Consumer Health Informatics to Advance Precision Prevention

Area of Science:

• Consumer health informatics (CHI)
• Precision prevention
• Public health
• Chronic disease management

Background:

• Traditional healthcare models are unsustainable.
• Consumer health informatics (CHI) offers a shift towards proactive, personalized healthcare.
• Precision prevention aims to mitigate the global burden of chronic diseases.

Purpose of the Study:

• To review how consumer health informatics (CHI) can advance precision prevention.
• To explore CHI's role in public health policy, individual risk assessment, early detection, and tailored interventions.
• To guide consumers, researchers, and practitioners in evidence generation for practice translation.

Main Methods:

• Narrative synthesis of recent (2018 onwards) interventional studies on CHI for precision prevention.
• Analysis of research trends, ethical considerations, and future directions.
• Inclusion of a health consumer co-author for a balanced perspective.

Main Results:

• CHI enables precision prevention through population surveillance, predictive modeling (machine learning), digital phenotyping (wearables, mHealth), and personalized behavior change interventions.
• Evidence for CHI's effectiveness in improving health outcomes was mixed across case studies.
• Research trends emphasize inclusive design, data governance, and federated learning.

Conclusions:

• Consumer health informatics (CHI) and precision prevention can transform healthcare from a reactive 'break-fix' to a proactive 'predict-prevent' model.
• Strong partnerships between researchers, practitioners, and consumers are crucial.
• Generating high-quality evidence through robust study designs is essential for practice translation.