Customization options in consumer health information materials on type-2 diabetes mellitus-an analysis of modifiable features in different types of media.

Introduction: The understanding of health-related information is essential for making informed decisions. However, providing health information in an understandable format for everyone is challenging due to differences in consumers' health status, disease knowledge, skills, and preferences. Tailoring health information to individual needs can improve comprehension and increase health literacy. Objective: The aim of our research was to analyze the extent to which consumers can customize consumer health information materials (CHIMs) for type-2 diabetes mellitus through various media types. Methods: We conducted a comprehensive search for various CHIMs across various media types, such as websites, apps, videos, and printed or printable forms. A representative sample of CHIMs was obtained for analysis through blocked randomization across the various media types. We conducted a quantitative content analysis to determine the frequency of user-centered customization options. Cross-comparisons were made to identify trends and variations in modifiable features among the media. Results: In our representative sample of 114 CHIMs, we identified a total of 24 modifiable features, which we grouped into five main categories: (i) language, (ii) text, (iii) audiovisual, (iv) presentation, and (v) medical content. Videos offered the most customization opportunities (95%), while 47% of websites and 26% of apps did not allow users to tailor health information. None of the printed or printable materials provided the option to customize the information. Overall, 65% of analyzed CHIMs did not allow users to tailor health information according to their needs. Conclusion: Our results show that CHIMs for type-2 diabetes mellitus could be significantly improved by providing more customization options for users. Further research is needed to investigate the effectiveness and usability of these options to enhance the development and appropriate provision of modifiable features in health information.

Check the box! How to deal with automation bias in AI-based personnel selection.

Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system's recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality.