Bridging the gap: Experimental evidence on information provision and health insurance choices.

Previous research has shown that individuals do not always make rational decisions when selecting their health insurance, for example, due to the existence of information frictions or mental gaps. We study the effect of specific types of information provision for decision support on health plan choices and test their potential to improve decision quality by implementing a randomized laboratory experiment. We provide personalized and generic aids, differentiate between numerical and visual decision support, and provide one or two optional formats of personalized information. We find that generic aids have no effect on health plan choices while personalized information leads to better choices as measured by several indicators of decision quality. The largest effects were observed for those who "opted in" to visualize personalized information, with immediate and lasting improvements in health insurance decisions. By reducing information frictions, our results suggest that accessible and easy-to-use tools can positively impact health insurance navigation, improve decision-making, and reduce switching costs.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action.

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.