A COVID-19 Recovery Strategy Based on the Health System Capacity Modeling. Implications on Citizen Self-management

Versión preprint depositada sin articulo publicado dada la actualidad del tema. *Solicitud de los autoresConfinement ends, and recovery phase should be accurate planned. Health System (HS) capacity, specially ICUs and plants capacity and availability, will remain the key stone in this new Covid-19 pandemic life cycle phase. Until massive vaccination programs will be a real option (vaccine developed, world wield production capacity and effective and efficient administration process), date that will mark recovery phase end, important decisions should be taken. Not only by authorities. Citizen self-management and organizations self-management will be crucial. This means: citizen and organizations day a day decision in order to control their own risks (infecting others and being infected). This paper proposes a management tool that is based on a ICUs and plants capacity model. Principal outputs of this tool are, by sequential order and by last best data available: (i) ICUs and plants saturation estimation data (according to incoming rate of patients), (ii) with this results new local and temporal confinement measure can be planned and also a dynamic analysis can be done to estimate maximum Ro saturation scenarios, and finally (iii) provide citizen with clear and accurate data allow them adapting their behavior to authorities’ previous recommendations. One common objective: to accelerate as much as possible socioeconomic normalization with a strict control over HS relapses risk

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

Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission

Pre-treatment selection or censoring (`selection on treatment') can occur when two treatment levels are compared ignoring the third option of neither treatment, in `censoring by death' settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument

Forecasting hospital bed availability using computer simulation and neural networks

The success of hospitals in treating patients and staying in business relies on their efficient use of resources. In particular, the utilization of hospital beds is a critical concern, since over-crowding will result in delays or transfers of patients, and under-utilization will result in lost opportunity to treat patients and generate profit. To this end, hospital decision makers must have reliable forecasts of patient demand and bed availability. The objective of this thesis was to create a general method to forecast the availability of hospital beds in the short term, up to 2 days into the future. Specifically, this thesis employed a computer simulation model of the hospital and a time-dependent neural network to learn from the simulated model and forecast the availability of beds. The computer simulation model was found to be well suited to the task of describing a general hospital system and creating training data for a neural network. The neural network was found to provide accurate performance in predicting bed availability in the short term. The network incorporated the effect of time explicitly to capture the non-stationary behavior of hospital systems. These findings have a number of implications that will be discussed

A decision support simulation model for bed management in healthcare

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources such as beds. Bed management is key to the effective delivery of high-quality and low-cost healthcare. An efficient utilization of beds requires a detailed understanding of the hospital\u27s operational behavior. It is necessary to understand the behavior of a hospital in order to make necessary adjustments to its resources, and policies, which can improve patient\u27s access to care. The aim of this research was to develop a discrete event simulation to assist in planning and staff scheduling decisions. Each department\u27s performance measures were taken into consideration separately to understand and quantify the behavior of individual departments, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time for patients, and length of stay of patients. From the results, the departments that have long queues of patients, waiting times, and lengths of stay are detailed to predict how the hospital reacts to patient flow --Abstract, page iv

A Model to Create an Efficient and Equitable Admission Policy for Patients Arriving to the Cardiothoracic ICU

To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons. Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs

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

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