Operations Research for Health Care最新文献

The planning of radiotherapy treatments is based on the use of mathematical optimization models and algorithms. A treatment plan will correspond to an admissible solution to a problem that is defined by the medical prescription. The medical prescription defines the constraints that have to be satisfied, and that are patient dependent. To find a treatment plan complying with all the defined constraints, an objective function is built, having a set of parameters that can be tuned. In the clinical practice, the objective function parameters are tuned through a trial-and-error procedure. One common way of defining this objective function is to consider as parameters weights that are related with the importance of the corresponding structures of interest (volumes to treat and organs to spare), as well as upper and lower bounds that are related with the constraints defined by the medical prescription. Some treatment options, like the set of irradiation directions (beam angles), are usually defined beforehand by the planner, considering previous experiences with similar cases. This trial-and-error process is lengthy, since it can take up to several hours to calculate an admissible treatment plan for a single patient. There have been some research efforts to automate the treatment planning procedure, releasing the planner for other important tasks in the radiotherapy treatment workflow and guaranteeing the consistent calculation of high-quality plans. In this work derivative-free optimization algorithms are integrated with fuzzy inference systems that automatically tune the objective function parameters so that an admissible solution is found. This fully automated approach was tested and assessed considering six head-and-neck cancer cases already treated at the Portuguese Institute of Oncology at Coimbra (IPOC). For the clinical cases tested, comparisons between different treatment plans clearly favor the proposed approach. For a similar tumor coverage, it was possible to improve the sparing of the spinal cord, brainstem and parotids. Automating radiotherapy treatment planning can contribute to improved treatment plans in a consistent way.

Growing healthcare needs leverage the potential savings of using resources efficiently. To that end, ProMoBed is a comprehensive model that supports strategic planning of bed capacity in inpatient hospitals. The model consists of an extrapolation and simulation component, the former supplying input for the latter. The extrapolation model forecasts admission rates and the average Length of Stay for pathology groups, and corrects for demographic changes. Subsequently, the simulation model emulates the demand for bed capacity, and makes service-level based bed capacity suggestions. Additionally, the model uses the Shapley value principle to disaggregate the effects on demand for inpatient days due to different causes. Results from the extrapolation model are applied to regions in Belgium, showing expected divergence in inpatient day demand evolution.

In this paper, we study an integrated master surgery and outpatient clinic scheduling problem, motivated by the situation at the Orthopaedic Department at St. Olav’s Hospital, Trondheim. During a treatment process, the patients require one or several consultations at the outpatient clinic, and potentially a surgery in one of the operating rooms. The physicians perform both consultations and surgeries, and coordinating the two facilities is challenging. The surgeons are trained to handle different surgical specialties, and they differ in experience. The overall goal is to schedule the specialties, and a number of qualified surgeons, to time slots in the outpatient clinic and operating rooms through the week, to efficiently handle the patient demand. Our main contribution is an optimisation model for solving the integrated master surgery and outpatient clinic scheduling problem. In addition to allocating specialties and a number of surgeons, the model also schedules activity types (surgery categories and outpatient clinic consultation types) to the time slots. These can guide the operational scheduling of individual patients at a later stage. A computational study is performed, demonstrating the use of the optimisation model to provide a set of master schedules, based on a set of different resource capacity cases. We develop a simulation model for evaluating the master schedules in an operational setting, and three different operational scheduling policies are compared. We conclude that scheduling patients to activities governed primarily by the optimisation model solution outperforms a FIFO scheduling policy based only on specialty.

Improving operating room efficiency has become one of the most important goals for health care providers given growing expenditures in managing hospital operations. Effective appointment schedules, which minimize the cost of patient waiting time and operating room idle time and overtime, play an important role in terms of improving efficiency. The variability inherent in surgical procedure times and random arrival of urgent cases significantly complicates the scheduling process. In this study, we compare several strategies for scheduling outpatient surgical procedures in the presence of urgent arrivals. First, a simulation optimization approach is used to find heuristic solutions for a surgery scheduling problem with a heterogeneous set of elective and urgent procedures in a multiple operating room setting. Second, a discrete-event simulation model is used to numerically evaluate how different sequencing and allocation policies perform for multiple surgery procedure types. Historical surgery procedure data for a hospital providing day surgeries over a two-year period from the Canadian Institute for Health Information (CIHI) database will be used to provide empirical support for the input parameters of the model.

The COVID-19 pandemic had a major impact on healthcare systems across the world. In the United Kingdom, one of the strategies used by hospitals to cope with the surge in patients infected with SARS-Cov-2 was to cancel a vast number of elective treatments planned and limit its resources for non-critical patients. This resulted in a 30% drop in the number of people joining the waiting list in 2020–2021 versus 2019–2020. Once the pandemic subsides and resources are freed for elective treatment, the expectation is that the patients failing to receive treatment throughout the pandemic would trigger a significant backlog on the waiting list post-pandemic with major repercussions to patient health and quality of life. As the nation emerges from the worst phase of the pandemic, hospitals are focusing on strategies to prioritise patients for elective treatments. A key challenge in this context is the ability to quantify the expected backlog and predict the delays experienced by patients as an outcome of the prioritisation policies. This study presents an approach based on discrete-event simulation to predict the elective waiting list backlog along with the delay in treatment based on a predetermined prioritisation policy. The model is demonstrated using data on the endoscopy waiting list at Cambridge University Hospitals. The model shows that 21% of the patients on the waiting list will experience a delay less than 18-weeks, the acceptable threshold set by the National Health Service (NHS). A longer-term scenario analysis based on the model reveals investment in NHS resources will have a significant positive outcome for addressing the waiting lists. The model presented in this paper has the potential to be an invaluable tool for post-pandemic planning for hospitals around the world that are facing a crisis of treatment backlog.

In this paper, length of stay (LOS) competition between two servers in tandem without buffer between them is investigated using queuing games. This system typifies the relationship between the intensive care unit (ICU) and the step-down unit (SDU) of a hospital. We model and analyze the equilibrium LOS decision under four different games (one cooperative and three non-cooperative games) as follows: (i) both servers cooperate; (ii) the servers do not cooperate and make decisions simultaneously; (iii) the servers do not cooperate and the first server, the ICU, is the leader (ICU Stackelberg); (iv) the servers do not cooperate and the second server, the SDU, is the leader (SDU Stackelberg). The payoff of the ICU is expressed as the difference between the service benefit and the waiting in queue penalty, while that of the SDU is the difference between the service benefit and the overstay penalty. The results show that LOS decisions of each server depends critically on the payoff function’s form and the exogenous demand. Secondly, with a linear payoff function, the SDU is only beneficial to the system if the unit cost is greater than the unit reward at the ICU. Our results revealed also that payoffs depend on the substitutability in both ICU Stackelberg and SDU Stackelberg games. When most of the LOS is spent at the ICU unit. Our results suggest that the critical care pathway performs better under coordination and or leadership at the ICU level.

Background:

Discrete event simulation has been widely used for decades to model the Emergency Department (ED) patient flow, understand system bottlenecks, and analyse resource capacity planning constraints. Given the complexity and constraints related to modelling the ED setting, such as data availability, there is often a gap in prescribing the correct validation approach.

Objectives:

The purpose of this review study is to come up with practical guidelines for employing specific validation techniques by comparing the available “best practice” simulation validation approaches against the approaches commonly found in published ED Discrete Event Simulation (DES) studies.

Methods:

We conducted a systematic review of the peer-reviewed literature to identify DES studies of patient flow within hospital EDs across the globe. Our search strategy focused on two main domains of knowledge associated with the current study: DES and ED. In total, we selected 90 studies a basis for the analysis. Additionally, we have identified a total of 7 papers focused on best practice approaches in validation.

Results:

A plurality of studies only discuss a single type of validation (data-led) at 30%, closely followed by none (23%), and data-led paired with qualitative validation, but no verification (at 22%). LOS is the most common validation metric, with 47% of studies that used data-led validation selecting length of stay (LOS) as the key validation metric. The next most frequently validated metric is throughput (9%), followed by time to triage (TTT) (8%). “Confidence Interval” and “% Difference” are by far the most common, with 32% of studies employing the former, and 22% employing the latter. Remaining techniques tend to be used more sporadically, with hypothesis testing, correlation coefficient, Student t-test, and Welch’s two-sample t-test being the most frequent (5% to 9% of studies).

Conclusion:

Based on the reviewed studies, we propose guidelines for the validation procedure given five “levels” of available data quality. The guideline incorporates both the analysis of best practice literature, as well as the trends for validation based on the review of 90 generic and specific simulation studies.

We develop a novel online real-time scheduling algorithm with applications for healthcare diagnostic centers to deal with walk-in patients based on a set of constraints on the sequence of tests and resources. The problem is especially significant at healthcare centers in developing and emerging nations, such as India, where appointment schedules do not work. Within this realistic context, our objective is to improve patient satisfaction by reducing waiting time and improve diagnostic center performance through better utilization of the constrained resources. We propose a Mixed Integer Linear Programming (MILP) formulation to represent diagnostic centers as a Flow and Open Shop, to capture the system dynamics of the Flexible Hybrid Shop Scheduling Problem. We then develop a novel Online Genetic Algorithm (OGA) capable of solving real life large scale problems, as Open Shop scheduling problems are NP-hard. The developed OGA is first validated for small instances against a theoretical lower bound and the MILP model using CPLEX solver for flow time and makespan. The OGA is then empirically validated with data collected from two diagnostic centers of different sizes and configurations. For both centers, the developed OGA shows significant improvement compared to the simulation model. This research offers an important contribution to both literature and practice as it is one of the first to model the patient scheduling problem as an online real-time process. Implementing the developed OGA would help diagnostic centers significantly improve time estimates, thus reducing actual patient time and improving the efficiency of the system. Most importantly, the OGA is generalizable beyond healthcare to a broad range of environments that share Hybrid Shop characteristics.