The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

A heuristic approach to solve the preventive health care problem with budget and congestion constraints

This document is the Accepted Manuscript version of the following article: Soheil Davari, Kemal Kilic, and Siamak Naderi, ‘A heuristic approach to solve the preventive health care problem with budget and congestion constraints’, Applied Mathematics and Computation, Vol. 276, pp. 442-453, March 2016, doi: https://doi.org/10.1016/j.amc.2015.11.073. This manuscript version is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License CC BY NC-ND 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Preventive health care is of utmost importance to governments since they can make massive savings on health care expenditure and promote the well-being of the society. Preventive care includes many services such as cancer screenings, vaccinations, hepatitis screenings, and smoking cessation programs. Despite the benefits of these services, their uptake is not satisfactory in many countries in the world. This can be attributed to financial barriers, social issues., and other factors. One of the most important barriers for preventive care is accessibility to proper services, which is a function of various qualitative and quantitative factors such as the distance to travel, waiting time, vicinity of facilities to other attractive facilities (such as shopping malls), and even the cleanliness of the facilities. Statistics show that even a small improvement in people’s participation can save massive amounts of money for any government and improve the well-being of the people in a society. This paper addresses the problem of designing a preventive health care network considering impatient clients, and budget constraints. The objective is to maximize the accessibility of services to people. We model the problem as a mixed-integer programming problem with budget constraints, and congestion considerations. An efficient variable neighborhood search procedure is proposed and computational experiments are performed on a large set of instances.Peer reviewedFinal Accepted Versio

An exact Method for Stochastic Maximal Covering Problem of Preventive Healthcare Facilities

Abstract Effective preventive healthcare services have a significant role in reducing fatality and medical expenses in all human societies and the level of accessibility of customers to these services can be considered as a measure of their efficiency and effectiveness. The main purpose of this paper is to develop a service network design model of preventive healthcare facilities with the principal objective of maximizing participation in the offered services. While considering utility constraints and incorporating demand elasticity of customers due to travel distance and congestion delays, optimal number, locations and capacities of facilities as well as customer assignment o facilities are determined. First, the primary nonlinear integer program is transformed, and then the linearized model is solved by developing an exact algorithm. Computational results show that large-sized instances can be solved in a reasonable amount of time. An illustrative case study of network of hospitals in Shiraz, Iran, is used to demonstrate the model and the managerial insights are discussed

Design of pedestrian network friendliness maps

This article introduces the concept of pedestrian, or walking friendliness, and presents a methodology for obtaining maps thereof. Walking friendliness is a quality of walking indicator, defined for any given origin in a city, which combines accessibility measures, based on impedance between that origin and destinations, with performance scores for the pedestrian infrastructure linking those origins and destinations. The methodology uses geographic information systems to obtain walking friendliness values and represent them in a map. The approach is demonstrated through a case study for the city of Coimbra, Portugal, for which friendliness maps were derived. The procedure and maps that were produced can be scaled to any size of city.info:eu-repo/semantics/publishedVersio

Assessing performance in health care: A mathematical programming approach for the re-design of primary health care networks

Mathematical models allow studying complex systems. In particular, optimal facility location models provide a sound framework to assess the performance of first-level of health care networks. In this work, a methodology founded on need/offer/demand quantification through a facility location-based mathematical model is proposed to assess the performance of existing networks of Primary Health Care Centers (PHCC) and assist in its re-design. The proposed re-design problem investigates the re-allocation of existing resources within the given infrastructure (existing PHCCs) to better satisfy the estimated health needs of the target population. This problem has not been widely addressed in the open literature despite its paramount importance in modern societies with fast demographic dynamics and constrained investment capacities. The model seeks to optimally assign the required type of service and the corresponding capacity to each PHCC (offer). The objective function to be maximized is the number of (needed) patients’ visits effectively covered by the network (demand). The following constraints are explicitly considered: i) geographic accessibility from need centers to PHCCs, ii) maximum delivery capacity of each service in each PHCC, and iii) total budget regarding fixed, variable, and relocation costs. The proposed methodology was applied to a medium-size city. Results show that the non-attended necessity can be reduced by introducing capacity modifications in the existing network. Moreover, different solutions are obtained if budgetary restrictions or minimum attention volume constraints are included. This reveals how model-based decision support tools can help health decision-makers assessing primary health care network performance.Fil: Elorza, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Moscoso, Nebel Silvana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

Measuring Spatial Health Disparity Using a Network-Based Accessibility Index Method in a GIS Environment: A Case Study of Hillsborough County, Florida

In recent decades, the health care delivery system in the United States has been greatly transformed and more widely examined. Even with one of the most developed health care systems in the world, the United States still experiences great spatial disparity in health care access. Increasing diversity of class, culture, and ethnicity also has a significant impact on health disparity. The goal of this paper is to address the spatial disparity of health care access using a network-based health accessibility index method (NHAIM) in a Geographic Information System (GIS) environment. Ensuring a desired level of accessibility for patients is the goal of the health care delivery system, through which health care service providers are supplied to populations in need. GIS plays an increasing role in understanding and analyzing accessibility to health care by incorporating geographical physical barriers, network-based travel time, and transportation costs required for access to health care services. In this study, we develop a NHAIM to examine the spatial disparity in health care access in Hillsborough County, Florida, determining the locations of registered medical doctors and facilities using data from Medical Quality Assurance Services (MQA) and the U.S. Census. This research reveals the spatial disparity of health care accessibility and availability in this region and provides an effective method for capturing health care accessibility surplus and shortage areas for future health care service planning

Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimizing costs, the expected completion time, population travel and waiting times. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade offs between solution cost, completion time, population travel and waiting times.Peer ReviewedPostprint (author's final draft

A review of the healthcare-management (modeling) literature published at Manufacturing and Service Operations Management

Healthcare systems throughout the world are under pressure to widen access, improve efficiency and quality of care, and reduce inequity. Achieving these conflicting goals requires innovative approaches, utilizing new technologies, data analytics, and process improvements. The operations management community has taken on this challenge: more than 10% of articles published in M&SOM in the period from 2009 to 2018 has developed analytical models that aim to inform healthcare operational decisions and improve medical decision-making. This article presents a review of the research published in M&SOM on healthcare management since its inception 20 years ago and reflects on opportunities for further research