JMIR Publications

ABSTRACT

Background:

Seeking health information on the Internet is very popular despite of debatable ability of lay users to evaluate quality of health information and uneven quality of information available online. Consulting Internet for health information is pervasive particularly when other sources are inaccessible due to time, distance and money constraints or to explore sensitive or embarrassing questions. Q&A platforms are online services which provide personalized health advice upon information seekers’ request. However, it is not clear how quality of health advices is ensured in these platforms.

Objective:

To identify how platform design impact quality of online health advices on Internet and equal access to health information

Methods:

900 questions and answers collected from 9 Q&A platforms with different design features. Data on the design features for each platform were generated. Paid physicians evaluated the data to quantify the quality of health advice. Guided by literature, the design features that affect information quality were identified and recorded for each Q&A platform. Machine learning techniques including LASSO and unbiased regression tree were used for the analysis.

Results:

Q&A platform design and health advice quality are related. ‘Expertise of information providers’ (beta=0.48, p<.001), ‘financial incentive’ (beta=0.4, p<.001), ‘extended reputation’ (beta=0.28, p=.002) and ‘question quality’ (beta=0.12, p=.001) best predict health advice quality. ‘Virtual incentive’, ‘Web 2.0 mechanisms’, ‘Reputation systems’ are not associated with health advice quality.

Conclusions:

Access to high quality health advice on Internet is unequal and skewed toward high income and high literacy groups. Yet, there are possibilities to generate high quality health advice for free.