Literature DB >> 7920367

Interactive individualization: patient counselling and drug information supported by knowledge systems.

J A Swart1, R Vos, T F Tromp.   

Abstract

A model for computer-supported patient counselling and drug information in community pharmacies is described. Two types of informational need are distinguished: the subjective informational need, i.e. the informational need perceived by the patient himself, and the normative informational need, i.e. the patient's informational need according to the professional. Accordingly, individualization is defined as the fine-tuning of information to the informational needs of the participants in the process of communication. A computer-supported process of communication based on this model is defined as 'interactive individualization'. The task of this knowledge system is to support the interactive encounter of the professional and the patient. The process of providing information consists of two subtasks: determining the items of information, e.g. pregnancy, dosage, etc., and subsequently determining the content of information or the advice itself. Based on these subtasks several functionalities of the proposed knowledge system can be derived.

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Mesh:

Year:  1994        PMID: 7920367     DOI: 10.1007/bf01877486

Source DB:  PubMed          Journal:  Pharm World Sci        ISSN: 0928-1231


  9 in total

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Journal:  Am J Public Health       Date:  1979-01       Impact factor: 9.308

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Journal:  J Am Pharm Assoc       Date:  1973-08       Impact factor: 2.217

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Journal:  Med Care       Date:  1982-06       Impact factor: 2.983

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Authors:  M W Johnson; W E Mitch; J Sherwood; L Lopes; A Schmidt; H Hartley
Journal:  JAMA       Date:  1986-11-21       Impact factor: 56.272

  9 in total
  2 in total

1.  Information for patients with cancer. Does personalization make a difference? Pilot study results and randomised trial in progress.

Authors:  R Jones; J Pearson; A Cawsey; A Barrett
Journal:  Proc AMIA Annu Fall Symp       Date:  1996

Review 2.  Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.

Authors:  Lena Davidson; Mary Regina Boland
Journal:  J Pharmacokinet Pharmacodyn       Date:  2020-04-11       Impact factor: 2.745

  2 in total

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