Prioritization of Patient Vital Signs Transmission Using Wireless Body Area Networks - E-Healthcare Systems and Wireless Communications - page 260

and on developing pervasive healthcare systems
to monitor vital signs of multiple patients.
In this chapter, we focus on providing a net-
working solution for in-hospital triage , which is
the process of prioritizing patients based on the
severity of their condition. This process facilitates
the ability of the medical team to treat as many
patients as possible when resources are insuf-
ficient for all to be treated immediately. Existing
devices for monitoring patient vital signs are
mostly wired, often depend on direct user inter-
action, have a limited analytic capability, require
manual archiving even of digital data sources,
and have limited capability to propagate data to
the next destination on the patient's path. This is
particularly critical in in-hospital settings.
Accurate and reliable monitoring of patient's
vital signs during this period is crucial for making
efficient and error-free triage decisions. During
triage, emergency service providers need to rapidly
assess the injured patient and determine the need
for trauma center care. In addition to challenges of
acquiring patient data, trauma triage is now limited
by a reliance on human interpretation of acquired
patient data, which requires the emergency ser-
vice team be adequately trained. During triage
(especially for mass casualty scenarios), this may
greatly delay the treatment of patients in critical
conditions. Using existing technology, the in-
hospital environment lacks effective methods for
prioritizing information streams, evaluating time-
dependent trends, managing incomplete data, and
providing effective alerts. Current limitations of
patient monitoring represent an important barrier
for developing improved trauma triage methods.
To seamless transfer the data when patients are
moved between different settings such as the injury
scene, the emergency department, and other loca-
tions in the hospital, wireless technologies should
be used. However, the explosive growth of wireless
technologies brings in an important problem. For
example, wireless communication and networking
devices are being deployed almost everywhere at
an incredible speed, resulting in increased spec-

trum use by a variety of heterogeneous devices,
standards, and applications. This holds especially
true for the unlicensed Industrial, Scientific, and
Medical (ISM) bands that host a number of het-
erogeneous networks such as Bluetooth, ZigBee,
IEEE 802.11b/g. Because radio waves centered
at the same frequency emitted from the wireless
devices interfere with each other, coexistence of
them has become an important issue in order to
ensure that wireless services can maintain their
desired performance requirements. For instance, in
a critical environment such as medical emergency
scenarios it is extremely important to avoid the
failure of the medical devices that may be caused
by radio frequency interference.
With an ever-increasing use of electronics
in medical devices of all kinds as well as many
wireless communication devices in medical envi-
ronments, some unforeseen problems are arising:
the interactions between the products emitting
the electromagnetic (EM) energy and sensitive
medical devices. Even the devices themselves
can emit EM energy, which can react with other
devices or products. It has been reported that
medical devices may fail to operate correctly due
to the existence of electromagnetic interference
(Silberberg, 1993).
To guarantee wireless services in such envi-
ronments, it is necessary to design a system that
can handle such interference. Existing research
on wireless healthcare systems has focused on
the design of purpose-specific one-BAN system
(Chen, Black, Khan, & Jamshaid, 2008; Dabiri,
Vahdatpour, Noshadi, Hagopian, & Sarrafzadeh,
2008) (i.e., system used on one patient), in-BAN
data processing/fusion (Li & Tan, 2008; Weisen-
berg, Cuddihy, & Rajiv, 2008), and improve-
ment of network performance metrics such as
throughput and energy efficiency (Li & Tan, 2007;
Varshney, 2008). In addition, emergency services
have been considered in (Malan, Fulford-Jones,
Welsh, & Moulton, 2004; Varshney, 2008). While
these studies have proposed solutions to access
patient healthcare data in real time, no research