A unified approach to approximation algorithms for bottleneck problems
Journal of the ACM (JACM)
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
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This paper describes a spatial model for healthcare workers' location in a large hospital facility. Such models have many applications in healthcare, such as supporting time-and- motion efficiency studies to improve healthcare delivery, or modeling the spread of hospital-acquired infections. We use our model to estimate spatial distributions for healthcare workers in The University of Iowa Hospitals and Clinics (UIHC), a 700-bed comprehensive academic medical center spanning a total of 3.2 million square feet and employing about 8,000 healthcare workers. We model the UIHC as a metric space induced by walking distance between pairs of rooms, and with each room having a level of attractiveness representing the activity level in that room. We combine this with a model in which each healthcare worker has a center of activity and a probability density function that decays polynomially as we move away from the center. Using 12 million Electronic Medical Record (EMR)logins collected over 22 months, we solve for the model parameters for each room and each healthcare worker using heuristic techniques to make the problem computationally tractable. We then validate the model parameters obtained by comparing realworld expectations of healthcare worker behavior for several job categories to our model predictions (e.g., we verify that Unit Clerks are much more stationary than Respiratory Therapists). Finally we present solutions to two important applications. First, we use healthcare worker spatial distributions to generate random walks representing their movement through the hospital. We use these random walks to This paper describes a spatial model for healthcare workers' location in a large hospital facility. Such models have many applications in healthcare, such as supporting timeand- motion efficiency studies to improve healthcare delivery, or modeling the spread of hospital-acquired infections. We use our model to estimate spatial distributions for healthcare workers in The University of Iowa Hospitals and Clinics (UIHC), a 700-bed comprehensive academic medical center spanning a total of 3.2 million square feet and employing about 8,000 healthcare workers. We model the UIHC as a metric space induced by walking distance between pairs of rooms, and with each room having a level of attractiveness representing the activity level in that room. We combine this with a model in which each healthcare worker has a center of activity and a probability density function that decays polynomially as we move away from the center. Using 12 million Electronic Medical Record (EMR) logins collected over 22 months, we solve for the model parameters for each room and each healthcare worker using heuristic techniques to make the problem computationally tractable. We then validate the model parameters obtained by comparing realworld expectations of healthcare worker behavior for several job categories to our model predictions (e.g., we verify that Unit Clerks are much more stationary than Respiratory Therapists). Finally we present solutions to two important applications. First, we use healthcare worker spatial distributions to generate random walks representing their movement through the hospital. We use these random walks to access that resource.