Yong-Hong Kuo

Using Simulation to Analyze Patient Flows in a Hospital Emergency Department in Hong Kong

This paper presents a case-study of applying simulation to analyze patient flows in a hospital emergency department in Hong Kong. The purpose of our work is to analyze the impact of the enhancements made to the system after the relocation of the emergency department. We developed a simulation model (using ARENA) to capture all the key relevant processes of the department. Using the simulation model, we evaluated the impact of possible changes to the system by running different scenarios. This provides a tool for the operations manager in the emergency department to “foresee” the impact on the daily operations when making possible changes (such as, adjusting staffing levels or shift times), and consequently make much better decisions.

From Preparedness to Recovery: A Tri-Level Programming Model for Disaster Relief Planning

This paper proposes a tri-level programming model for disaster preparedness planning. The top level addresses facility location and inventory pre-positioning decisions; the second level represents damage caused by the disaster, while the third level determines response and recovery decisions. We use an interdiction framework instead of a stochastic or chance-constrained model. This allows the extent of damage to be treated as a parameter to facilitate scenario exploration for decision-support. We develop an iterative dual-ascent solution approach. Computational results show that our approach is efficient, and we can also draw some insights on disaster relief planning.

RFID analytics for hospital ward management

In this paper, we present an RFID-enabled platform for hospital ward management. Active RFID tags are attached to individuals and assets in the wards. Active RFID readers communicate with the tags continuously and automatically to keep track of the real-time information about the locations of the tagged objects. The data regarding the locations and other transmitted information are stored in the ward management system. This platform enables capabilities of real-time monitoring and tracking of individuals and assets, reporting of ward statistics, and providing intelligence and analytics for hospital ward management. All of these capabilities benefit hospital ward management by enhanced patient safety, increased operational efficiency and throughput, and mitigation of risk of infectious disease widespread. A prototype developed based on our proposed architecture of the platform was tested in a pilot study, which was conducted in two medical wards of the intensive care unit of one of the largest public general hospitals in Hong Kong. This pilot study demonstrates the feasibility of the implementation of this RFID-enabled platform for practical use in hospital wards. Furthermore, the data collected from the pilot study are used to provide data analytics for hospital ward management.

Using simulation to examine appointment overbooking schemes for a medical imaging center

In this paper, we present an appointment scheduling problem faced by a medical imaging center in a major hospital in Macau. We developed an empirically calibrated simulation model to represent the appointment and medical diagnosis procedure as a multi-server queuing network with multiple patient classes. Four appointment overbooking schemes are proposed to compensate for patient no-shows and unpunctuality. The focus of this study is to integrate overbooking schemes with existing appointment rules to improve the operational efficiency of the center. Simulation results show that our proposed overbooking schemes significantly enhance the performance of the center. Compared with the current practice, the best performing overbooking scheme reduces the overtime by 58.32% and the idle time by 23.65%, increases the number of patients served by 15.9%, while still ensuring that patient waiting times remain acceptable.

On the mixed set covering, packing and partitioning polytope

We study the polyhedral structure of the mixed set covering, packing and partitioning problem, which has drawn little attention in the literature but has many real-life applications. By considering the interactions between the different types of edges of an induced graph, we develop new classes of valid inequalities. In particular, we derive the (generalized) mixed odd hole inequalities, and identify sufficient conditions for them to be facet-defining. In the special case when the induced graph is a mixed odd hole, the inclusion of this new facet-defining inequality provide a complete polyhedral characterization of the mixed odd hole polytope. Computational experiments indicate that these new valid inequalities may be effective in reducing the computation time in solving mixed covering and packing problems.

From data to big data in production research: the past and future trends

Data have been utilised in production research in meaningful ways for decades. Recent years have offered data in larger volumes and improved quality collected from diverse sources. The state-of-the-art data research in production and the emerging methodologies are discussed. The review of the literature suggests that production research enabled by data has shifted from that based on analytical models to data-driven. Manufacturing and data envelopment analysis have been the most popular application areas of data-driven methodologies. The research published to date indicates that data mining is becoming a dominant methodology in production research. Future trends and opportunities for data-driven production research are presented.

Appointment overbooking with different time slot structures

Abstract Unattended appointments result in underperformance of a healthcare service provider. Such uncertainty in the appointment process not only lowers healthcare utilization and productivity but also hinders patients from having timely access to healthcare services and extends waiting time for receiving medical examinations or treatments. In this study, we formulate this stochastic optimization problem for appointment scheduling as its two-stage deterministic equivalent to simultaneously optimize overbooking and scheduling decisions to compensate patient no-shows with different time slot structures. We examine the impacts of three types of time slot structures, which are of fixed-length slot intervals, dome-pattern slot intervals and flexible appointment start times, on the efficiency of the system. With the optimal solutions found, we investigate the interaction between the time slot structure and the optimal overbooking solution. We found that the flexibility in appointment start times result in a “dome-dome-dome pattern with alternate long and short time slots” and could achieve a better patient experience (regarding the patient waiting time) while maintaining the same service provider efficiency (regarding the resource overtime and idle time), compared with the pre-defined time intervals. While there has been a large body of literature on appointment scheduling, to the best of our knowledge, this “dome-dome-dome” pattern has not been reported in the existing literature. Sensitivity analysis further shows that the flexible slot scheme is more effective when the number of overbooked patients is relatively low and the service duration is relatively long.

Data Visualization on Global Trends on Cancer Incidence An Application of IBM Watson Analytics

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across over a hundred of cancer registries worldwide. In this study, we present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. We included 26 cancers from different geographic regions. An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, we can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is assessable by any computer connected to the Internet.

Appointment Overbooking and Scheduling: Tradeoffs Between Schedule Efficiency and Timely Access to Service

Appointment overbooking is one of the most popular ways to mitigate the risk of no-shows. Although appointment overbooking can improve resource utilization, it may also lead to adverse consequences such as increases in resource overtime and patient waiting time. While the majority of research on appointment overbooking focuses on the system performance within the session, our work aims to study the tradeoffs between schedule efficiency and timely access to service. We integrate a stochastic mixed-integer linear program, which assigns patients to appointment time slots, into a queueing model, which evaluates the timeliness of access to service. Our computational results suggest that when the session capacity is less than the appointment request rate, overbooking can greatly reduce appointment lead time and patient abandonment rate and only slightly increases resource overtime and patient waiting time. However, the benefits of overbooking become mild when the session capacity is larger than the appointment request rate.

How Do Missing Patients Aggravate Emergency Department Overcrowding? A Real Case and a Simulation Study

Emergency department overcrowding has been reported over decades around the globe and the phenomenon is observed to be worsening in recent years. The overcrowding issue will hinder critically-ill patients from accessing timely and adequate medical services, and may result in unnecessary deaths of emergency patients. Furthermore, it may lead to patient dissatisfaction due to the many hours of waiting for consultation. While most studies suggest that there is a mismatch between demand and supply for emergency care and this is the primary factor for the phenomenon, reducing system inefficiency is a possible way to relieve the overcrowding situation when the demand and supply are not adjustable. In this paper, we study the impacts of missing patients, referring to the patients who are not present at the time that they are called for consultation. We conduct a real case study and a simulation study of an emergency department in Hong Kong. We found that even if there is only a small proportion of missing patients and their missing time is short, there is a significant increase in patient waiting time. We suggest that emergency departments should consider to adopt information technology to reduce the inefficiency due to missing patients.