Maximization of Open Hospital Capacity under Shortage of SARS-CoV-2 Vaccines-An Open Access, Stochastic Simulation Tool.

Motive. The Covid-19 pandemic has led to the novel situation that hospitals must prioritize staff for a vaccine rollout while there is acute shortage of the vaccine. In spite of the availability of guidelines from state agencies, there is partial confusion about what an optimal rollout plan is. This study investigates effects in a hospital model under different rollout schemes. Methods. A simulation model is implemented in VBA, and is studied for parameter variation in a predefined hospital setting. The implemented code is available as open access supplement. Main results. A rollout scheme assigning vaccine doses to staff primarily by staff's pathogen exposure maximizes the predicted open hospital capacity when compared to a rollout based on a purely hierarchical prioritization. The effect increases under resource scarcity and greater disease activity. Nursing staff benefits most from an exposure focused rollout. Conclusions. The model employs SARS-CoV-2 parameters; nonetheless, effects observable in the model are transferable to other infectious diseases. Necessary future prioritization plans need to consider pathogen characteristics and social factors.

The impact of medical documentation assistants on process performance measures in a surgical emergency department.

BACKGROUND: The administrative work of physicians, particularly documentation effort, consumes considerable time in surgical emergency departments. At the same time, the latter face an ever-growing influx of patients, leading to increasing waiting and flow times and thus patient dissatisfaction as well as overload of physicians and nurses. The deployment of medical documentation assistants, who specialize in and undertake documentation work currently performed by physicians, poses a solution to the problem. The goal of this study is to assess the impact of deploying medical documentation assistants on key performance indicators of a surgical emergency department, i.e. waiting and flow times of patients differentiated according to triage categories, utilization of physicians and time allocation of physicians. METHODS: The underlying study has analysed the processes of the surgical emergency department of a major university medical centre and modelled them in a discrete event simulation. Data on patient arrivals as well as processing times in the X-ray department and the laboratory were obtained from the clinical information system, while processing times in the emergency department were recorded using time-motion studies. Though the emergency department currently does not deploy medical documentation assistants, the simulation model includes a variable number of such assistants. RESULTS: The deployment of a medical documentation assistant frees up physician working time and decreases the waiting time and consequently the flow time of patients, in particular for standard and non-urgent patients. Adding additional documentation assistants leads to further improvements, however, with diminishing marginal returns. Under the assumption of medical documentation assistants being 35% more efficient than physicians in undertaking documentation work, one of the three physicians can be replaced in the analysed surgical emergency department with an average of 502 patient arrivals per week. CONCLUSIONS: Medical documentation assistants are a viable way of improving the performance of surgical emergency departments. Depending on the goals of the hospital, medical documentation assistants can be used for an array of measures such as decreasing patients' waiting and flow times or increasing physicians' time spent on medical treatment.

Overutilization and underutilization of operating rooms - insights from behavioral health care operations management.

The planning of surgery durations is crucial for efficient usage of operating theaters. Both planning too long and too short durations for surgeries lead to undesirable consequences, e.g. idle time, overtime, or rescheduling of surgeries. We define these consequences as operating room inefficiency. The overall objective of planning surgery durations is to minimize expected operating room inefficiency, since surgery durations are stochastic. While most health care studies assume economically rational behavior of decision makers, experimental studies have shown that decision makers often do not act according to economic incentives. Based on insights from health care operations management, medical decision making, behavioral operations management, as well as empirical observations, we derive hypotheses that surgeons' behavior deviates from economically rational behavior. To investigate this, we undertake an experimental study where experienced surgeons are asked to plan surgeries with uncertain durations. We discover systematic deviations from optimal decision making and offer behavioral explanations for the observed biases. Our research provides new insights to tackle a major problem in hospitals, i.e. low operating room utilization going along with staff overtime.

Improving Intensive Care Unit and Ward Utilization by Adapting Master Surgery Schedules.

With increasing organizational and financial pressure on hospitals, each individual surgical treatment has to be reviewed and planned thoroughly. Apart from the expensive operating room facilities, proper staffing and planning of downstream units, like the wards or the intensive care units (ICUs), should be considered as well. In this article, we outline the relationship between a master surgery schedule (MSS), i.e., the assignment of surgical blocks to medical specialties, and the bed demand in the downstream units using an analytical model. By using historical data retrieved from the clinical information system and a patient flow model, we applied a recently developed algorithm for predicting bed demand based on the MSSs for patients of 3 surgical subspecialties of a hospital. Simulations with 3 different MSSs were performed. The impact on the required amount of beds in the downstream units was analyzed. We show the potential improvements of the current MSS considering 2 main goals: leveling workload among days and reduction of weekend utilization. We discuss 2 different MSSs, one decreasing the weekend ICU utilization by 20% and the other one reducing maximum ward bed demand by 7%. A test with 12 months of real-life data validates the results. The application of the algorithm provides detailed insights for the hospital into the impact of MSS designs on the bed demand in downstream units. It allowed creating MSSs that avoid peaks in bed demand and high weekend occupancy levels in the ICU and the ward.

Flexible shift scheduling of physicians.

This research addresses a shift scheduling problem in which physicians at a German university hospital are assigned to demand periods over a planning horizon that can extend up to several weeks. When performing the scheduling it is necessary to take into account a variety of legal and institutional constraints that are imposed by a national labor agreement, which governs all physicians in German university hospitals. Currently, most medical departments develop their staff schedules manually at great cost and time. To solve the problem, a new modeling approach is developed that requires shifts to be generated implicitly. Rather than beginning with a predetermined number of shift types and start times, shifts are allowed to start at every pre-defined period in the planning horizon and extend up to 13 h with an hour-long break included. The objective is to find an assignment such that the total hours that have to be paid out as overtime are minimal under the restrictions given by the labor agreement. The problem is formulated as a mixed-integer program and solved with CPLEX. During the solution process individual lines-of-work are constructed for each physician. Using data from an anesthesia department, computational results indicate that high quality schedules can be obtained much more quickly than by current practice.

The performance of a generalized Bailey-Welch rule for outpatient appointment scheduling under inpatient and emergency demand.

We are considering the problem of scheduling a given number of outpatients to a medical service facility with two resources servicing outpatients, inpatients, and emergency patients. Each of the three patient classes has associated class-specific arrival processes and cost-figures. The objective is to maximize the total expected reward which is made of revenues for served patients, costs for letting patients wait, and costs for denial of service. For this problem we propose a generalization of the well-known Bailey-Welch rule as well as a neighborhood search heuristic. We analyze the impact of different problem parameters on the total reward and the structure of the derived appointment schedules and address the question of the number of outpatients to be scheduled. The results show that the generalized Bailey-Welch rule performs astonishingly well over a wide range of problem parameters.