Transcript Adaptive Health Care Information for Consumers

Group: CSH Partners
Adaptive Health Care
Information for Consumers
Group Members
• Butt, Salman
• MOT Model: Domain/Goal Maps
• LAG Strategies
• Researching adaptation in healthcare
• Fernando, Charith
• MOT Model: Domain/Goal Maps
• LAG Strategies
• Researching adaptation in healthcare
• Yang, Hui
• Researching Healthcare information
• Domain Model
Index
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Motivation for the chosen topic
Related Research
Main Findings
Adaptive Information for Consumers
Demonstration
Further Research
Conclusions
Questions
References
Motivation
• Internet has provided new opportunities for new
generation of users, “the health information
consumers”.
• Can bring real benefits and have a big impact on the
lives of consumers
• Growing need for adaptive healthcare information.
Related Research
• Adaptive user interfaces for health care applications,
IBM [5]
• Techniques of Adaptive Hypermedia [6]
• Providing personalized accurate healthcare
information [7]
Main Findings
• Generic health information has a less impact than
health information tailored to the individual
• An increasing number of people are now using the
Internet to support their healthcare.
• The amount of information available on the web
continues to grow.
• Web based interventions to provide knowledge can
have more impact than non web-based interventions
Health Education Goals
• To Inform, to enable decision making or to persuade
• Adapt to the needs of the patients both emotional
and informational and adjust content accordingly.
• To make sure the patients follow prescribed medical
plan.
• Educate patients on medications and its side effects.
What needs to be captured
(User Modelling)
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Medical condition and the state of the patient
Current treatments
Patient's current mental and emotional state
Different types of personality
How to Capture the Information
• Existing patient records.
• Standardized questionnaires: Personality, Stage of
change, anxiety level
• Psychological sensors: Emotional state and stress,
Motivation levels.
User Modelling
• State of change model - The model assumes that
people progress through very distinct stages of
change on their way to improve health
• Pre-contemplation - people see no problem with their
behaviour and don’t intend to change.
• Contemplation – people understand the problem and its
causes and start to take action.
• Preparation – planning to take action and putting together
a plan.
• Action – in process of making changes
• Maintenance – health behaviour continues on regular basis.
• Termination – no problem or threat presented.
Techniques for Adaptation
• Page Variant Approach – Different versions of
each page.
• Versions have to be written in advance
• At runtime most appropriate page will be displayed.
• Fragment-Variant approach – Constructed by
combining appropriate set of fragments.
• Fragments refers to a self contained information
element eg. Text paragraph or picture
• Page Constructed by selecting and combining an
appropriate set of fragments.
Techniques for Adaptation Cont...
• Natural Language Generation(NLG)
• Natural Language Generation (NLG) is the natural
language processing task of generating natural
language from a machine representation system such
as a knowledge base or a logical form.
• It involves:
• Content planning; deciding what content is most
relevant to the current user.
• Content Presentation; deciding how to most effectively
adapt the presentation of the selected content to the
user.
Evaluation
• Usability of the overall system
• Evaluate whether the content presented according
to the system goals
• Anxiety levels
• Level of Compliance
• State of Health
• Validity of the content presented
• How privacy is maintained when it comes to patient
data
Evaluation Techniques
• Questionnaires: Rate & Compare systems
• Monitoring usage of the system (if permitted)
• Randomized evaluation
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Patients are randomly assigned and results monitored
Cannot always draw conclusions
Costly approach
Usually used to decide benefits of various treatments
and monitor behavioral towards different systems
(web vs. non-web or tailored vs. generic data)
Issues with Health Care
Information
• Privacy, security and trust.
• Patient’s emotional state and attitude
• Updating the user model
Demonstration of our system
• Adaptive Healthcare Information system adapted in
two main strategies
• Monitor user behaviour to identify the medical
condition of the user/patient
• Enable the user to adapt the system to its medical
condition and the state.
Adaptive Behavior
• Show articles on different health conditions
• Capture the user’s condition by this
• Show educational articles and medication details according
to the user’s identified condition
• Let the user configure the system on medical condition and
the state
• Show medical condition related articles and the
educational material according to the user’s preferences
• Show medications according to the state of the health
condition
Conclusions
• We learned that
• There is a growing interest in health care applications
• The system not only educate patients but also assists
health professionals
• Promotes better communication between both health
professionals and between patients and their health
care team
• Provides diagnostic tools and assists in health care
provision
Further Research
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How to capture patients emotional states
Measuring anxiety levels
Detecting the current mental state of the patient
Better communication between patients and health
care professionals
Questions & Comments
References
1) Buchanan, B., Carenini, G., Mittal, V., Moore, J.: Designing computer-based frameworks that
facilitate doctor-patient collaboration. Artificial Intelligence in Medicine 12 (1995) 171–193
2) Gena, C., Weibelzahl, S.: Usability engineering for the adaptive web. In Brusilovsky, P.,Kobsa,
A., Niejdl, W., eds.: The Adaptive Web: Methods and Strategies of Web Personalization.
Volume 4321 of Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg
New York (2007)
3) Grol, R.: Personal paper: Beliefs and evidence in changing clinical practice. British Medical
Journal 315 (1997) 418–421
4) Mittal, V., Carenini, G., Moore, J.: Generating patient specific explanation in migraine. In:
Proceedings of the 18th Annual Symposium on Computer Applications in Medical Care,
Washington DC, McGraw-Hill Inc. (1994) 5–9
5) Krish Ramachandran, Adaptive user interfaces for health care applications, IBM,
http://www.ibm.com/developerworks/web/library/wa-uihealth/ (2009)
6) Peter Brusilovsky, Methods and techniques of adaptive hypermedia, User Modeling and User
Adapted Interaction, 1996, v 6, n 2-3, pp 87-129
7) Kees van Hee, Helen Schonenberg, Alexander Serebrenik, Natalia Sidorova and Jan Martijn
van derWerf Adaptive Workflows for Healthcare Information Systems BPM 2007
Workshops, LNCS 4928, pp. 359–370
References
8) Cawsey, A., Jones, R., Pearson, J.: The evaluation of a personalised health information system
for patients with cancer. User Modeling and User-Adapted Interaction 10(1) (2001) 47–72
9) Bellazzi, R., Montani, S., Riva, A., Stefanelli, M.: Web-based telemedicine systems for homecare: technical issues and experiences. Computer Methods and Programs in Biomedicine 64
(2001) 175–187
10) Hirst, G., DiMarco, C., Hovy, E., Parsons, K.: Authoring and generating health-education
documents that are tailored to the needs of the individual patient. In Jameson, A., Paris, C.,
Tasso, C., eds.: Proceedings of the Sixth International Conference on User Modeling
(UM’97), Sardinia, Springer Wien New York (1997) 107–119
11) McKeown, K.: The TEXT system for natural language generation: An overview. In:
Proceedings of the 20th Annual Meeting of the ACL (ACL’82). (1982) 113–120
12) McKeown, K.: Discourse strategies for generating natural-language text. Artificial Intelligence
27(1) (1985) 1–42
13) Reiter, E., Dale, R.: Building applied natural-language generation systems. Journal of NaturalLanguage Engineering 3 (1997) 57–87
14) Reiter, E., Osman, L.: Tailored patient information: some issues and questions. In:
roceedings of the ACL-1997 Workshop on From Research to Commercial Applications:
Making NLP Technology Work in Practice. (1997) 29–34