Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

A multilevel integrative approach to hospital case mix and capacity planning.

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.Health care; Case mix and capacity planning; Master surgery schedule; Multilevel; Resource efficiency; Service level;

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

Heuristiken im Service Operations Management

This doctoral thesis deals with the application of operation research methods in practice. With two cooperation companies from the service sector (retailing and healthcare), three practice-relevant decision problems are jointly elicited and defined. Subsequently, the planning problems are transferred into mathematical problems and solved with the help of optimal and/or heuristic methods. The status quo of the companies could be significantly improved for all the problems dealt with.Diese Doktorarbeit beschäftigt sich mit der Anwendung von Operation Research Methoden in der Praxis. Mit zwei Kooperationsunternehmen aus dem Dienstleistungssektor (Einzelhandel und Gesundheitswesen) werden drei praxisrelevante Planungsprobleme gemeinsam eruiert und definiert. In weiterer Folge werden die Entscheidungsmodelle in mathematische Probleme transferiert und mit Hilfe von optimalen und/oder heuristischen Verfahren gelöst. Bei allen behandelten Problemstellungen konnte der bei den Unternehmen angetroffene Status Quo signifikant verbessert werden

Operating room planning and scheduling: A literature review.

This paper provides a review of recent research on operating room planning and scheduling. We evaluate the literature on multiple fields that are related to either the problem setting (e.g. performance measures or patient classes) or the technical features (e.g. solution technique or uncertainty incorporation). Since papers are pooled and evaluated in various ways, a diversified and detailed overview is obtained that facilitates the identification of manuscripts related to the reader's specific interests. Throughout the literature review, we summarize the significant trends in research on operating room planning and scheduling and we identify areas that need to be addressed in the future.Health care; Operating room; Scheduling; Planning; Literature review;

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Dynamic Resource Allocation For Coordination Of Inpatient Operations In Hospitals

Healthcare systems face difficult challenges such as increasing complexity of processes, inefficient utilization of resources, high pressure to enhance the quality of care and services, and the need to balance and coordinate the staff workload. Therefore, the need for effective and efficient processes of delivering healthcare services increases. Data-driven approaches, including operations research and predictive modeling, can help overcome these challenges and improve the performance of health systems in terms of quality, cost, patient health outcomes and satisfaction. Hospitals are a key component of healthcare systems with many scarce resources such as caregivers (nurses, physicians) and expensive facilities/equipment. Most hospital systems in the developed world have employed some form of an Electronic Health Record (EHR) system in recent years to improve information flow, health outcomes, and reduce costs. While EHR systems form a critical data backbone, there is a need for platforms that can allow coordinated orchestration of the relatively complex healthcare operations. Information available in EHR systems can play a significant role in providing better operational coordination between different departments/services in the hospital through optimized task/resource allocation. In this research, we propose a dynamic real-time coordination framework for resource and task assignment to improve patient flow and resource utilization across the emergency department (ED) and inpatient unit (IU) network within hospitals. The scope of patient flow coordination includes ED, IUs, environmental services responsible for room/bed cleaning/turnaround, and patient transport services. EDs across the U.S. routinely suffer from extended patient waiting times during admission from the ED to the hospital\u27s inpatient units, also known as ED patient `boarding\u27. This ED patient boarding not only compromises patient health outcomes but also blocks access to ED care for new patients from increased bed occupancy. There are also significant cost implications as well as increased stress and hazards to staff. We carry out this research with the goal of enabling two different modes of coordination implementation across the ED-to-IU network to reduce ED patient boarding: Reactive and Proactive. The proposed `reactive\u27 coordination approach is relatively easy to implement in the presence of modern EHR and hospital IT management systems for it relies only on real-time information readily available in most hospitals. This approach focuses on managing the flow of patients at the end of their ED care and being admitted to specific inpatient units. We developed a deterministic dynamic real-time coordination model for resource and task assignment across the ED-to-IU network using mixed-integer programming. The proposed \u27proactive\u27 coordination approach relies on the power of predictive analytics that anticipate ED patient admissions into the hospital as they are still undergoing ED care. The proactive approach potentially allows additional lead-time for coordinating downstream resources, however, it requires the ability to accurately predict ED patient admissions, target IU for admission, as well as the remaining length-of-stay (care) within the ED. Numerous other studies have demonstrated that modern EHR systems combined with advances in data mining and machine learning methods can indeed facilitate such predictions, with reasonable accuracy. The proposed proactive coordination optimization model extends the reactive deterministic MIP model to account for uncertainties associated with ED patient admission predictions, leading to an effective and efficient proactive stochastic MIP model. Both the reactive and proactive coordination methods have been developed to account for numerous real-world operational requirements (e.g., rolling planning horizon, event-based optimization and task assignments, schedule stability management, patient overflow management, gender matching requirements for IU rooms with double occupancy, patient isolation requirements, equity in staff utilization and equity in reducing ED patient waiting times) and computational efficiency (e.g., through model decomposition and efficient construction of scenarios for proactive coordination). We demonstrate the effectiveness of the proposed models using data from a leading healthcare facility in SE-Michigan, U.S. Results suggest that even the highly practical optimization enabled reactive coordination can lead to dramatic reduction in ED patient boarding times. Results also suggest that signification additional reductions in patient boarding are possible through the proposed proactive approach in the presence of reliable analytics models for prediction ED patient admissions and remaining ED length-of-stay. Future research can focus on further extending the scope of coordination to include admissions management (including any necessary approvals from insurance), coordination needs for admissions that stem from outside the ED (e.g., elective surgeries), as well as ambulance diversions to manage patient flows across the region and hospital networks

Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management

Incorporation of contextual inference in the optimality analysis of operational problems is a canonical characteristic of data-informed decision making that requires interdisciplinary research. In an attempt to achieve individualization in operations management, we design rigorous and yet practical mechanisms that boost efficiency, restrain uncertainty and elevate real-time decision making through integration of ideas from machine learning and operations research literature. In our first study, we investigate the decision of whether to admit a patient to a critical care unit which is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient’s individual health metrics can be incorporated while considering the hospital’s operational constraints. We model the problem as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A data-driven optimization methodology then approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are also examined. In the second study, we analyze the efficiency of surgical unit operations in the era of big data. The accuracy of surgical case duration predictions is a crucial element in hospital operational performance. We propose a comprehensive methodology that incorporates both structured and unstructured data to generate individualized predictions regarding the overall distribution of surgery durations. Consequently, we investigate methods to incorporate such individualized predictions into operational decision-making. We introduce novel prescriptive models to address optimization under uncertainty in the fundamental surgery appointment scheduling problem by utilizing the multi-dimensional data features available prior to the surgery. Electronic medical records systems provide detailed patient features that enable the prediction of individualized case time distributions; however, existing approaches in this context usually employ only limited, aggregate information, and do not take advantages of these detailed features. We show how the quantile regression forest, can be integrated into three common optimization formulations that capture the stochasticity in addressing this problem, including stochastic optimization, robust optimization and distributionally robust optimization. In the last part of this dissertation, we provide the first study on online learning problems under stochastic constraints that are "soft", i.e., need to be satisfied with high likelihood. Under a Bayesian framework, we propose and analyze a scheme that provides statistical feasibility guarantees throughout the learning horizon, by using posterior Monte Carlo samples to form sampled constraints that generalize the scenario generation approach commonly used in chance-constrained programming. We demonstrate how our scheme can be integrated into Thompson sampling and illustrate it with an application in online advertisement.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145936/1/meisami_1.pd

Large-Scale Solution Approaches for Healthcare and Supply Chain Scheduling

This research proposes novel solution techniques for two real world problems. We first consider a patient scheduling problem in a proton therapy facility with deterministic patient arrivals. In order to assess the impacts of several operational constraints, we propose single and multi-criteria linear programming models. In addition, we ensure that the strategic patient mix restrictions predetermined by the decision makers are also enforced within the planning horizon. We study the mathematical structures of the single criteria model with strict patient mix restrictions and derive analytical equations for the optimal solutions under several operational restrictions. These efforts lead to a set of rule of thumbs that can be utilized to assess the impacts of several input parameters and patient mix levels on the capacity utilization without solving optimization problems. The necessary and sufficient conditions to analytically generate exact efficient frontiers of the bicriteria problem without any additional side constraint are also explored. In a follow up study, we investigate the solution techniques for the same patient scheduling problem with stochastic patient arrivals. We propose two Markov Decision Process (MDP) models that are capable of tackling the stochasticity. The second problem of interest is a variant of the parallel machine scheduling problem. We propose constraint programming (CP) and logic-based Benders decomposition algorithms in order to make the best decisions for scheduling nonidentical jobs with time windows and sequence dependent setup times on dissimilar parallel machines in a fixed planning horizon. This problem is formulated with (i) maximizing total profit and (ii) minimizing makespan objectives. We conduct several sensitivity analysis to test the quality and robustness of the solutions on a real life case study