Implementation of a lean-focused, patient-centric rounding structure stressing essential processes was associated with increased timeliness and efficiency of rounds, improved staff and customer satisfaction, improved throughput, and reduced attending physician man-hours.

Disasters have recently received the attention of the operations research community because of the great potential of improving disaster‐related operations through the use of analytical tools, and the impact on people that this implies. In this introductory article, we describe the main characteristics of disaster supply chains, and we highlight the particular issues that are faced when managing these supply chains. We illustrate how operations research tools can be used to make better decisions, taking debris management operations as an example, and discuss potential general research directions in this area.

We present a two-phase model for a staff planning problem in a surgical department. We consider the setting where staff, in particular nurse circulators and surgical scrub technicians, are assigned to one of different service lines, and while they can be 'pooled' and temporally assigned to other service line if needed, these re-assignments should belimited. In Phase I, we decide on the number of staff hours to budget for each service line, considering policies limiting staff pooling and overtime, and different demand scenarios. In Phase II, we determine how these budgeted staff hours should be allocated across potential work days and shifts, given estimated staff requirements and shift-related scheduling restrictions. We propose a heuristic to speed the model's Phase II solution time. We implement the model using a hospital's surgical data and compare the model's results with the hospital's current practices. Using a simulation model for the surgical operations, we find that our two-phase model reduces the delays caused by staff unavailability as well as staff pooling, without increasing the workforce size. Finally, we briefly describe a decision-support tool we developed with the objective of fine-tuning staff planning decisions.

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We report a case of cardiac rhabdomyosarcoma the initial clinical features of which were pericardial effusion, clinical symptoms of congestive heart failure and probable pulmonary thromboembolism, in which echocardiography constituted the first approach to the diagnosis of cardiac tumor and MRI confirmed it, precisely delimiting the tumoral extension and possible infiltration of pericardiac structures. A brief literature review of this entity is given, the MRI findings obtained in our case are described, and we discuss the advantages and limitations of this technique as compared with other alternatives of image diagnosis.

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We develop a mathematical model for planning and scheduling staff and demand, considering a time window for on-time demand fulfillment, as well as individual staff characteristics, preferences, and availability. We also discuss a version where the staff schedule is fixed. The model can be applied in many service settings, such as warehouses, fulfillment centers, and back-office services. We develop a user-friendly decision-support tool that employs the model and the solution methodology, and we implement it in a healthcare back-office services provider, considering additional operational practices of this company, such as team leader scheduling. We conduct a computational study to develop insights regarding the trade-offs between the on-time demand fulfillment and the quality of the staff schedule, the effect of a change in the demand fulfillment time window, the impact of client behavior (e.g., batch arrivals of demand), and the consequences of considering additional preferences and operational constraints. We also evaluate the robustness of the staff schedule generated by the model under different demand scenarios. Finally, we present a heuristic to set high-quality staff schedules quickly. After the implementation, the company reported a 25% increase in staff productivity.

Denver Public Schools (DPS) serves roughly 90,000 K–12 students using a mixed bus fleet. Developing and reviewing bus-route assignments manually has been challenging and time consuming for DPS. During 2017–2018, DPS analysts reviewed and adjusted over 700 routes assigned to approximately 200 buses, considering time and capacity feasibility. We developed a decision support tool (DST) to generate feasible bus-route assignments and help inform DPS’s decisions. The DST employs optimization models to solve the bus-route assignment problem using distance data from Google Maps Application Programming Interface and various interroute reposition-time scenarios to account for the impact of potential traffic delays. The model incorporates multiple objectives related to minimizing cost, meeting demand, and maximizing “consistency”—that is, the difference between a newly created and previously implemented solution The solutions generated by the DST for the 2017–2018 school year utilized significantly fewer buses and lower reposition mileage compared with the DPS solution. Considering the convenience, efficiency, and flexibility of generating high-quality bus-route assignments using the DST, the DPS transportation team has used the DST in the route planning process since 2018.