A Semantic Computer-Enabled Architecture For The Provision Of Personalised Patient Education

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Background:Current approaches to patient education include the provision of standardised pamphlets. However the effectiveness of this approach may be hampered due to a patient’s inability or motivation to engage with generic material. Personalisation presents a means to enhance the usability of patient education.Material & Methods:We developed a web-based architecture to provide personalised education to diabetic patients. Semantic technologies were utilised to link and reason on social demographic data to support the personalisation. A Web Ontology Language (OWL) ontology was used to model various features of diabetes such as symptoms, treatments and complications. A user model was also represented in the ontology. This captured the personal and educational characteristics of a patient along with their health status. Personalisation rules, represented using Semantic Web Rule Language (SWRL), were developed to facilitate the adaptation of the education material to the individual needs of each patient. The rules utilised the data captured in the ontology to determine the composition, style and readability of the education. Results:The personalised education was tailored to the health status of each patient, focusing on their particular experience of symptoms, treatments and complications. Moreover, in order to assist the patient’s comprehension the textual information was adapted to a suitable readability level. Readability was also enhanced by displaying the text at an appropriate size and style for each patient. We also attempted to enhance engagement by including images that were personalised by age group and gender. The educational content was adaptable to changes in the patient’s health status.Conclusions:Personalisation presents a means to enhance the effectiveness of patient education through the provision of interactive material that focuses on the particular patient’s needs and health objectives. Semantic web technologies may be utilised to adapt the presentation and content of the education to enhance patient engagement and increase their health literacy.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages1
Publication statusPublished - 27 Oct 2015
Event7th Annual Translational Medicine Conference - Derry/Londonderry
Duration: 27 Oct 2015 → …

Conference

Conference7th Annual Translational Medicine Conference
Period27/10/15 → …

Fingerprint

Computer Systems
Patient Education
Semantics
Education
Health Status
Language
Technology
Health Literacy
Patient Participation
Pamphlets
Motivation
Age Groups
Demography
Health
Therapeutics

Keywords

  • Patient Education
  • ontology
  • knowledge engineering

Cite this

@inproceedings{39d1a330febd4b54b3411e1ff8260a9b,
title = "A Semantic Computer-Enabled Architecture For The Provision Of Personalised Patient Education",
abstract = "Background:Current approaches to patient education include the provision of standardised pamphlets. However the effectiveness of this approach may be hampered due to a patient’s inability or motivation to engage with generic material. Personalisation presents a means to enhance the usability of patient education.Material & Methods:We developed a web-based architecture to provide personalised education to diabetic patients. Semantic technologies were utilised to link and reason on social demographic data to support the personalisation. A Web Ontology Language (OWL) ontology was used to model various features of diabetes such as symptoms, treatments and complications. A user model was also represented in the ontology. This captured the personal and educational characteristics of a patient along with their health status. Personalisation rules, represented using Semantic Web Rule Language (SWRL), were developed to facilitate the adaptation of the education material to the individual needs of each patient. The rules utilised the data captured in the ontology to determine the composition, style and readability of the education. Results:The personalised education was tailored to the health status of each patient, focusing on their particular experience of symptoms, treatments and complications. Moreover, in order to assist the patient’s comprehension the textual information was adapted to a suitable readability level. Readability was also enhanced by displaying the text at an appropriate size and style for each patient. We also attempted to enhance engagement by including images that were personalised by age group and gender. The educational content was adaptable to changes in the patient’s health status.Conclusions:Personalisation presents a means to enhance the effectiveness of patient education through the provision of interactive material that focuses on the particular patient’s needs and health objectives. Semantic web technologies may be utilised to adapt the presentation and content of the education to enhance patient engagement and increase their health literacy.",
keywords = "Patient Education, ontology, knowledge engineering",
author = "Susan Quinn and Raymond Bond and Chris Nugent",
year = "2015",
month = "10",
day = "27",
language = "English",
booktitle = "Unknown Host Publication",

}

Quinn, S, Bond, R & Nugent, C 2015, A Semantic Computer-Enabled Architecture For The Provision Of Personalised Patient Education. in Unknown Host Publication. 7th Annual Translational Medicine Conference, 27/10/15.

A Semantic Computer-Enabled Architecture For The Provision Of Personalised Patient Education. / Quinn, Susan; Bond, Raymond; Nugent, Chris.

Unknown Host Publication. 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Quinn, Susan

AU - Bond, Raymond

AU - Nugent, Chris

PY - 2015/10/27

Y1 - 2015/10/27

N2 - Background:Current approaches to patient education include the provision of standardised pamphlets. However the effectiveness of this approach may be hampered due to a patient’s inability or motivation to engage with generic material. Personalisation presents a means to enhance the usability of patient education.Material & Methods:We developed a web-based architecture to provide personalised education to diabetic patients. Semantic technologies were utilised to link and reason on social demographic data to support the personalisation. A Web Ontology Language (OWL) ontology was used to model various features of diabetes such as symptoms, treatments and complications. A user model was also represented in the ontology. This captured the personal and educational characteristics of a patient along with their health status. Personalisation rules, represented using Semantic Web Rule Language (SWRL), were developed to facilitate the adaptation of the education material to the individual needs of each patient. The rules utilised the data captured in the ontology to determine the composition, style and readability of the education. Results:The personalised education was tailored to the health status of each patient, focusing on their particular experience of symptoms, treatments and complications. Moreover, in order to assist the patient’s comprehension the textual information was adapted to a suitable readability level. Readability was also enhanced by displaying the text at an appropriate size and style for each patient. We also attempted to enhance engagement by including images that were personalised by age group and gender. The educational content was adaptable to changes in the patient’s health status.Conclusions:Personalisation presents a means to enhance the effectiveness of patient education through the provision of interactive material that focuses on the particular patient’s needs and health objectives. Semantic web technologies may be utilised to adapt the presentation and content of the education to enhance patient engagement and increase their health literacy.

AB - Background:Current approaches to patient education include the provision of standardised pamphlets. However the effectiveness of this approach may be hampered due to a patient’s inability or motivation to engage with generic material. Personalisation presents a means to enhance the usability of patient education.Material & Methods:We developed a web-based architecture to provide personalised education to diabetic patients. Semantic technologies were utilised to link and reason on social demographic data to support the personalisation. A Web Ontology Language (OWL) ontology was used to model various features of diabetes such as symptoms, treatments and complications. A user model was also represented in the ontology. This captured the personal and educational characteristics of a patient along with their health status. Personalisation rules, represented using Semantic Web Rule Language (SWRL), were developed to facilitate the adaptation of the education material to the individual needs of each patient. The rules utilised the data captured in the ontology to determine the composition, style and readability of the education. Results:The personalised education was tailored to the health status of each patient, focusing on their particular experience of symptoms, treatments and complications. Moreover, in order to assist the patient’s comprehension the textual information was adapted to a suitable readability level. Readability was also enhanced by displaying the text at an appropriate size and style for each patient. We also attempted to enhance engagement by including images that were personalised by age group and gender. The educational content was adaptable to changes in the patient’s health status.Conclusions:Personalisation presents a means to enhance the effectiveness of patient education through the provision of interactive material that focuses on the particular patient’s needs and health objectives. Semantic web technologies may be utilised to adapt the presentation and content of the education to enhance patient engagement and increase their health literacy.

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