A tactical multi-week implicit tour scheduling model with applications in healthcare.

In many healthcare tactical scheduling analyses, we need to solve large tour scheduling problems in which required staffing levels vary by time of day and day of week. A tour is a set of shift start times and shift lengths worked over a scheduling horizon of one or more weeks. As the degree of scheduling flexibility increases, the resulting tour scheduling problems get larger and this increase in size is exacerbated when the scheduling horizon is longer than one week. In this article, we present a tactical multi-week implicit tour scheduling model intended to complement operational scheduling systems. The implicit nature of the model allows us to solve problems that would be prohibitively large if modeled using traditional explicit tour scheduling approaches. We incorporate a variety of tour types with both intra-tour start time and shift length flexibility as well as varying degrees of weekend flexibility. We test the performance of our models on a set of medical units with different demand patterns. Computational experiments have shown that the developed implicit model can play an important role in quantifying trade-offs between labor costs, understaffing levels and scheduling flexibility. Our models have been released as an open source project in the hopes of facilitating practitioner use and also providing access to other scheduling researchers.

Global patterns and drivers of ecosystem functioning in rivers and riparian zones.

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth's biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented "next-generation biomonitoring" by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.

An open source software project for obstetrical procedure scheduling and occupancy analysis.

Increases in the rate of births via cesarean section and induced labor have led to challenging scheduling and capacity planning problems for hospital inpatient obstetrical units. We present occupancy and patient scheduling models to help address these challenges. These patient flow models can be used to explore the relationship between procedure scheduling practices and the resulting occupancy on inpatient obstetrical units such as labor and delivery and postpartum. The models capture numerous important characteristics of inpatient obstetrical patient flow such as time of day and day of week dependent arrivals and length of stay, multiple patient types and clinical interventions, and multiple patient care units with inter-unit patient transfers. We have used these models in several projects at different hospitals involving design of procedure scheduling templates and analysis of inpatient obstetrical capacity. In the development of these models, we made heavy use of open source software tools and have released the entire project as a free and open source model and software toolkit.

Short term hospital occupancy prediction.

Inpatient census, or occupancy, is a primary driver of resource use in hospitals. Fluctuations in occupancy complicate decisions related to staffing, bed management, ambulance diversions, and may ultimately impact both quality of patient care and nursing job satisfaction. We describe our approach in building a computerized model to provide short-term occupancy predictions for an entire hospital by nursing unit and shift. Our model is a comprehensive system built using real hospital data and utilizes statistical predictions at the individual patient level. We discuss the results of piloting an early version of the model at a mid-size community hospital. The primary focus of the paper is on the development and methodology of a second generation of the predictive occupancy model. The results and accuracy of this new model is compared to a variety of other predictive methods based on tests using 2 years of actual hospital data.

Collection and preparation of sensor network data to support modeling and analysis of outpatient clinics.

Simulation studies of outpatient clinics often involve significant data collection challenges. We describe an approach for data collection using sensor networks which facilitates the collection of a large volume of very detailed patient flow data through healthcare clinics. Such data requires extensive preprocessing before it is ready for analysis. We present a general data preparation framework for sensor network generated data with particular emphasis on the creation and analysis of patient path strings. Several examples of the analysis of sensor network data are also presented. Our approach has been used in two large outpatient clinics in the United States.

Hillmaker: an open source occupancy analysis tool.

Managerial decision making problems in the healthcare industry often involve considerations of customer occupancy by time of day and day of week. We describe an occupancy analysis tool called Hillmaker which has been used in numerous healthcare operations studies. It is being released as a free and open source software project.

Data mining to support simulation modeling of patient flow in hospitals.

Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.

Simulation analysis of pneumatic tube systems.

Pneumatic tube systems play an important material handling role in many hospitals. These systems are costly and complex to design and operate, yet little exists in the way of analytical methodologies for them. We present a decision support framework based on defining relevant system performance metrics, traffic analysis reporting, as well as discrete event simulation modeling. We have used this approach to analyze numerous pneumatic tubes systems in the United States and present a representative case study from a large tertiary care hospital. Our general approach can be generalized to other computer controlled hospital operational systems such as elevators, track vehicles, automatic guided vehicles, workflow enabled processes, and laboratory automation systems.