adaptive health care information for consumers
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Adaptive Health Care Information for
Consumers
Group: CSH Partners
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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
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Index• Motivation for the chosen topic• Related Research• Main Findings• Adaptive Information for Consumers• Demonstration• Further Research• Conclusions• Questions• References
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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.
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Related Research• Adaptive user interfaces for health care
applications, IBM [5]• Techniques of Adaptive Hypermedia [6]• Providing personalized accurate healthcare
information [7]
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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
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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.
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What needs to be captured (User Modelling)• Medical condition and the state of the
patient • Current treatments• Patient's current mental and emotional
state• Different types of personality
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How to Capture the Information• Existing patient records.• Standardized questionnaires: Personality,
Stage of change, anxiety level• Psychological sensors: Emotional state and
stress, Motivation levels.
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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.
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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.
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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.
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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
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Evaluation Techniques• Questionnaires: Rate & Compare systems• Monitoring usage of the system (if
permitted)• Randomized evaluation
• 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)
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Issues with Health Care Information• Privacy, security and trust.• Patient’s emotional state and attitude• Updating the user model
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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.
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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
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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
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Further Research• 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
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Questions & Comments
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References1) 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
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References8) 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 home-care: 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 Natural-Language 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