No abstract

The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020. Hence, this issue has received considerable critical attention with the policy makers. Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program. In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data. Our results are promising, and they benchmark previous results in predicting hospital readmissions. The contributions of this study add to existing literature on healthcare analytics.

The multi-dimensionality of online word-of-mouth not only provides rich attribute-level information but also influences the attribute preference construction of the online consumer. Though prior research affirms that consumer reviews impact the attribute preference assessment of a consumer in a non-personalized single-product environment, in a personalized, multiple alternative environment, consumers' behavior could be completely different and requires separate attention. Building on the information processing approach and constructive preference perspective, our research analyzes how personalization influences this swaying effect, i.e., the influence of personalization on the attribute preference of a consumer. We conducted a multi-group experiment with four different types of personalization - non-personalized information (no personalization), self-referent information, relevant information, and both self-referent and relevant information. Our results show evidence of a swaying effect of personalization on consumers' attribute preference for products. We found that users, when exposed to different types of personalization, experience different levels of the swaying effect on their attribute preferences of the product. This study contributes significantly to the current discourse on the setbacks of web personalization and also informs practicing managers on how to develop recommender system strategies.

No abstract