Operations Research for Health Care最新文献

Markov model allows medical prognosis to be modeled with health state transitions over time and are particularly useful for decisions regarding diseases where uncertain events and outcomes may occur. To provide sufficient detail for operations researchers to carry out a Markov analysis, we present a detailed example of a Markov model with five health states with monthly transitions with stationary transition probabilities between states to model the cost and effectiveness of two treatments for advanced cervical cancer. A different approach uses survival curves to directly model the fraction of patients in each state at each time period without the Markov property. We use this alternative method to analyze the cervical cancer case and compare the Markov and non-Markov approaches. These models provide useful insights about both the effectiveness of treatments and the associated costs for healthcare decision makers.

This is a sequel to Whitt and Zhang (2017), in which we developed an aggregate stochastic model of an emergency department (ED) based on the publicly available data from the large 1000-bed Rambam Hospital in Haifa, Israel, from 2004–7, associated with the patient flow analysis by Armony et al. (2015). Here we focus on forecasting future daily arrival totals and predicting hourly occupancy levels in real time, given recent history (previous arrival and departure times of all patients) and useful exogenous variables. For the arrival forecasting, we divide the dataset into an initial training set for fitting the models and a final test set to evaluate the performance. By using 200 weeks of data instead of the previous 25, we identify (i) long-term trends in both the arrival process and the length-of-stay distributions and (ii) dependence among successive daily arrival totals, which were undetectable before. From several forecasting methods, including artificial neural network models, we find that a seasonal autoregressive integrated moving average with exogenous (holiday and temperature) regressors (SARIMAX) time-series model is most effective. We then combine our previous ED model with the arrival prediction to create a real-time predictor for the future ED occupancy levels.

This study focused on increasing productivity and efficiency in a Cancer Clinic (CC) taking into consideration mental workload. The demand of the clinic has increased and the clinic recognized the importance of improving the distribution of the resources. Addressing these objectives have a positive impact in operations, however, it also requires managing the human elements of the system in an efficient way. Previous studies have considered human resources as a number representing a fix quantity of available entities without considering their mental capabilities. This research measured mental workload using a perceptual tool, NASA-TLX, as well as physiological responses. The purpose was to balance patient appointments and increase resource utilization while taking into consideration the balance of human workload as a constraint in the mathematical model. Mental workload was included to assure a balance in the capacity of the human resources without overloading them. The mathematical model was able to successfully build a patient scheduling model considering nurses’ workload. It was shown that the model balanced patient appointments throughout the day by leveling the workload of nurses. Sensitivity analysis showed that the patient demand of the center could be increased by up to 50% without negatively impacting patient service.

In a hospital, surgical supplies can be stored in multiple locations, each of which has limited space and different associated costs. The locations include central storage, where items are retrieved to build a cart of supplies for each procedure; sterile storage adjacent to the operating rooms; and within the operating rooms themselves. In practice, the decision on allocating items to these locations is often based on the staff’s experience, rather than through optimization methods. In this research, we have identified the costs associated with each location to determine where each item should be stored and in what quantities. These costs include the cost of building the case cart, the cost of returning unused items to storage, the cost of picking items during a procedure, the cost of restocking and the cost of reviewing items to determine what needs to be replenished. Since the number of supplies required to perform a procedure is uncertain, we have developed a robust stochastic mixed-integer programming model to make the inventory allocation decision. The model also enables a hospital to assess the potential cost saving from optimization of the preference cards, which are used by surgeons to specify the requested supplies available on the case carts. The performance of the proposed model is evaluated through a case study. Three alternatives to the current configuration of the system are presented and reduction of inventory expenditure within each alternative is discussed. Finally, sensitivity analyses are performed to determine which cost parameters contribute to the model more significantly and how the model behaves against different levels of risk coefficient.

Uncertain surgery durations in Operating Rooms (OR) can cause a large deviation from the expected completion time of all surgery cases scheduled for each day. When the deviation is significantly large, it causes an extended overtime for the surgical team to complete the scheduled cases, and it often creates unnecessarily excessive idle times. As a result, the hospital will lose revenue opportunities. To address this issue, this paper presents a risk-based solution approach using the concept of Conditional Value-at-Risk (CVaR) to reduce variability on overtime, idle time, and associated costs in a daily OR scheduling problem. The OR scheduling problem is formulated as a stochastic mixed-integer linear programming (SMILP) model, where a surgery duration follows a probability distribution function. The objective of the SMILP model is to minimize the CVaR of overtime and idle time costs. Numerical experiments are conducted on real-life benchmark instances, and showed that CVaR outperformed the widely used expected value (EV) approach in reducing variance of the total cost. As compared to the EV in terms of the total cost, the CVaR reduced the variance by 37%, produced a 25% lower interquartile range, and 24% lower median absolute deviation at a slight increase (4%) in the expected value.

Continuity of care for patients, that is ensuring patients are treated by a single physician, is one of the most important concerns for hospital management in regards to general medicine (inpatient) departments. Discontinuous care occurs when the number of patients various physicians are caring for becomes imbalanced and patients are transferred between physicians to correct this. This issue can be addressed by constructing rosters for the physicians which aim to balance their patient workloads and thereby improve the continuity of care that patients receive. A mixed integer programme, which uses admission information coupled with a model of the patient pathways, is formulated to generate cyclic rosters for general medicine physicians. The capabilities of the model are demonstrated by applying it to a New Zealand hospital. A solution technique is also proposed and numerical experiments performed on the demonstration instance.

Kidney paired donation is a partial solution to overcoming biological incompatibility preventing kidney transplants. A kidney paired donation (KPD) program consists of altruistic or non-directed donors (NDDs) and pairs, each of which comprises a candidate in need of a kidney transplant and her/his willing but incompatible donor. Potential transplants from NDDs or donors in pairs to compatible candidates in other pairs are determined by computer assessment, though various situations involving either the donor, candidate, or proposed transplant may lead to a potential transplant failing to proceed. A KPD program can be viewed as a directed graph with NDDs and pairs as vertices and potential transplants as edges, where failure probabilities are associated with each vertex and edge. Transplants are carried out in the form of directed cycles among pairs and directed paths initiated by NDDs, which we refer to respectively as cycles and chains. Previous research shows that selecting disjoint subgraphs with a view to creating fallback options when failures occur generates more realized transplants than optimal selection of disjoint chains and cycles. In this paper, we define such subgraphs, which are called locally relevant (LR) subgraphs, and present an efficient algorithm to enumerate all LR subgraphs. Its computational efficiency is significantly better than the previous, more restrictive, algorithms.

This paper proposes a new mixed-integer linear programming model to build cyclic master surgery schedules (MSSs) for a case study of a medium-sized Portuguese private hospital. The problem integrates tactical and strategical decisions of operating room (OR) planning and scheduling. OR time blocks are assigned to surgical services and to individual surgeons. A target OR time per surgical specialty is not given as it is often the case of other studies in the literature. The model aims to: level the workload at downstream departments (hospitalization units); avoid sharing OR time among different surgical specialties; allocate OR time blocks to the surgical specialty with the highest number of surgeons available; renew the MSS based on recent demand for surgeries. This approach allows the surgical suite to be more efficiently managed, while increasing the sense of fairness among surgeons and facilitating the negotiation for OR time. Moreover, this automated system releases the surgical suite manager to more added value tasks.

This study introduces a new methodology for calling blood donors by integrating perishability and dual sourcing. It is shown that the proposed donor-arrival policy could replace the current procedure, which requires frequent adjustments to the number of calls. We show that both donor-arrival (current and proposed) policies could decrease shortages and wastages of blood, while only the proposed policy allows a longer lead-time for arrivals and planning operations, and could significantly increase the blood center’s resilience to errors made in forecasting. The proposed policy is also shown to be more effective than existing blood substitution policies in reducing wastages and shortages (with nearly zero shortages for any given admissible safety inventory). The proposed policy is designed and validated using real-life data from a large blood center in New York State.

Diagnostic imaging services are essential to the diagnosis pathway for many patients arriving at hospital emergency departments with a suspected fracture. Commonly, these patients need to be seen again by a doctor or emergency nurse practitioner after an X-ray image has been taken in order to finalise the diagnosis and determine the next stage in the patients’ pathway. Here, significant waiting times can accrue for these follow-up consultations after radiographic imaging although the vast majority of patients are discharged. Research evidence from pilot studies suggests that patients with minor appendicular injuries could be safely discharged by a suitably qualified radiographer directly after imaging thereby avoiding queues for repeated consultation. In this study, we model patient pathways through an emergency department (ED) at a hospital in the South West of England using process mapping, interviews with ED staff and discrete event simulation (DES). The DES model allowed us to compare the current practice at the hospital with scenarios using radiographer-led discharge of patients directly after imaging and assess the reduction in patients’ length of stay in ED. We also quantified trade-offs between the provision of radiographer-led discharge and its effects, i.e. reduction in waiting times and ED workload. Finally, we discuss how this decision support tool can be used to support understanding for patients and members of staff.