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.
