Online healthcare platforms allow physicians and patients to communicate in a timely manner. Yet little is known about how physicians’ online and offline activities affect each other and, consequently, the healthcare system. We collected data from both online and offline channels to study physicians’ online-offline behavior dynamics. We find that physicians’ online activities can lead to a higher service quantity in offline channels, whereas offline activities may reduce physicians’ online services because of resource constraints. We also find that the more offline patients that physicians serve, the more articles the physicians will likely share in online healthcare platforms. These findings are of great importance to practitioners and policy makers. Our work provides evidence that online healthcare platforms supplement offline services and thus lessen the concern that physicians’ participation in online healthcare platforms will negatively influence offline healthcare services. Our findings also indicate the need for the improvement of online-offline coordination and better system design.

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Choice overload is a common problem in many online settings, including healthcare. Online healthcare platforms tend to provide a large variety of behavior intervention information or programs to help individuals modify their lifestyles to improve wellness. However, having too many options can significantly increase searching cost, prevent users from discovering the truly relevant interventions, and harm users’ long-term healthcare decision-making efficiency. This motivates us to propose a personalized healthcare recommendation system to provide tailored support for individuals’ intervention participation. The proposed framework, a deep-learning and diversity-enhanced multiarmed bandit (DLDE-MAB), integrates several predictive and prescriptive analytics components to combat the unique challenges presented in the healthcare recommendation setting. It leverages online machine learning to provide adaptive and real-time support, a theory-guided diversity promotion scheme to cover multiple healthcare needs, and deep learning to further enhance dynamic context representation. Through extensive experiments, we show that the proposed framework outperforms various competing models in terms of its adaptivity to data dynamics, diversity, and uncertainty. The proposed model and evaluation results provide important implications for business intelligence and personalized, contextualized, and agile healthcare decision making.