Integration of Simulation and DEA to Determine the Most Efficient Patient Appointment Scheduling Model for a Specific Clinic Setting

This study develops a method to determine the most efficient scheduling model for a specific clinic setting. The appointment scheduling system assigns clinics' timeslots to incoming requests. There are three major scheduling models: centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM). In order to schedule multiple appointments, CSM involves one scheduler, DSM involves all the schedulers of individual clinics and HSM combines CSM and DSM. Clinic settings are different in terms of important factors such as randomness of appointment arrival and proportion of multiple appointments. Scheduling systems operate inefficiently if there is not an appropriate match between scheduling models and clinic settings to provide balance between indicators of efficiency. A procedure is developed to determine the most efficient scheduling model by the integrated contribution of simulation and Data Envelopment Analysis (DEA). A case study serves as a guide to use and as proof for the validity of the developed procedure

A Computational Approach to Patient Flow Logistics in Hospitals

Scheduling decisions in hospitals are often taken in a decentralized way. This means that different specialized hospital units decide autonomously on e.g. patient admissions and schedules of shared resources. Decision support in such a setting requires methods and techniques that are different from the majority of existing literature in which centralized models are assumed. The design and analysis of such methods and techniques is the focus of this thesis. Specifically, we develop computational models to provide dynamic decision support for hospital resource management, the prediction of future resource occupancy and the application thereof. Hospital resource management targets the efficient deployment of resources like operating rooms and beds. Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. The issues are further complicated by the fact that patient arrivals are dynamic and treatment processes are stochastic. Our approach to providing decision support combines techniques from multi-agent systems and computational intelligence (CI). This combination of techniques allows to properly consider the dynamics of the problem while reflecting the distributed decision making practice in hospitals. Multi-agent techniques are used to model multiple hospital care units and their decision policies, multiple patient groups with stochastic treatment processes and uncertain resource availability due to overlapping patient treatment processes. The agent-based model closely resembles the real-world situation. Optimization and learning techniques from CI allow for designing and evaluating improved (adaptive) decision policies for the agent-based model, which can then be implemented easily in hospital practice. In order to gain insight into the functioning of this complex and dynamic problem setting, we developed an agent-based model for the hospital care units with their patients. To assess the applicability of this agent-based model, we developed an extensive simulation. Several experiments demonstrate the functionality of the simulation and show that it is an accurate representation of the real world. The simulation is used to study decision support in resource management and patient admission control. To further improve the quality of decision support, we study the prediction of future hospital resource usage. Using prediction, the future impact of taking a certain decision can be taken into account. In the problem setting at hand for instance, predicting the resource utilization resulting from an admission decision is important to prevent future bottlenecks that may cause the blocking of patient flow and increase patient waiting times. The methods we investigate for the task of prediction are forward simulation and supervised learning using neural networks. In an extensive analysis we study the underlying probability distributions of resource occupancy and investigate, by stochastic techniques, how to obtain accurate and precise prediction outcomes. To optimize resource allocation decisions we consider multiple criteria that are important in the hospital problem setting. We use three conflicting objectives in the optimization: maximal patient throughput, minimal resource costs and minimal usage of back-up capacity. All criteria can be taken into account by finding decision policies that have the best trade-off between the criteria. We derived various decision policies that partly allow for adaptive resource allocations. The design of the policies allows the policies to be easily understandable for hospital experts. Moreover, we present a bed exchange mechanism that enables a realistic implementation of these adaptive policies in practice. In our optimization approach, the parameters of the different decision policies are determined using a multiobjective evolutionary algorithm (MOEA). Specifically, the MOEA optimizes the output of the simulation (i.e. the three optimization criteria) as a function of the policy parameters. Our results on resource management show that the benchmark allocations obtained from a case study are considerably improved by the optimized decision policies. Furthermore, our results show that using adaptive policies can lead to better results and that further improvements may be obtained by integrating prediction into a decision policy

AI Watch: Assessing Technology Readiness Levels for Artificial Intelligence

Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are when and how this is going to happen. Not only do we lack the tools to determine what achievements will be attained in the near future, but we even underestimate what various technologies in AI are capable of today. Many so-called breakthroughs in AI are simply associated with highly-cited research papers or good performance on some particular benchmarks. Certainly, the translation from papers and benchmark performance to products is faster in AI than in other non-digital sectors. However, it is still the case that research breakthroughs do not directly translate to a technology that is ready to use in real-world environments. This document describes an exemplar-based methodology to categorise and assess several AI research and development technologies, by mapping them into Technology Readiness Levels (TRL) (e.g., maturity and availability levels). We first interpret the nine TRLs in the context of AI and identify different categories in AI to which they can be assigned. We then introduce new bidimensional plots, called readiness-vs-generality charts, where we see that higher TRLs are achievable for low-generality technologies focusing on narrow or specific abilities, while low TRLs are still out of reach for more general capabilities. We include numerous examples of AI technologies in a variety of fields, and show their readiness-vs-generality charts, serving as exemplars. Finally, we use the dynamics of several AI technology exemplars at different generality layers and moments of time to forecast some short-term and mid-term trends for AI.JRC.B.6-Digital Econom

Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel

This thesis is concerned with automated personnel scheduling in healthcare organisations; in particular, nurse rostering. Over the past forty years the nurse rostering problem has received a large amount of research. This can be mostly attributed to its practical applications and the scientific challenges of solving such a complex problem. The benefits of automating the rostering process include reducing the planner’s workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. Basically stated, the nurse rostering problem requires the assignment of shifts to personnel to ensure that sufficient employees are present to perform the duties required. There are usually a number of constraints such as working regulations and legal requirements and a number of objectives such as maximising the nurses working preferences. When formulated mathematically this problem can be shown to belong to a class of problems which are considered intractable. The work presented in this thesis expands upon the research that has already been conducted to try and provide higher quality solutions to these challenging problems in shorter computation times. The thesis is broadly structured into three sections. 1) An investigation into a nurse rostering problem provided by an industrial collaborator. 2) A framework to aid research in nurse rostering. 3) The development of a number of advanced algorithms for solving highly complex, real world problems

Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel

This thesis is concerned with automated personnel scheduling in healthcare organisations; in particular, nurse rostering. Over the past forty years the nurse rostering problem has received a large amount of research. This can be mostly attributed to its practical applications and the scientific challenges of solving such a complex problem. The benefits of automating the rostering process include reducing the planner’s workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. Basically stated, the nurse rostering problem requires the assignment of shifts to personnel to ensure that sufficient employees are present to perform the duties required. There are usually a number of constraints such as working regulations and legal requirements and a number of objectives such as maximising the nurses working preferences. When formulated mathematically this problem can be shown to belong to a class of problems which are considered intractable. The work presented in this thesis expands upon the research that has already been conducted to try and provide higher quality solutions to these challenging problems in shorter computation times. The thesis is broadly structured into three sections. 1) An investigation into a nurse rostering problem provided by an industrial collaborator. 2) A framework to aid research in nurse rostering. 3) The development of a number of advanced algorithms for solving highly complex, real world problems