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To provide human judgment input for computer algorithm development, this study examines the relative importance of source, content, and style cues in predicting the truthfulness ratings of two common types of online health information: news stories and institutional news releases.

This study employed a multi-method approach using (1) a manual content analysis of 400 randomly selected online health news stories and news releases from HealthNewsReview.org and (2) an online experiment comparing truthfulness ratings between news stories and news releases.

Using content analysis, the authors found significant differences in the importance of source, content, and style cues in predicting truthfulness ratings of news stories and news releases: source and style cues predicted truthfulness ratings better than content cues. In the experiment, source credibility was the most important predictor of truthfulness ratings, controlling for individual differences. Experts have higher ratings for news media stories than news releases and lay people have no differences in rating the two news formats.

It is important for health educators to curb consumer trust in misinformation and increase health information literacy. Rather than solely reporting scientific evidence, educators should focus on addressing cues people use to judge the truthfulness of health information.

This is the first study that directly compares human judgments of health news stories and news releases. Using both the breadth of content analysis and experimental causality testing, the authors evaluate the relative importance of source, content, and style cues in predicting truthfulness ratings.

Health Information Exchange (HIE) is essential for the efficient and cost-effective delivery of health-care services. The provider’s administrative structure and external environment can substantially influence adopting technologies involving inter-organizational linkages, such as HIE. Using the theoretical lens of institutional theory, this study aims to compare how public and private hospitals' engagement in HIE is influenced by corruption and government online services or e-government usage.

The study uses the positivist research design of secondary data analysis to test the six hypotheses proposed. Data from multiple third-party reliable sources, including the European Commission and World Bank, are combined into the final dataset consisting of observations from 1,442 hospitals across 30 countries in Europe. A multilevel modeling approach is used to associate country and hospital-level variables and test the hypothesis.

The study finds that, on average, a 10% increase in corruption leads to a 6.3% decrease, while a 10% increase in e-government leads to a 7% increase in the probability of HIE engagement for a hospital. The negative impact of corruption on average is 18% more in public than private hospitals, while the positive impact of e-government is 75% stronger in public in comparison to private hospitals. The study also finds that HIE engagements in health systems with predominantly public hospitals are more sensitive to corruption and e-government.

To the best of the authors’ knowledge, the study is one of the first to use the institutional view to test the influence of government actions and public providers' concentration on HIE engagement. The comparison of public and private institutions enriches our understanding of promoters and inhibitors of HIE.