1 / 52

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. Health decision aids Clinical example

cachez
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Outline • Health decision aids • Clinical example • e-Preference approach • Prototype system and evaluation

  3. 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

  4. Health Decision Aids • Focus typically on • Improvements in patient knowledge • Explanation of treatment alternatives • Communication of risk

  5. HDA Presentation • Non-interactive formats • Brochure (paper booklet or Web based) • Audiotape • Video • Interactive formats • Decision board • Computer • Multimedia

  6. Outline • Health decision aids • Clinical example • e-Preference approach • Prototype system and evaluation

  7. 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

  8. 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)

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

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

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

  12. Decision Analysis (Protheroe et al., 2000)

  13. Decision Analysis (Protheroe et al., 2000) 17 on treatment 28 on treatment

  14. Decision-Support Tool (Thomson et al., 2002)

  15. Decision-Support Tool (Thomson et al., 2002)

  16. 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

  17. 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

  18. Outline • Health decision aids • Clinical example • e-Preference approach • Prototype system and evaluation

  19. 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

  20. EON Architecture End-User Application Problem-Solving Method Query Engine Patient Database Protocol KB Protégé

  21. Design of e-Preference • A set of software methods for • Knowledge representation • Decision-analytic computation • Data access from existing database • Web-based multimedia presentation

  22. e-Preference Architecture HDA Query Engine FLAIR Netica Patient Database KBDM Protégé

  23. Knowledge-Based Decision Model • Encode concepts related to • Influence diagrams • Health decisions and outcomes • Risk factors • Patient preferences • Relationships between these factors

  24. Netica

  25. FLAIR

  26. 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 …

  27. The NCI Thesaurus

  28. Structuring Knowledge

  29. 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”)

  30. OWL Example Patient Influence Diagrams has_chance_node AF E. MyChart Nodes has_model has_diagnosis Diagnoses Decision Chance Outcome AF DM

  31. 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)

  32. Making Restrictions

  33. Generating a Decision Model

  34. Remaining Challenges • Modeling and editing probabilities in Protégé OWL • Generating interface based on modified influence diagram

  35. KBDM Approach • Advantages • Ability to modify knowledgebase and create tailored decision model for HDA • Disadvantages • Efforts needed for acquiring and maintaining knowledge

  36. Outline • Health decision aids • Clinical example • e-Preference approach • Prototype system and evaluation

More Related