10-10-2012, 05:37 PM
A Cloud Computing Solution for Patient’s Data Collection in Health Care Institutions
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Abstract
Existing processes for patients' vital data collection
require a great deal of labor work to collect, input and analyze
the information. These processes are usually slow and errorprone,
introducing a latency that prevents real-time data
accessibility. This scenario restrains the clinical diagnostics
and monitoring capabilities. We propose a solution to
automate this process by using “sensors” attached to existing
medical equipments that are inter-connected to exchange
service. The proposal is based on the concepts of utility
computing and wireless sensor networks. The information
becomes available in the “cloud” from where it can be
processed by expert systems and/or distributed to medical
staff. The proof-of-concept design applies commodity
computing integrated to legacy medical devices, ensuring costeffectiveness
and simple integration.
INTRODUCTION
Telemedicine allows remote diagnoses and monitoring
of patients [1]. It guarantees agility, safety, and reliability in
modern health-care institutions. There are several challenges
associated to automation in this sort of environment [2], viz:
heterogeneity of devices, protocols, and programming
interfaces; the requirement for flexible, impact-free
deployment; the requirement for easy to configure, easy to
manage, scalable and, if possible, self-adjusting systems,
and others.
We focus on the problem of patients’ vital data
collection, distribution, and processing. We suggest that
current solutions based on manual note taking are slow, time
consuming, and labor resource intensive. Besides, it
imposes an obstacle to real-time data access that curbs the
ability of clinical diagnostics and monitoring.
RELATED WORKS
There is several related works that envision similar
solutions. We analyze these works in two categories: (i)
solutions for telemedicine and automated data gathering;
and (ii) solutions for data gathering on wireless sensor
networks.
On the first group, the work in [9] proposes a solution to
monitor cardiovascular disease using personal digital
assistant (PDA) and applying Grid Computing as
technology enabler. Medical staff can access use application
in software as a service (SaaS) basis. The resulting solution
provides some requirements of our work; however, it
focuses on a different solution thus not covering how vital
data is acquired, i.e. requirement (1) in the previous section.
UbiMon [10] proposes a platform for patients'
monitoring. It applies sensors implanted in patients' bodies
to get vital data. This platform uses nodes to carry out the
acquisition, processing, and storage tasks. Nonetheless, it
does not provide a flexible, scalable and ‘on demand’ way to
handle collected data, thus failing to requirement (2).
Finally, the work in [11] provides an overview of the
utilization of pervasive computing to healthcare solutions.
The concepts presented in that paper helped us defining the
requirements and high-level architecture. Nonetheless, the
paper is not focused on concrete solutions, leaving it open to
other works to build on the introduced ideas.
For the second group, i.e., “solutions for data gathering
on wireless sensor networks”, the work in [12] introduces a
monitoring system for multiple vital signs based on mobile
devices and remote connectivity. It uses wearable sensor to
collect vital signals. However, this work focus on individual
support cases, failing to support large scale environments as
we are seeking in this work, i.e. requirement (6).
CONCLUSIONS AND FUTURE WORKS
In short, our solution delivers an integrated telemedicine
service that automates the process from data collecting to
information deliver as a computing utility. There are several
practical advantages in this implementation, such as: it
provides always-on, real-time data collecting; it eliminates
manual collecting work and possibility of typing errors, and;
it eases the deployment process, as wireless networking
means no need for cabling or other physical setup.