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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

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Adaptive Health Care Information for Consumers

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  1. Group: CSH Partners Adaptive Health Care Information for Consumers

  2. 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

  3. Index • Motivation for the chosen topic • Related Research • Main Findings • Adaptive Information for Consumers • Demonstration • Further Research • Conclusions • Questions • References

  4. 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.

  5. Related Research • Adaptive user interfaces for health care applications, IBM [5] • Techniques of Adaptive Hypermedia [6] • Providing personalized accurate healthcare information [7]

  6. 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

  7. 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.

  8. 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

  9. How to Capture the Information • Existing patient records. • Standardized questionnaires: Personality, Stage of change, anxiety level • Psychological sensors: Emotional state and stress, Motivation levels.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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)

  15. Issues with Health Care Information • Privacy, security and trust. • Patient’s emotional state and attitude • Updating the user model

  16. 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.

  17. 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

  18. 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

  19. 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

  20. Questions & Comments

  21. References • 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 • 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) • Grol, R.: Personal paper: Beliefs and evidence in changing clinical practice. British Medical Journal 315 (1997) 418–421 • 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 • KrishRamachandran, Adaptive user interfaces for health care applications, IBM, http://www.ibm.com/developerworks/web/library/wa-uihealth/ (2009) • Peter Brusilovsky, Methods and techniques of adaptive hypermedia, User Modeling and User Adapted Interaction, 1996, v 6, n 2-3, pp 87-129 • 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

  22. References • 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 • 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 • 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 • 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 • McKeown, K.: Discourse strategies for generating natural-language text. Artificial Intelligence 27(1) (1985) 1–42 • Reiter, E., Dale, R.: Building applied natural-language generation systems. Journal of Natural-Language Engineering 3 (1997) 57–87 • 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

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