Current Projects

This research aims to quantify the workload experienced by EMS personnel in real time using system-generated data, along with work measurements performed by a team of WSU students. The project uses techniques from data analytics, simulation, and optimization.

This research aims to quantify the extent of potential therapeutic gain that can be achieved by altering the radiation dose distribution across treatment sessions in fractionated radiotherapy. The design of optimal radiotherapy plans with spatiotemporally heterogeneous dose distributions gives rise to large-scale, nonconvex treatment plan optimization problems. We employ global optimization techniques to solve these treatment plan optimization problems and quantify the potential gain over traditional radiotherapy planning approaches using phantom cancer cases.

This research focuses on designing inpatient care tasks so that necessary interruptions can be integrated into the workday. We compare the risks associated with interrupting a primary task with the risks of not addressing a secondary task immediately to determine the recommended action for handling such interruptions.

This research focuses on the decisions made by a single worker over the short term, after higher-level decisions have been made; staff has been scheduled, and actual work must be accomplished. Examples of work execution decisions include the sequencing of tasks to be performed throughout a workday, the actions available to address randomly arising tasks or disruptions, and revisions to task sequences upon realizing variability in work plans and disruptions. Theoretical constructs from state-of-the-art research on interruptions will be integrated with machine scheduling techniques using a computer-based, interactive, and learning-enabled simulation modeling approach. The identified work execution decisions will be studied in terms of their impacts on operational outcomes (i.e., quality, productivity, and workload).

The objective of this research is to develop model-driven staffing strategies for nursing care delivery in inpatient care settings. Traditionally, nurse-to-patient ratios have been used to staff inpatient care units, specifying the number of patients that can be safely supervised by a nurse. However, patients often require different levels of care based on the severity of their medical conditions. Furthermore, not all care tasks require the support of highly trained registered nurses; therefore, hospitals often employ nursing staff with different skill levels for cost-saving purposes. The heterogeneity in patient mix and nursing skill mix can potentially render ratio-based staffing strategies ineffective. We incorporate this heterogeneity into staffing decisions using systems engineering approaches. In particular, queueing theory and discrete-event simulation techniques are used to determine optimal nursing skill-mix configurations that minimize staffing costs while ensuring the timely delivery of care.

The Institute of Medicine established that quality of care has six quality aims: effectiveness, efficiency, timeliness, safety, patient-centeredness, and equitability. This research uses data from the Michigan Trauma Quality Improvement Program to define composite performance metrics for each quality aim and to perform multivariate analyses of trauma care quality considering all six aims. The project is a collaboration between Wichita State University, Bronson Methodist Hospital Trauma Center, and the Western Michigan University School of Nursing.

This research aims to quantify the extent of potential therapeutic gain that can be achieved by altering the radiation dose distribution across treatment sessions in fractionated radiotherapy. The design of optimal radiotherapy plans with spatiotemporally heterogeneous dose distributions gives rise to large-scale, nonconvex treatment plan optimization problems. We employ global optimization techniques to solve these treatment plan optimization problems and to quantify the potential gain over traditional radiotherapy planning approaches using phantom cancer cases.

Unidirectional leaf‑sequencing schemes have traditionally been used for the dynamic delivery of intensity‑modulated radiotherapy (IMRT) plans, in which, to modulate a desired fluence map, multileaf collimator (MLC) leaves start from one end of the radiation field, sweep across it, and stop at the opposite end. This research relaxes the unidirectional leaf‑motion restriction and develops exact and heuristic leaf‑sequencing approaches to obtain optimal bidirectional leaf trajectories. The trade‑off between fluence modulation quality and required beam‑on time is quantified for fluence maps with different levels of complexity using both the proposed and traditional leaf trajectory optimization methods.