second
ContradictoryIndicated in one guideline, relative contraindications in another
ContraindicatedStrong contraindication in one guideline
MixedIndications and relative contraindications in both guidelines
Cumulative number of recommendations
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)))
Prioritizing Guidelines and Recommendations
Prioritizing guidelinesManual selection
Silence guideline if guideline targets (e.g., BP) satisfied
Prioritizing recommendationsRanking based on importance of goals?
Ranking based on weighted average of indications/contraindications??
Constrain the total number of recommended interventions??
???
Current Status
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?
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)
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.