title: Predicting hospital visits from geo-tagged Internet search logs.
abstract: The steady rise in healthcare costs has deprived over 45 million Americans of healthcare services (1, 2) and has encouraged healthcare providers to look for opportunities to improve their operational efficiency. Prior studies have shown that evidence of healthcare seeking intent in Internet searches correlates well with healthcare resource utilization. Given the ubiquitous nature of mobile Internet search, we hypothesized that analyzing geo-tagged mobile search logs could enable us to machine-learn predictors of future patient visits. Using a de-identified dataset of geo-tagged mobile Internet search logs, we mined text and location patterns that are predictors of healthcare resource utilization and built statistical models that predict the probability of a user's future visit to a medical facility. Our efforts will enable the development of innovative methods for modeling and optimizing the use of healthcare resources-a crucial prerequisite for securing healthcare access for everyone in the days to come.
authors: Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
authors: Vibhu
authors: V
authors: Agarwal
authors: Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
authors: Lichy
authors: L
authors: Han
authors: Department of Computer Science, Stanford University, Stanford, CA.
authors: Isaac
authors: I
authors: Madan
authors: Department of Computer Science, Stanford University, Stanford, CA.
authors: Shaurya
authors: S
authors: Saluja
authors: Department of Computer Science, Stanford University, Stanford, CA.
authors: Aaditya
authors: A
authors: Shidham
authors: Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
authors: Nigam H
authors: NH
authors: Shah