E preference a tool for incorporating patient preferences into health decision aids
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e -Preference: A Tool for Incorporating Patient Preferences into Health Decision Aids. Amar K. Das, MD, PhD Assistant Professor Departments of Medicine (Medical Informatics) and Psychiatry and Behavioral Sciences Stanford University. Outline. Health decision aids Clinical example

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E preference a tool for incorporating patient preferences into health decision aids

e-Preference: A Tool for Incorporating Patient Preferences into Health Decision Aids

Amar K. Das, MD, PhD

Assistant Professor

Departments of Medicine (Medical Informatics) and

Psychiatry and Behavioral Sciences

Stanford University


Outline
Outline

  • Health decision aids

  • Clinical example

  • e-Preference approach

  • Prototype system and evaluation


Health decisions in aging
Health Decisions in Aging

  • Older individuals often face complex health decisions involving significant risk of morbidity and/or mortality

  • Patient participation is desirable in such decisions

  • Clinicians’ ability to facilitate shared decision making varies


Health decision aids
Health Decision Aids

  • Focus typically on

    • Improvements in patient knowledge

    • Explanation of treatment alternatives

    • Communication of risk


Hda presentation
HDA Presentation

  • Non-interactive formats

    • Brochure (paper booklet or Web based)

    • Audiotape

    • Video

  • Interactive formats

    • Decision board

    • Computer

    • Multimedia


Outline1
Outline

  • Health decision aids

  • Clinical example

  • e-Preference approach

  • Prototype system and evaluation


Atrial fibrillation
Atrial Fibrillation

  • Atrial fibrillation leads to a significant risk of stroke, ranging from 1% to 15% per year, based on patient factors

  • Anticoagulation therapy (warfarin) can reduce the risk of stroke by approximately two thirds, but incurs a risk of major bleeding complications of 1% to 3% per year


Measuring preferences
Measuring Preferences

  • Eight studies that modeled treatment preferences of patients with atrial fibrillation

  • Studies used three methods

    • Probability tradeoff technique

    • Decision aid

    • Decision analysis

(Man-Son-Hing et al., 2005)


Audiobooklet
Audiobooklet

(Man-Son-Hing et al., 2000)


Audiobooklet1
Audiobooklet

(Man-Son-Hing et al., 2000)


Audiobooklet2
Audiobooklet

(Man-Son-Hing et al., 2000)


Decision analysis
Decision Analysis

(Protheroe et al., 2000)


Decision analysis1
Decision Analysis

(Protheroe et al., 2000)

17 on treatment

28 on treatment


Decision support tool
Decision-Support Tool

(Thomson et al., 2002)


Decision support tool1
Decision-Support Tool

(Thomson et al., 2002)


Hda limitations
HDA Limitations

  • Typically designed for one type of health decision

  • May not provide patient-specific information on alternatives and risks

  • May be only accessible in particular settings

  • Does not have readily modifiable design


Design desiderata for hdas
Design Desiderata for HDAs

  • We need a design that can

    • Be tailored to specific health problems

    • Incorporate patient-specific data

    • Be accessible via the Internet

    • Be easily modified


Outline2
Outline

  • Health decision aids

  • Clinical example

  • e-Preference approach

  • Prototype system and evaluation


Motivation for e preference
Motivation for e-Preference

  • Create an environment for clinical experts and software developers to design and implement HDAs

  • Based on our research group’s long standing interest in developing customizable and reusable software architectures for decision support


Eon architecture
EON Architecture

End-User

Application

Problem-Solving

Method

Query

Engine

Patient

Database

Protocol KB

Protégé


Design of e preference
Design of e-Preference

  • A set of software methods for

    • Knowledge representation

    • Decision-analytic computation

    • Data access from existing database

    • Web-based multimedia presentation


E preference architecture
e-Preference Architecture

HDA

Query

Engine

FLAIR

Netica

Patient

Database

KBDM

Protégé


Knowledge based decision model
Knowledge-Based Decision Model

  • Encode concepts related to

    • Influence diagrams

    • Health decisions and outcomes

    • Risk factors

    • Patient preferences

    • Relationships between these factors




Aristotle s categories
Aristotle’s Categories

Supreme genus:SUBSTANCE

Differentiae: material immaterial

Subordinate genera:BODYSPIRIT

Differentiae: animate inanimate

Subordinate genera:LIVINGMINERAL

Differentiae: sensitive insensitive

Proximate genera:ANIMALPLANT

Differentiae: rational irrational

Species:HUMANBEAST

Individuals:Socrates Plato Aristotle …




Web ontology language
Web Ontology Language

  • A Semantic Web standard to use ontologies to represent knowledge on the Internet

  • OWL can be used to build ontologies of high-level descriptions, based on three concepts:

    • Classes (e.g., Influence Diagram, Nodes, Patient)

    • Properties (e.g., has_node, has_disease)

    • Individuals (e.g., “atrial fibrilaton”)


Owl example
OWL Example

Patient

Influence

Diagrams

has_chance_node

AF

E. MyChart

Nodes

has_model

has_diagnosis

Diagnoses

Decision

Chance

Outcome

AF

DM


Semantic web rule language
Semantic Web Rule Language

  • A language for expressing logical rules in terms of OWL concepts

  • Rules in SWRL can be used to deduce new knowledge about an existing OWL ontology

Patient(?pt) ^ has_dx(?pt, ?dx) ^ has_model(dx, ?hda) 

activate_HDA(?pt, ?hda)




Remaining challenges
Remaining Challenges

  • Modeling and editing probabilities in Protégé OWL

  • Generating interface based on modified influence diagram


Kbdm approach
KBDM Approach

  • Advantages

    • Ability to modify knowledgebase and create tailored decision model for HDA

  • Disadvantages

    • Efforts needed for acquiring and maintaining knowledge


Outline3
Outline

  • Health decision aids

  • Clinical example

  • e-Preference approach

  • Prototype system and evaluation


Conclusions
Conclusions

  • HDAs can help to incorporate patient preferences into shared decision making

  • The knowledge used in developing HDAs using decision analyses can be encoded

  • Such knowledge can be used to generate and tailor HDAs


Acknowledgments
Acknowledgments

Stanford Medical Informatics

Bilal Ahmed Daniel Rubin

Yael Garten Ravi Shankar

Jeremy Robin Samson Tu

Center for Primary Care and Outcomes Research

Mary Goldstein Tamara Sims

Doug Owens

NIA and CADMA for funding support


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