Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.
The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.
Various pharmacy services are offered in public health facilities, ranging from distributive activities (dispensing) to patient-oriented services (pharmaceutical care). These activities are monitored through indicators established at the national level. In Malaysia, the indicators have not been transformed into a measurement of hospital pharmacy service efficiency. The main objectives of this study were to assess the relative performance of hospital pharmacy services and to investigate the factors that may affect the performance levels. Double-bootstrap data envelopment analysis was applied to measure the technical efficiency levels of 124 public hospital pharmacies in 2014. An input-oriented variable returns to scale model was adopted in the study, while bootstrap truncated regression was conducted to identify the factors that may explain the differences in the efficiency levels. The average bias-corrected technical efficiency score varies according to the hospital size (0.84, 0.78 and 0.82 in small, medium and large hospitals, respectively). The hospital size, hospital age, urban location and information technology are important determinants of the efficiency levels. The study contributes to establishing baseline technical efficiency information for public hospital pharmacy services in Malaysia. The measurement of hospital pharmacy efficiency can guide future policy making to improve performance and ensure the optimum level of available resources.
Malaysia was faced with a life-threatening crisis in combating COVID-19 with a number of positive cases reaching 5305 and 88 deaths by 18th April 2020 (the first detected case was on 25th January 2020). The government rapidly initiated a public health response and provided adequate medical care to manage the public health crisis during the implementation of movement restrictions, starting 18th March 2020, throughout the country. The objective of this study was to investigate the relative efficiency level of managing COVID-19 in Malaysia using network data envelopment analysis. Malaysia state-level data were extracted from secondary data sources which include variables such as total number of confirmed cases, death cases and recovered cases. These variables were used as inputs and outputs in a network process that consists of 3 sub processes i) community surveillance, ii) medical care I and iii) medical care II. A state-level analysis was performed according to low, medium and high population density categories. The efficiency level of community surveillance was highest compared to medical care processes, indicating that the overall inefficiency is greatly influenced by the inefficiency of the medical care processes rather than the community surveillance process. Results showed that high-density category performed well in both community surveillance and medical care II processes. Meanwhile, low-density category performed better in medical care I process. There was a good overall performance of the health system in Malaysia reflecting a strong preparedness and response level to this pandemic. Furthermore, resource allocation for rapid response was distributed effectively during this challenging period.