Factors Affecting Engagement in Web-Based Health Care Patient Information: Narrative Review of the Literature (Preprint)

Oktay, Liam Alperen; Abuelgasim, Eyad; Abdelwahed, Aida; Houbby, Nour; Lampridou, Smaragda; Normahani, Pasha; Peters, Nicholas S.; Jaffer, Usman

doi:10.2196/preprints.19896

Cited by 2 publications

(1 citation statement)

References 71 publications

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“…A systematic review evaluating factors associated with engaging web-based content revealed the following key categories: textual information, discussion boards and web-based groups, video content, visual or pictographs, device accessibility, stage of patient journey, credibility, and completeness of information [ 57 ]. A framework was developed for each category describing the factors that should be considered when designing an effective content.…”

Section: Content Personalizationmentioning

confidence: 99%

Harnessing Machine Learning to Personalize Web-Based Health Care Content

Guni¹,

Normahani²,

Davies³

et al. 2021

J Med Internet Res

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Web-based health care content has emerged as a primary source for patients to access health information without direct guidance from health care providers. The benefit of this approach is dependent on the ability of patients to access engaging high-quality information, but significant variability in the quality of web-based information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant, or inaccessible content. Personalization positions the patient at the center of health care models by considering their needs, preferences, goals, and values. However, the traditional methods used thus far in health care to determine the factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The health care sector has lagged behind other industries in implementing ML to analyze user and content features, which can automate personalized content recommendations on a mass scale. With the advent of big data in health care, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health and support education, self-management, and positive behavior change as well as to enhance clinical outcomes.

show abstract

Section: Content Personalizationmentioning

confidence: 99%

Harnessing Machine Learning to Personalize Web-Based Health Care Content

Guni¹,

Normahani²,

Davies³

et al. 2021

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

Harnessing Machine Learning to Personalize Web-Based Health Care Content (Preprint)

Guni¹,

Normahani²,

Davies³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

UNSTRUCTURED Online healthcare content has emerged as a primary source for patients to access health information without direct guidance from healthcare providers. This is dependent on patients being able to access engaging high-quality information, but significant variability in the quality of online information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant or inaccessible content. Personalisation positions the patient at the centre of healthcare models by considering their needs, preferences, goals and values. However, traditional methods used thus far in healthcare to determine factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The healthcare sector has lagged behind other industries in implementing ML to analyse user and content features, which can automate personalised content recommendation on a mass scale. With the advent of ‘big data’ in healthcare, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content in order to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health, supporting education, self-management, positive behaviour change and enhancing clinical outcomes.

show abstract

Factors Affecting Engagement in Web-Based Health Care Patient Information: Narrative Review of the Literature (Preprint)

Cited by 2 publications

References 71 publications

Harnessing Machine Learning to Personalize Web-Based Health Care Content

Harnessing Machine Learning to Personalize Web-Based Health Care Content

Harnessing Machine Learning to Personalize Web-Based Health Care Content (Preprint)

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