Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions

Area of Science:

• Health Informatics
• Human-Computer Interaction
• Preventive Medicine

Background:

• Preventive care identifies health issues early for effective treatment.
• The internet provides vast health information, but can be overwhelming.
• Recommender systems can filter and personalize health information for users.

Purpose of the Study:

• To identify key factors influencing patient use of recommender systems for preventive care.
• To propose practical, evidence-based propositions for recommender system implementation.
• To outline a study design, survey methods, and analysis techniques.

Main Methods:

• A 6-stage approach to examine user perceptions of recommender systems for preventive care.
• Formulating research propositions for empirical testing.
• Developing and validating a survey instrument using expert analysis and validity testing.
• Collecting data from ~600 participants via Amazon Mechanical Turk.
• Analyzing data using statistical methods including factor analysis and correlational analysis.

Main Results:

• Data collection and analysis are pending institutional review board approval.
• The study will provide insights into factors influencing recommender system adoption in healthcare.

Conclusions:

• Integrating recommender systems into healthcare can improve preventive care delivery and patient experiences.
• These systems support precision medicine and best practices, aligning with the quadruple aims of healthcare.