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Über dieses Buch

Healthcare service systems are of profound importance in promoting the public health and wellness of people. This book introduces a data-driven complex systems modeling approach (D2CSM) to systematically understand and improve the essence of healthcare service systems. In particular, this data-driven approach provides new perspectives on health service performance by unveiling the causes for service disparity, such as spatio-temporal variations in wait times across different hospitals.

The approach integrates four methods -- Structural Equation Modeling (SEM)-based analysis; integrated projection; service management strategy design and evaluation; and behavior-based autonomy-oriented modeling -- to address respective challenges encountered in performing data analytics and modeling studies on healthcare services. The thrust and uniqueness of this approach lies in the following aspects:

Ability to explore underlying complex relationships between observed or latent impact factors and service performance.

Ability to predict the changes and demonstrate the corresponding dynamics of service utilization and service performance.

Ability to strategically manage service resources with the adaptation of unpredictable patient arrivals.

Ability to figure out the working mechanisms that account for certain spatio-temporal patterns of service utilization and performance.

To show the practical effectiveness of the proposed systematic approach, this book provides a series of pilot studies within the context of cardiac care in Ontario, Canada. The exemplified studies have unveiled some novel findings, e.g., (1) service accessibility and education may relieve the pressure of population size on service utilization; (2) functionally coupled units may have a certain cross-unit wait-time relationship potentially because of a delay cascade phenomena; (3) strategically allocating time blocks in operating rooms (ORs) based on a feedback mechanism may benefit OR utilization; (4) patients’ and hospitals’ autonomous behavior, and their interactions via wait times may bear the responsible for the emergence of spatio-temporal patterns observed in the real-world cardiac care system. Furthermore, this book presents an intelligent healthcare decision support (iHDS) system, an integrated architecture for implementing the data-driven complex systems modeling approach to developing, analyzing, investigating, supporting and advising healthcare related decisions.

In summary, this book provides a data-driven systematic approach for addressing practical decision-support problems confronted in healthcare service management. This approach will provide policy makers, researchers, and practitioners with a practically useful way for examining service utilization and service performance in various ``what-if" scenarios, inspiring the design of effectiveness resource-allocation strategies, and deepening the understanding of the nature of complex healthcare service systems.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
A healthcare service system is complex in nature largely because of its interacted autonomous entities, such as patients and service providers. Partially due to this reason, a healthcare service system may exhibit certain complex phenomena, such as periodically emptying the queue for specific services, which may result in unreasonable service performance that is quite different from managers’ expectations. Therefore, proposing an approach to systematically understanding the healthcare services from a complex systems self-organizing perspective is of paramount importance in addressing many challenging healthcare service management problems, such as shortening long wait times, and figuring out the underlying reasons that account for the specific spatio-temporal patterns of wait times. This chapter first introduces a conceptual model for a healthcare service system with a brief review about its complex nature and the importance of the interactions between the factors and entities in the system. The commonly-faced healthcare service management problems in practice relating to wait times management are summarized. Then, it presents a data-driven complex systems modeling (D2CSM) approach, which includes the following four specific methods, i.e., Structural Equation Modeling (SEM)-based analysis, integrated prediction, service management strategy design and evaluation, and behavior-based autonomy-oriented modeling, to systematically understanding healthcare services and addressing the wait time management problems. Finally, this chapter introduces the profiles of the cardiac care system in Ontario, Canada, which is the research scenario in this book.
Li Tao, Jiming Liu

Chapter 2. Data Analytics and Modeling Methods for Healthcare Service Systems

Abstract
In order to better utilize healthcare services and improve wait times, we should know what factors cause long waits. How do the factors affect the wait times? How can we estimate the changes in the wait times, taking into account the dynamics of patient arrivals as well as some impact factors? What strategies can be proposed to efficiently utilize healthcare service resources and thus shorten wait times? How can we characterize the dynamics of patient arrivals and wait times? These are common questions that have long been a concern in healthcare systems. This chapter reviews the commonly employed methods to address these questions.
Li Tao, Jiming Liu

Chapter 3. Effects of Demand Factors on Service Utilization

Abstract
Although the literature has associated demand factors, such as geodemographics, with healthcare service utilization, little is known about how these factors—such as the population size, age profile, service accessibility, and educational profile—interact to influence service utilization, and thus indirectly affect wait times. This chapter presents a case study that employs the SEM-based analysis method to explore whether certain demand factors, i.e., population size, age profile, service accessibility, and educational profile, have direct or moderating effects on service utilization in cardiac care services in Ontario, Canada.
Li Tao, Jiming Liu

Chapter 4. Effects of Supply Factors on Wait Times

Abstract
Prior research shows that supply factors, such as supplier capacity, significantly affect the throughput and the wait times within an isolated unit. However, it is doubtful whether the characteristics (i.e., service utilization, capacity, throughput, and wait times) of one unit affect the wait times of subsequent units on the patient flow process. To answer this question, this chapter examines the impact of characteristics of a catheterization unit (CU) on the wait times of a cardiac surgery unit (SU), within the scenario of cardiac care in Ontario, Canada. The work presented in this chapter gives an additional example of using Structural Equation Modeling (SEM)-based analysis to explore whether and how some supply factors affect wait times in a hospital.
Li Tao, Jiming Liu

Chapter 5. Integrated Prediction of Service Performance

Abstract
Estimating the changes of patient arrivals and service performance over the mid- or long-term is a common problem faced by healthcare service managers. This chapter presents an example to show how to use our proposed integrated prediction method for predicting the changes of the healthcare service performance with respect to demographic shifts in the context of cardiac surgery services in Ontario, Canada. The work presented in this chapter shows that the proposed method gives a way to reasonably estimate the variations in service utilization and service performance with respect to the changes of certain factors.
Li Tao, Jiming Liu

Chapter 6. An Adaptive Strategy for Wait Time Management

Abstract
Healthcare service managers often consider how to improve service management behavior to better service performance. Commonly-faced problems include how to allocate time blocks of operating rooms (ORs) for patients who have different levels of urgency and how to schedule patients so as to shorten wait times. In this chapter, we discuss how to design adaptive strategies for time block allocations in ORs with the aim of improving service performance with respect to unpredictable patient arrivals.
Li Tao, Jiming Liu

Chapter 7. Spatio-Temporal Patterns in Patient Arrivals and Wait Times

Abstract
When regional healthcare service managers review the operations of healthcare services in a past period of time, they often feel confused about some of the unexpected spatio-temporal patterns in patient arrivals and wait times. How did these patterns emerge? What reasons and mechanisms account for the emerging patterns? How can patient arrivals be regulated at different hospitals and thus improve the service utilization in the region? In this chapter, we present how to use our proposed behavior-based autonomy-oriented modeling method to characterize the spatio-temporal patterns in cardiac surgery services in Ontario, Canada, with the aim of answering some of the aforementioned questions. The work shown in this chapter reveals the working mechanisms that explain how the spatio-temporal patterns in patient arrivals and wait times at a systems level emerge from individual patients’ hospital selection behavior and their interactions with hospital wait times. It also reveals that our proposed behavior-based autonomy-oriented modeling method is useful in finding the underlying reasons for emergent spatio-temporal patterns in complex healthcare systems.
Li Tao, Jiming Liu

Chapter 8. An Intelligent Healthcare Decision Support System

Abstract
In the previous chapters, we showed how to systematically utilize the four specific methods, i.e., Structural Equation Modeling (SEM)-based analysis, integrated prediction, service management strategy design and evaluation, and behavior-based autonomy-oriented modeling, to address practical healthcare service management problems. This chapter presents an intelligent healthcare decision support (iHDS) system that implements the four methods to develop, analyze, investigate, support, and provide advice for healthcare-related decisions. The iHDS system provides the architecture and components for user interactions, data collection and processing, data-driven inferences and simulations, and decision analytics and support to generate solutions for various healthcare analytics and decision-making problems. This chapter also describes two cases to illustrate how the iHDS system works to address practical healthcare analytics problems. One case illustrates how the components and methods work to generate adaptive solutions for allocating time blocks in operating rooms (ORs), while the other addresses the need for adaptive decision support in regional healthcare resource allocation that has the advantage of reducing healthcare performance disparities.
Li Tao, Jiming Liu

Backmatter

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