Kiok Liang Teow

Analysis of Factors Causing Long Patient Waiting Time and Clinic Overtime in Outpatient Clinics

This paper is focused on the factors causing long patient waiting time/clinic overtime in outpatient clinics and how to mitigate them using discrete event simulation. A two-week period of data collection is conducted in an outpatient clinic of a Singapore government hospital. Detailed time study from patient arrival to patient departure is conducted, and the possible factors causing long patient waiting time/clinic overtime are discussed. A discrete simulation model is constructed to illustrate how to improve the clinic performance by mitigating the detected factors. Simulation and implementation results show that significant improvement is achieved if the factors are well addressed.

Queueing for Healthcare

Patient queues are prevalent in healthcare and wait time is one measure of access to care. We illustrate Queueing Theory—an analytical tool that has provided many insights to service providers when designing new service systems and managing existing ones. This established theory helps us to quantify the appropriate service capacity to meet the patient demand, balancing system utilization and the patient’s wait time. It considers four key factors that affect the patient’s wait time: average patient demand, average service rate and the variation in both. We illustrate four basic insights that will be useful for managers and doctors who manage healthcare delivery systems, at hospital or department level. Two examples from local hospitals are shown where we have used queueing models to estimate the service capacity and analyze the impact of capacity configurations, while considering the inherent variation in healthcare.

Estimating ICU bed capacity using discrete event simulation.

PURPOSE The intensive care unit (ICU) in a hospital caters for critically ill patients. The number of the ICU beds has a direct impact on many aspects of hospital performance. Lack of the ICU beds may cause ambulance diversion and surgery cancellation, while an excess of ICU beds may cause a waste of resources. This paper aims to develop a discrete event simulation (DES) model to help the healthcare service providers determine the proper ICU bed capacity which strikes the balance between service level and cost effectiveness. DESIGN/METHODOLOGY/APPROACH The DES model is developed to reflect the complex patient flow of the ICU system. Actual operational data, including emergency arrivals, elective arrivals and length of stay, are directly fed into the DES model to capture the variations in the system. The DES model is validated by open box test and black box test. The validated model is used to test two what-if scenarios which the healthcare service providers are interested in: the proper number of the ICU beds in service to meet the target rejection rate and the extra ICU beds in service needed to meet the demand growth. FINDINGS A 12-month period of actual operational data was collected from an ICU department with 13 ICU beds in service. Comparison between the simulation results and the actual situation shows that the DES model accurately captures the variations in the system, and the DES model is flexible to simulate various what-if scenarios. ORIGINALITY/VALUE DES helps the healthcare service providers describe the current situation, and simulate the what-if scenarios for future planning.

Intelligent Analysis of Acute Bed Overflow in a Tertiary Hospital in Singapore

Hospital beds are a scarce resource and always in need. The beds are often organized by clinical specialties for better patient care. When the Accident & Emergency Department (A&E) admits a patient, there may not be an available bed that matches the requested specialty. The patient may be thus asked to wait at the A&E till a matching bed is available, or assigned a bed from a different specialty, which results in bed overflow. While this allows the patient to have faster access to an inpatient bed and treatment, it creates other problems. For instance, nursing care may be suboptimal and the doctors will need to spend more time to locate the overflow patients. The decision to allocate an overflow bed, or to let the patient wait a bit longer, can be a complicated one. While there can be a policy to guide the bed allocation decision, in reality it depends on clinical calls, current supply and waiting list, projected supply (i.e. planned discharges) and demand. The extent of bed overflow can therefore vary greatly, both in time dimension and across specialties. In this study, we extracted hospital data and used statistical and data mining approaches to identify the patterns behind bed overflow. With this insight, the hospital administration can be better equipped to devise strategies to reduce bed overflow and therefore improve patient care. Computational results show the viability of these intelligent data analysis techniques for understanding and managing the bed overflow problem

Setting up a regional health system database for seamless population health management in Singapore

Objective: Understanding the health and health service utilization of the population is critical for Regional Health System’s (RHS) population health management (PHM) initiatives in Singapore. The RHS database is a collaborative effort toward developing a national architecture for healthcare utilization data across diverse clinical systems with disparate data models. This manuscript describes the setup of an RHS database which would facilitate big data analytics for proactive population health management and health services research. Materials and methods: The RHS database is a conglomeration of four isolated databases from the three RHSs. It contains linked National Healthcare Group (NHG) polyclinic visit records, specialist outpatient clinic visit records, hospital discharge records from Tan Tock Seng Hospital (TTSH), National University Hospital (NUH) and Alexandra Hospital (AH), chronic disease management system (CDMS) records and mortality records from local registries. The data linkage process was conducted using the unique identification number (NRIC) as the linking variable. The final anonymized database has multiple interconnected tables that includes patient demographics, chronic disease and healthcare utilization information. Results: Over 2.8 million patients had contact with the three RHSs from 2008 to 2013. The database facilitated risk stratification of patients based on their past healthcare utilization and chronic disease information. This database aids in understanding the cross-utilization of healthcare services across the three RHSs and can help address the challenges of setting up a distinct geographical boundary for individual RHSs. Conclusions: The RHS database has been established with the intention to support the secondary use of administrative health data in health services research and proactive PHM in Singapore.

Analysis of patient waiting time governed by a generic maximum waiting time policy with general phase-type approximations

Waiting time can affect patient satisfaction and quality of care in the emergency department (ED). Studies have shown that waiting time accounted for more than 50 % of total patient turnaround time at ED. The objective of this study is to examine a maximum waiting time policy such that patients who would experience a long wait are assumed to be processed in a threshold period. In particular, we are interested to investigate the associated factors of the policy such as new mean waiting time and the threshold period and their interaction. Under the policy, original patient waiting distribution is transformed to a piecewise distribution where one piecewise discontinuous and one piecewise continuous distributions are further investigated. Under the phase-type (PH) distribution assumption on the original waiting time, we establish closed-form expressions concerning new mean waiting time and time points of the threshold period. By fitting PH distributions to patient waiting data of an emergency department in Singapore, the factors are then estimated under various scenarios using the obtained analytical expressions. Specifically, for a given target mean waiting time, the threshold period needed in the policy is estimated. New mean waiting time is assessed with different choices of the threshold period. Analytical expressions in terms of the variance of the transformed waiting time and the threshold period are also presented.

Interactive Data Visualization to Understand Data Better: Case Studies in Healthcare System

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.