Customer Service Multichannel Model in a Health Care Service Provider: A Discrete Simulation Case Study/MODELO MULTICANAL DE ATENCION AL CLIENTE EN UNA IPSS: ESTUDIO DE CASO CON SIMULACION DISCRETA/MODELO MULTICANAL DE ATENCAO AO CLIENTE NUMA IPSS: ESTUDO DE CASO COM SIMULACAO DISCRETA/MODELE DE SERVICE CLIENT MULTICANAL DANS UNE IPSS : UNE ETUDE DE CAS AVEC SIMULATION DISCRETE. - Vol. 29 Núm. 72, Abril 2019 - Revista Innovar - Libros y Revistas - VLEX 780444125

Customer Service Multichannel Model in a Health Care Service Provider: A Discrete Simulation Case Study/MODELO MULTICANAL DE ATENCION AL CLIENTE EN UNA IPSS: ESTUDIO DE CASO CON SIMULACION DISCRETA/MODELO MULTICANAL DE ATENCAO AO CLIENTE NUMA IPSS: ESTUDO DE CASO COM SIMULACAO DISCRETA/MODELE DE SERVICE CLIENT MULTICANAL DANS UNE IPSS : UNE ETUDE DE CAS AVEC SIMULATION DISCRETE.

AutorRestrepo-Morales, Jorge Anibal
CargoCompetitividad y Gestion

Introduction

This project deals with commercial management applied to health care services, a field that counts with infinite possibilities to apply modeling techniques such as the queueing theory, methods and times, and linear programming, among others. Under the current unsettled conditions, health care entities, as service-providing institutions, not only need to offer a wide range of services for their users, but also to develop strategies to provide such services appropriately. Therefore, aspects such as users' comfort are critical for success. This variable is closely related to the time a service is requested and the moment it is provided (Jimenez, 2011).

In this way, one of the parameters for estimating the demand for care at the Health Care Service Providers (HCSP) is their operating hours; that is, from 6:00 a.m. to 6:00 p.m. Evidence shows that the assistance process has serious weaknesses, since users have to repeatedly wait for services at several stations; therefore, they are subject to remain standing in multiple lines waiting for the required services. At the first station line users have to wait for the taking of a blood sample, which will be later used by physicians for the diagnosis of diseases. Afterwards, users have to wait approximately one hour for medical consultation. Finally, they have to wait almost 45 minutes to obtain the results of their examinations. Thus, time estimates by the manager are far from the actual estimates, generating a gap of two hours per user, on average. In this way, service operation costs are expected to increase, while users' satisfaction with the service provided diminishes.

Discrete Event Simulation (DES) has been widely used in modeling health-care systems for many years and the number of papers published has steadily increased since 2004 (Gunal & Pidd, 2010). This methodology provides elements of judgment with a direct impact on patients' care, cost reduction and users' satisfaction, making possible to achieve an adequate distribution of resources in the health care system (Brailsford & Hilton, 2001). On this regard, Gardner and Berry (1995) carried out a research study in which simulated experiments were developed for treating three groups of patients. The integration of varying techniques is presented as a differentiating element, since the simultaneous application of industrial engineering techniques--recording time, studying demand, and studying the waiting process and discrete event simulation--have been often used separately. All of these have been applied by health service providers to find an optimal solution to their installed capacity problems, which is understood as the recommendations to implement the best response to the problem addressed (Eppen, 2000); in this case, the constant lines users have to make in the customer service area.

In this context, Duguay and Chetouane (2007) described a DES study of an emergency department in a Canadian hospital in which the objective was to reduce patient waiting times and improve overall service delivery and system throughput. In the same way, by determining the optimal number of doctors, laboratory technicians and nurses required, Ahmed and Alkhamis (2009) presented a combination of simulation techniques with optimization to maximize patient performance and reduce patient time spent in the emergency unit of a government hospital in Kuwait. In addition, Knight, Williams, and Reynolds (2012) proposed a simulation for modeling the selection of patients for knee surgery in Wales, United Kingdom. Using hierarchical models, Hall (2013) tried to resolve waiting times by establishing a sequence of activities patients must go through in order to receive attention, also including the development and implementation of performance measures for the system. Norouzzadeh, Riebling, Carter, Conigliaro, and Doerfler (2015) proposed a simulation model using DES for the optimization of the total time patients spend in a clinic in the United States. More recently, Kad, Kuvvetli, and Colak (2016) studied the blood laboratory of a university hospital via discrete event simulation to analyze processes and bottleneck operations, while Demir, Gunal, and Southern (2016) developed a DES model for capturing individual patient pathways until discharge.

After this introduction, which presents the problem, goals and justification of our study, the paper is organized as follows. The second section exposes the theoretical grounds for modeling systems and their application in business. The third section is a theoretical approach to the methodology and introduces the probability distributions that can be applied; ProModel will be used for the simulation process and Statfit and Excel for statistical analysis, so that program frameworks with information about times could be obtained for modeling the probability distributions of customer service times. The fourth section sets forth the methodology suggested, as well as the data and the information to be used; field research, analysis of information and modeling application are presented in this section as well. The last section gathers the main findings of our research, as well as its limitations, and signals future research lines.

Theoretical Framework

Work Measurement

Through a set of procedures, work measurement aims to establish the time it takes a qualified worker to carry out a defined task according to a preconfigured rule of execution (De-la-Fuente-Garcia, Gomez-Gomez, Garcia-Fernandez, & Puente-Garcia 2006). It is the application of diverse techniques, such as work measurement and the study of methods, to systematically study the work of man in all contexts in order to identify improvement plans by inquiring about the determinants of efficiency and productivity of the object under study (Aguirre-de-Mena, Rodriguez-Fernandez, & Tous-Zamora, 2002; meyers, 2000; OIT, 2005). The study of methods is considered essential to decrease the proportion of work and discard the redundant processes carried out by employees, with the objective of replacing inappropriate tasks by efficient activities. Thus, when the causes of unsuccessful time are established, it is feasible to carry out strategies such as, for example, redesigning processes that do not add value or combining tasks when possible (Alfaro-Beltran & Alfaro-Escolar, 1999; Castanyer-Figueras, 1988; Meyers, 2000; Prieto, 2007).

Work measuring general procedure is made up of six stages: (i) task selection, (ii) relevant information recording, (iii) critical data analysis, (iv) work measurement, (v) standard times collection, and (vi) definition of the operational method. It is important to note the strict observance of the previous stages when defining the standard time (Neira, 2006).

Queueing Theory and Work Measurement

To define a queue line service, the arrival of the customer and a service provision must be occurring at irregular intervals. Most of them follow the basic process for formulating a queueing model, that is, units requiring a service arrive at the system. Such units enter the system and join the line. At certain points in time an element from the line is selected to be provided with a service by means of a rule known as "queueing discipline". Then, the "service discipline" provides the unit selected with the service requested (Thierauf, 1995). Lines are common phenomena in diverse industrial and commercial activities, such as banks and stores, production orders, service stations, and others (Calderon, 1979). Healthcare services are not the exception, since various research studies on the field have reviewed the application of queueing theory in specific categories.

The use of the standard M/M/s model, its conjectures, extensions, and implementation in HCSPs is crucial to establish the necessary number of servers to offer an adequate service and increase consumer satisfaction. We found several studies on waiting queue models, such as the McClain model (1976), which analyzed different techniques to determine the incidence of bed allocation policies on utilization, waiting time, and the probability of rejecting patients. Nosek and Wilson (2001) focused the use of queueing theory in pharmacy applications with a special emphasis on identifying tactics to improve client fulfillment by predicting and reducing waiting times and adjusting staffing. Green (2006) approached queueing theory to show its application in healthcare by means of models, which argued the correspondence of variables such as time delays, installed capacity, and number of nurses. In addition, Fomundam and Herrmann (2007) studied the application of queueing theory in specific variables, such as waiting time and utilization analysis, system design, appointment systems, and system size. Meanwhile, two research papers reviewed the existing academic literature in the field of discrete simulation in healthcare settings. In the first instance, Jacobson, Hall, and Swisher (2006) report that a significant number of papers are devoted to understand the relationship between health systems inputs (hours and routes of care, triage, installed capacity, and human capital) and their products (waiting times and evolution of patients, designation of medical personnel, and use of facilities). Additionally, Lakshmi and Appa-Iyer (2013) established three categories for the classification of methods in health care systems: (i) design (type of care, facilities, pharmaceutical services); (ii) operation (scheduling of resources and patients); and (iii) analysis (waiting times and utilization, duration, and costs).

Basic Structure of Queueing Models

The queueing phenomenon is comprised of six main elements: (i) population source; (ii) the mechanism used by customers to arrive at the service facilities; (iii) the characteristics of the lines created; (iv) the mode of selecting customers waiting in line; (v) the characteristics of service facilities; and (vi) the...

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