93 research outputs found
An Integrated Framework for Staffing and Shift Scheduling in Hospitals
Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation
Incorporating declared capacity uncertainty in optimizing airport slot allocation
Slot allocation is the mechanism used to allocate capacity at congested airports. A number of models have been introduced in the literature aiming to produce airport schedules that optimize the allocation of slot requests to the available airport capacity. A critical parameter affecting the outcome of the slot allocation process is the airport’s declared capacity. Existing airport slot allocation models treat declared capacity as an exogenously defined deterministic parameter. In this presentation we propose a new robust optimization formulation based on the concept of stability radius. The proposed formulation considers endogenously the airport’s declared capacity and expresses it as a function of its throughput. We present results from the application of the proposed approach to a congested airport and we discuss the trade-off between the declared capacity of the airport and the efficiency of the slot allocation process
Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches
Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment
Modeling and Analysis of Value-Based Healthcare Delivery
Healthcare reforms are emerging in order to control the increasing healthcare expenditures and to improve the health outcomes. In the context of the Value-based Healthcare Delivery reform, Michael Porter defines value as a patient\u27s health outcome per dollar spent. Porter\u27s proposal is comprised of organizing care around a medical condition (or around patient segments for primary care). Specifically, care will be provided by a dedicated, multidisciplinary team of providers, an Integrated Practice Unit (IPU). The IPU is jointly accountable for the health outcomes of patients and the costs of providing care during the full cycle of care.
The main objective of this dissertation is to use analytics to determine enabling factors for the successful implementation of the value-based healthcare delivery reform. This dissertation consists of three core chapters.
Chapter 2 draws insights on the effects of current payment schemes, including fee-for-service, capitation, and pay-for-performance, in fulfilling the objectives of value-based healthcare delivery. Particularly, a mathematical representation of healthcare delivery is proposed to assess if any of the existing payment systems can incentivize providers to improve the quality and integrate the care simultaneously. The results provide insights on strengths, shortcomings, and applicability of each payment system in fulfilling value-based healthcare delivery objectives.
Chapter 3 determines the optimal payment system between the healthcare purchaser and the IPU. The current payment systems do not pay for health outcomes. Most importantly, they do not consider health outcomes over the care cycle and fail to provide dynamic incentives for the providers. This study investigates the contract that can coordinate the healthcare purchaser-IPU relationship over the care cycle.
Chapter 4 studies the effects of different contractual arrangements on collaboration dynamics among the providers involved in an IPU. A mathematical representation that characterizes the relationship between the providers throughout the care cycle is proposed. When efforts are not contractible, the contractual agreement will determine the dynamics of the collaboration. Aside from characterizing the first-best solution, the effects of reward-sharing and relational contracts, together with traditional schemes, such as capitation, are studied in this chapter.
The results of this dissertation shed light on the enablers of the value-based healthcare delivery reform. This dissertation is the first to design a dynamic incentives contract between the healthcare purchaser and the IPU, who is accountable for the health outcomes of a patient over the care cycle. The optimal contract can coordinate the objectives of the purchaser and the IPU and maximize social welfare. In addition, this is the first study to characterize the collaboration dynamics among the IPU members under different contractual agreements. The insights from this study can strengthen the work relationship of the providers within an IPU
Optimization for Decision Making II
In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner
Risk Management for the Future
A large part of academic literature, business literature as well as practices in real life are resting on the assumption that uncertainty and risk does not exist. We all know that this is not true, yet, a whole variety of methods, tools and practices are not attuned to the fact that the future is uncertain and that risks are all around us. However, despite risk management entering the agenda some decades ago, it has introduced risks on its own as illustrated by the financial crisis. Here is a book that goes beyond risk management as it is today and tries to discuss what needs to be improved further. The book also offers some cases
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