Operations Research for Health Care最新文献

Beds are a critical resource for hospitals, requiring effective management to ensure the quality of care for patients. Beds operate in a closed-loop circuit and must be thoroughly cleaned between patients’ arrivals to prevent infections. Hospitals must implement efficient logistics systems to collect, transport, store, and clean unclean beds from discharged patients. These systems must be robust and efficient to meet the varying bed supply needs, given the available resources such as beds, staff and machines. This study aims to develop a decision support tool to optimise bed cleaning logistics and ensure the availability of sterile beds for incoming patients at all times. The study is based on the bed flow and cleaning organisation of a Danish public hospital. A discrete event simulation model (DES) of the back-end bed flow has been developed. The paper also presents a tension level indicator to reflect the behaviour of cleaning staff when facing variations in demand and bed stock. Using the organisational set-up (staff schedules, policies, and bed fleet size), the DES model: (1) evaluates the ability to provide sterile beds in a reasonable time, (2) measures the stress on cleaning staff, and (3) visualises resource usage. This study illustrates how to incorporate the staff’s perceived workload and resulting behaviour into a DES model to capture the behavioural aspect of staff’s decision-making.

Planning the use of chemotherapy drugs in outpatient treatment is a complex problem due to the variability of cancer, resulting in different chemotherapy protocols. This process involves factors such as the cyclical nature of treatment protocols and clinical resources. Within this context, an optimization model is needed to plan the use of chemotherapy drugs in the preparation of doses for patient treatment, considering the operational particularities of treatment centers. This study proposed a linear programming model based on the multiple knapsack problem, in order to optimize the combination of different vials of chemotherapy drugs, minimizing the total cost of treatment. The model, based on the daily schedule of patients, provides the best combination of drug vials and supports the preparation process of each dose prescribed by the doctor for each patient, respecting the treatment protocol and resource limitations. The model was implemented in the CPLEX 12.9.0 application, and the computational tests were performed with real data. The results demonstrated that the costs when applying the model were 5% lower when compared to the current manner in which the oncology pharmacy combines the drugs vials.

As pressure on healthcare systems continues to increase, it is becoming more and more important for hospitals to properly manage the high workload levels of their staff. Ensuring a balanced workload allocation between various groups of employees in a hospital has been shown to contribute considerably towards creating sustainable working conditions. However, allocating work to different organizational units in a fair manner is not straightforward when it involves complex decision-making processes. In this paper we set out to balance the workload of heterogeneous hospital wards by optimizing the patient admission scheduling problem. Given the multi-period nature of patient admission scheduling, we introduce a new equity objective that captures both spatial (between hospital wards) and temporal (between days in the planning period) workload balancing. The resulting bi-objective problem is solved using an exact criterion space search algorithm. Our computational study employs problem instances that have been generated based on real-world data. The results demonstrate how spatially and temporally balanced workload allocations can be generated by minimizing the proposed equity objective. Moreover, we analyze sets of nondominated solutions to gain various insights into the trade-off between schedule cost and workload balance.

Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers through better management of intensive care units. In particular, it is important that intensive care unit patient discharge decisions account for the nuanced trade-off between decreasing the length of stay and the risk of readmission or death after discharge of a patient. This work introduces a comprehensive framework (i.e., not geared towards any particular disease or condition) for capturing this trade-off and to recommend optimal discharge timing decisions given the electronic health records of a patient. A data-driven approach is used to derive a parsimonious, discrete state space representation to represent the physiological condition of a given patient. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose performance is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.

The nurse scheduling problem (NSP) has become significant in recent years due to its direct impact on patient healthcare. This problem involves assigning nurses’ shifts while fulfilling a set of hard constraints associated with labor regulations and soft constraints related to personal preferences, workload balance, among others. Most studies have focused on providing solutions for deterministic scenarios without considering unexpected disruptions, such as an unscheduled nurse absence. This study integrates two of the most common approaches to address absenteeism: preventive and reactive. First, we propose a multiobjective linear model for staff scheduling that preventively assigns backup nurses for each day. The NSP is known to be an NP-hard problem. Therefore, we used a genetic algorithm to obtain solutions in a reasonable amount of time. To mitigate the effect of unscheduled nurse absences, we propose two reactive rescheduling policies, one that seeks to maintain the baseline schedule and another that prioritizes the exclusive use of backup nurses. We used Montecarlo simulation under different problem settings to compare the proposed policies with a policy that does not use the preventive approach. The probability that a nurse will accept an additional shift to cover an absence was also considered. Simulation results suggest that both of our preventive–reactive policies outperform the non-preventive policy, especially in the presence of a small probability that a nurse will accept an additional shift. Finally, we used the proposed policies to create the monthly nursing schedule in a reference hospital in Bogotá-Colombia.

The objective of this paper is to minimize the total cost of vaccine storage and distribution operations at centralized distribution centers (DCs) and at clinics so that clinics are provided with vaccines in a timely fashion while under resource and environment-protection constraints. A non-linear mathematical programming model is developed to improve the efficiency of large-scale influenza vaccination programs. The suggested model is tested and justified through computational experiments with real-life data from a Clalit HMO influenza vaccination case study. The investments in green (environment-protecting) activities recommended by the optimal plan are smaller than the expected monetary benefits associated with their effects. A possible application of this research is for optimizing vaccination plans for different subpopulations and various HMOs. Our vaccine supply chain model includes the costs of disposal, recycling, and utilizing clean technologies (i.e., low-pollution gas heating/cooling, electric transportation cars, energy saving policies). It integrates the operational cost/benefit parameters of vaccination programs with the costs/benefits of green activities.

To construct good quality plans or planning systems in hospitals, such as capacity planning, case mix planning, master surgery scheduling, and shift scheduling, knowing details about the duration of surgeries is paramount. Furthermore, the operating room is one of a hospital’s main cost drivers, thus making surgery duration a key to achieving cost effectiveness. To gain a better understanding of the interdependencies of determining surgery durations, we investigate the influence physicians have on the duration of a surgery. Since physician experience is a very generalizable factor across a heterogeneous group of hospitals, it is the most obvious influencing factor to analyze. Accordingly, we utilize information regarding a physician’s level of experience and examine its impact on surgery durations using data from a German hospital. Although we are forced to use aggregate data for privacy and labor law reasons, a combination of linear and quantile regression analysis allows us to derive several important insights. First, on average, an increase in a physician’s experience leads to a decrease in the duration of a surgery. Second, the effect of the first insight depends on the composition of the surgical team and diminishes in the case of teaching activities. Third, the relationship between experience level and surgery duration varies across the distribution of durations, i.e., the relationship is strongest for short surgeries and weakens as the duration of a surgery increases.

Nearly 20% of patients are readmitted to hospitals within a specific time period after hospital discharge. High readmission rates place an unnecessary burden on the healthcare system, and new initiatives to reduce preventable hospital readmissions have been established. The United States Hospital Readmission Reduction Program (HRRP) is an example of a health policy reform that links insurance payments to quality of care. Critics of HRRP believe that its punitive mechanism design provides less money to struggling hospitals and, in some cases, fails to provide proper incentives and resources for quality care improvements. An asymmetric penalty-incentive model for hospital readmission reductions was developed and studied for an insurer-led reimbursement system. We formulate a game-theoretic setting involving an insurer and a hospital. We derive the insurer’s optimal policy design and the hospital’s best response in an insurer-led Stackelberg setting with rational agents. The model was analyzed for centralized and decentralized solutions and compared to the do-nothing solution. Most notably, we found that a positive incentive level is necessary for a win-win region to exist. An example from public hospital data for acute myocardial infarction showed that transitioning from the current 3% penalty-only policy to the optimal 5.47% incentive-only policy would result in only a 0.17% increase in insurer costs while inspiring hospitals to maximize level of care and increase hospital profits by 39.7%.

Designing high quality nurse rostering plans is essential for health care facilities in order to guarantee efficiency, safety and quality-of-care balanced with staff well-being.

We introduce a new real-world formulation for the nurse rostering problem, arising in many Italian healthcare institutions, which has been developed in collaboration with a primary software company in the field. It considers nurses with different skills, special shifts depending on the skills, time work-load limits, and different types of days-off. In addition, preferences and incompatibilities between nurses are taken into account.

We propose a MIP model and a local search method, driven by a Simulated Annealing metaheuristic, based on a combination of two neighborhoods. The solution method was tested on 34 real-world instances coming from various healthcare institutions in North Italy. The dataset is available at https://bitbucket.org/satt/nrp-instances, along with our best solutions.