Automated Reasoning for Application of Clinical Guidelines BMIR Research-in-Progress Presentation May 26, 2011 Csongor Nyulas, Research Software Engineer

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




ATHENA CDS agents




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




ATHENA CDS agents




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




ATHENA CDS agents




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




ATHENA CDS agents




Presentation for review

Single Guideline CDS Recommendation

Consolidating Recommendations from Multiple Guidelines




ATHENA CDS agents



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.

Documents

Automated Reasoning for Application of Clinical Guidelines BMIR Research-in-Progress Presentation May 26, 2011 Csongor Nyulas, Research Software Engineer