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Front line health care providers (HCPs) play a central role in endemic (e.g., pertussis), epidemic (e.g., influenza) and pandemic (e.g., avian influenza) infectious disease outbreaks. Effective preparedness for this role requires access to and awareness of population-based data (PBD). We investigated the degree to which this is currently achieved among HCPs in Utah by surveying a sample about access, awareness and attitudes concerning PBD in clinical practice. We found variability in the number and nature (national vs. local, pushed vs. pulled) of PBD sources accessed by HCPs, with a subset using multiple sources and using them frequently. We found that HCPs believe PBD improves their clinical performance and that they cannot rely on their own practice to remain informed. These findings suggest that an integrated system, which interprets PBD from multiple sources and optimizes the delivery of PBD, may facilitate preparedness of HCPs through the application of PBD in routine clinical practice.

Developing a forecasting tool for patient census allows for improved staffing, better resource utilization and mobilization, and improved timing of educational campaigns around the disease control process. Using a neural network approach we evaluated several different models and variables for predicting patient census prospectively. These initial studies enabled selection of a subset of predictor variables and show that different network models, and variables must be used based on the season.

Hospital bed demands sometimes exceed capacity, leading to delays in patient admissions, transfers and cancellations of surgical procedures. Effective strategies must be in place for an efficient use of existing beds. Establishing such strategies at academic hospitals poses serious challenges. We developed and implemented a web-based software application called “Patient Tracker” to manage the discharge process, minimize delays in admission and reduce surgical procedure cancellations. We also tested the effectiveness of the software on the work flow by comparing outcomes between the pre-implementation control group (2002–2003) and the post-implementation experimental group (2003–2006). Following the implementation of the software, the number of cancelled surgical procedures decreased (120 vs. 12, p