User Modeling pp 107-118 | Cite as

Authoring and Generating Health-Education Documents That Are Tailored to the Needs of the Individual Patient

Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 383)


Health-education documents can be much more effective in achieving patient compliance if they are customized for individual readers. For this purpose, a medical record can be thought of as an extremely detailed user model of a reader of such a document. The HealthDoc project is developing methods for producing health-information and patient-education documents that are tailored to the individual personal and medical characteristics of the patients who receive them. Information from an on-line medical record or from a clinician will be used as the primary basis for deciding how best to fit the document to the patient. In this paper, we describe our research on three aspects of the project: the kinds of tailoring that are appropriate for health-education documents; the nature of a tailorable master document, and how it can be created; and the linguistic problems that arise when a tailored instance of the document is to be generated.


Authoring Tool Medical Writer Rhetorical Relation Cohesive Relationship Repair Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bateman, J.A. (1995). KPML: The KOMET-Penman multilingual linguistic resource development environment. Proceedings, 5th European Workshop on Natural Language Generation, Leiden, May 1995, 219–222.Google Scholar
  2. Buchanan, B., Moore, J.D., Forsythe, D.E., Carenini, G., Ohlsson, S., and Banks, G. (1995). An intelligent interactive system for delivering individualized information to patients. Artificial Intelligence in Medicine, 7:117–154.CrossRefGoogle Scholar
  3. Campbell, M.K., DeVellis, B.M., Strecher, V.J., Ammerman, A.S., DeVellis, R.F., and Sandler, R.S. (1994). Improving dietary behavior: The effectiveness of tailored messages in primary care settings. American Journal of Public Health, 84:783–787.CrossRefGoogle Scholar
  4. Cawsey, A., Binsted, K., and Jones, R. (1995). Personalized explanations for patient education. Proceedings, 5th European Workshop on Natural Language Generation, Leiden, May 1995, 59–74.Google Scholar
  5. DiMarco, C. and Banks, S. (1997). Using subsumption classification on a stylistic hierarchy in the multi-stage conversion of natural language text to sentence plans. In preparation.Google Scholar
  6. DiMarco, C. and Foster, M.E. (1997). The automated generation of Web documents that are tailored to the individual reader. Proceedings, AAAI Spring Symposium on Natural Language Processing on the World Wide Web, Stanford University, March 1997.Google Scholar
  7. DiMarco, C., Hirst, G., and Hovy, E. (1997). “Rewriting is easier than writing”: Generation by selection and repair in the HealthDoc project. In preparation.Google Scholar
  8. DiMarco, C., Hirst, G., Wanner, L., and Wilkinson, J. (1995). HealthDoc: Customizing patient information and health education by medical condition and personal characteristics. Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.Google Scholar
  9. Donohew, L., Palmgreen, P., and Lorch, E.P. (1994). Attention, need for sensation, and health communication campaigns. American Behavioral Scientist, 38:310–322.CrossRefGoogle Scholar
  10. Hale, J.L. and Dillard, J.P. (1995). Fear appeals in health promotion campaigns: Too much, too little, or just right? In Maibach and Parrott 1995, 65–80.Google Scholar
  11. Hovy, E.H. and Wanner, L. (1996). Managing sentence planning requirements. Proceedings, ECAI-96 Workshop ‘Gaps and Bridges’: New Directions in Planning and Natural Language Generation, Budapest, August 1996.Google Scholar
  12. Maibach, E. and Parrott, R.L. (1995). Designing health messages: Approaches from communication theory and public health practice. Thousand Oaks, CA: Sage Publications.Google Scholar
  13. Masi, R. (1993). Multicultural health: Principles and policies. In Masi, R., Mensah, L., and McLeod, K.A., eds., Health and cultures: Exploring the relationships. Volume I: Policies, professional practice and education. Oakville, Ontario: Mosaic Press, 11–22.Google Scholar
  14. Monahan, J.L. (1995). Thinking positively: Using positive affect when designing health messages. In Maibach and Parrott 1995, 81–98.Google Scholar
  15. Parsons, K. (1997). An Authoring Tool for Customizable Documents. M.Math. thesis, Department of Computer Science, University of Waterloo, forthcoming.Google Scholar
  16. Penman Natural Language Group (1989). The Penman primer, The Penman user guide, and The Penman reference manual. Information Sciences Institute, University of Southern California.Google Scholar
  17. Reiter, E. (1995). NLG vs. templates. Proceedings, 5th European Workshop on Natural Language Generation, Leiden, May 1995, 95–105.Google Scholar
  18. Skinner, C.S., Strecher, V.J., and Hospers, H. (1994). Physicians’ recommendations for mammography: Do tailored messages make a difference? American Journal of Public Health, 84:43–49.CrossRefGoogle Scholar
  19. Strecher, V.J., Kreuter, M., Den Boer, D.-J., Kobrin, S., Hospers, H.J., and Skinner C.S. (1994). The effects of computer-tailored smoking cessation messages in family practice settings. The Journal of Family Practice, 39:262–270.Google Scholar

Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TorontoCanada
  2. 2.Department of Computer ScienceUniversity of WaterlooCanada
  3. 3.Information Sciences InstituteUniversity of Southern CaliforniaUSA

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