Integration of simulation and DEA to determine the most efficient patient appointment scheduling model for a specific healthcare setting

Purpose: This study is to develop a systematic approach for determining the most efficient patient appointment scheduling (PAS) model for a specific healthcare setting with its multiple appointments requests characteristics in order to increase patients’ accessibility and resource utilization, and reduce operation cost. In this study, three general appointment scheduling models, centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM), are considered. Design/methodology/approach: The integration of discrete event simulation and data envelopment analysis (DEA) is applied to determine the most efficient PAS model. Simulation analysis is used to obtain the outputs of different configurations of PAS, and the DEA based on the simulation outputs is applied to select the best configuration in the presence of multiple and contrary performance measures. The best PAS configuration provides an optimal balance between patient satisfaction, schedulers’ utilization and the cost of the scheduling system and schedulers’ training. Findings: In the presence of high proportion (more than 70%) of requests for multiple appointments, CSM is the best PAS model. If the proportion of requests for multiple appointments is medium (25%-50%), HSM is the best. Finally, if the proportion of requests for multiple appointments is low (less than 15%), DSM is the best. If the proportion is in the interval from 15% to 25% the selected PAS model could be either DSM or HSM based on expert idea. Similarly, if the proportion is in the interval from 50% to 70% the best PAS model could be either CSM or HSM. Originality/value: This is the first study that determines the best PAS model for a particular healthcare setting. The proposed approach can be used in a variety of the healthcare settings. Keywords: data envelopment analysis, discrete event simulation, patient appointment scheduling, multiple appointments, centralized scheduling model, decentralized scheduling model, hybrid scheduling modelPeer Reviewe

A SIMULATION-BASED DEA FRAMEWORK TO IMPROVE CUSTOMER'S WAITING TIME AT VEHICLE INSPECTION CENTRE

A long queue and waiting time have become the most common issue that usually happened at service industry. Similarly, in a vehicle inspection centre (VIC), a higher quality of service is measured by a short and acceptable waiting time. Typically, the long waiting time among customers is resulted by some factors, which are customer arrivals, human factors, and maintenance strategy. However, this study only focuses on customer arrival factor that contributed to this problem. This paper is a review of work based on a study conducted at VIC in Selangor, Malaysia. A framework of simulation-based DEA model is proposed to determine the most efficient strategy to reduce the problem of customer waiting time at VIC. The developed framework aims to help the management in decision making to improve the operation of the VIC current system in future

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources

Discrete event simulation and data envelopment analysis models for selecting the best resource allocation alternative at an emergency department’s green zone

The Green Zone of Emergency Department Hospital Universiti Sains Malaysia (EDHUSM) which provides treatment for non-critical cases contributes partly to the hustle and bustle in the emergency department. The imbalance of doctors and nurses with the patient ratio which forms the resources’ bottleneck further results to the long patients’ waiting time especially after the office hours and during weekends and public holidays. Collectively, this disproportion and bottlenecks roots up the current problem faced by Green Zone EDHUSM which constantly fails to achieve the KPIs set by the hospital. Henceforth, this study focuses on the best resource allocation of doctors and nurses for shifts during the weekdays and for shifts during weekends and public holidays. The hybrid method of Discrete Event Simulation, and Data Envelopment Analysis models such as BCC-input oriented and Super-Efficiency, were deployed to obtain the best resource allocation for the two groups of shift. The method produced a series of resources allocation alternatives for doctors and nurses with a total of 64 alternatives for weekdays and 729 alternatives for weekends and public holidays. The results show that the best allocation for doctors and nurses during weekdays are three doctors and three nurses serving for every shift, while during weekends and public holidays, a combination of four doctors and four nurses for every shift are the best. The proposed combinations have reduced the average waiting time, optimized the utilization of doctors and nurses, and managed to increase the number of patients served during weekdays, weekends and public holidays

Application of multi-criteria decision analysis for investment strategies in the Indian equity market

In the Indian equity market, the Systematic Investment Plan (SIP) is the most popular strategy due to its convenience for disciplined investing regardless of market conditions. This study analyzes the excess returns of an extensive dataset of listed Indian companies from 2010 to 2019, along with a value-based version of the Multi-Criteria Decision Analysis (MCDA), to identify top performing stocks, based on their sectors and market capitalization. The findings of the study provide empirical evidence of Value Averaging (VA) as a viable alternative strategy over SIP (also known as Dollar Cost Averaging or Rupee Cost Averaging) as 352 out of 359 companies yielded higher returns under VA. The superiority of the VA strategy over the SIP was particularly marked in the consumer goods, financial services and industrial manufacturing sectors, with a clear dominance of small cap companies. The results also show that risk factors for VA strategy play an important role and should be taken into account, rather than base investment decisions on excess returns alone. The efficiency scores of individual stocks provide important insights for mutual funds, financial brokers and individual investors in India

A DEA Model to Optimize Insurance Payment Plans based on PACs

Healthcare industry has evolved dramatically over the time. From being a “cottage industry” to an “organized industry” has brought lot of changes. The changes have been both good and bad. Among the problems that have surfaced in past couple of decades, rising healthcare cost has been one of the most significant. The rising healthcare cost has been documented to be a symptom of several factors. Since the inception of healthcare as an organized industry several payment models for providers and hospitals have been adopted. Current healthcare reforms have proposed new payments models to curb the rising cost and provide consumer oriented healthcare. The proposed payment models such as, bundled, capitation, PROMETHEUS, pay-for-performance and traditional model of fee-for-service, all have their merits and demerits. Some are good for chronic and others for acute conditions, some provide bonuses to physicians for high quality and efficient care where as others pay more for number of services used. Our literature review has highlighted the lack of systemic study to analyze the effect of payment models on reimbursement of physicians and hospitals. This study shows that no “single model” can be implemented to serve all the stakeholders. The proposed optimization model is a strategic tool that aligns dynamic patient population with existing reimbursement models and provides information to providers to help them design favorable contracts with insurers. The model also has a potential to help improve planning and operational activities of hospitals

A Literature review on the Identification of Variables for Measuring Hospital Efficiency in the Data Envelopment Analysis (DEA)

Abstract The selection of input and output variables usually pose a problem when carrying out ef�ficiency assessment in hospitals. Data Envelopment Analysis (DEA) is an instrument that is used to calculate the efficiency of a hospital using some inputs and outputs. Therefore, this study aims to identify the most frequently used hospital inputs and outputs from an existing paper,, in order to assist the hospital management staffs in choosing the relevant variables that can represent available inputs, are easily accessible, and need improvement. It was conducted using keywords such as “hospital efficiency” and “DEA for hospital” to search for peer-reviewed journals in the PubMed and Open Knowledge Maps from the year 2014- 2020. From, the 586 articles, 54 samples were obtained from the about 5-3504 hospitals which were analyzed from 23 countries. The results showed that, the five most used inputs were the number of beds, medical personnel, non-medical staff, medical technician staff and operational costs, while the most used outputs were number of inpatients, surgeries, emergency visits, outpatient service, and days of inpatients. These variables are often used for accessing the efficiency of hospitals in the DEA application

Simulation Modelling in Healthcare: Challenges and Trends

AbstractIn this paper, we describe simulation models in healthcare that have been developed in the past two decades. Simulation systems, ranging from simulation of patient flow in emergency rooms to simulation of populations with a specific chronic diseases, are reviewed. Simulation types included discrete event simulation (DES) and agent based simulation (ABS). A trend of variability and scalability were identified, and discussed in terms of platform used to develop the model, data sources, and computational power needed to run the simulation. In the synthesis of simulation models, programming languages and products emerged as clusters. Design models and systems engineering development processes are examined with a focus on requirements discovery, models and scenarios of simulation. Graphic user interfaces in the simulation tools in healthcare are reviewed in terms of visual design and human factors. Furthermore, interaction modes and trends of information visualization techniques used for the simulations are reported. Agent-based simulation models in particular were reviewed, and findings suggest agent characteristics varied across literature researched in aspects such as socio-demographic design considerations