Aerospace and Electronic Systems Magazine May 2018 - 10

Evolution of ICT for the improvement of Quality of Life
technologies are been developing to address such kinds of challenges: specifically, the IoT framework will allow smart objects
(not limited to sensors) to communicate each other, while 5G will
provide services at very low-latency, i.e., close to real-time, with
minimum power consumption (and maximum battery life).
This extensive data exchange will arise issues regarding authentication and protection of data, most often sensitive patients'
data. Suitable strategies still need to be designed and implemented:
work has been already done [6], with encryption standards developed on purpose; however, the most challenging question deals
with the policy for keys distribution within the network and at the
moment when a new node joins it. A recent and promising solution
has been suggested by [7] where common encryption key distribution is complemented with authentication using much richer information from (some) physiological signal. Standards (national, as
well as international) are also needed to allow access to data stored
in the clinical databases: access authentication as well as privacy
of patients need to be ensured.
The second kind of processing could take longer time to be
completed and, as such, a more detailed analysis could be carried
on. Nowadays, the most popular approach (or research direction) is
to acquire big data from a large number of sensors and to process
them using smart artificial intelligence algorithms, e.g. deep learning, above all. This allows to take a comprehensive set of data, i.e.,
highly descriptive, into consideration to investigate on the current
patient's health conditions. At the same time, such advanced analytics on very large dataset, possibly longitudinal, can be helpful in
shedding light on the development and progress of highly impacting diseases, such as the 7BCD.
Prediction models as well as analysis on comorbidities could
be developed with advantage for today patients as well as for the
generations of the future. Indeed, literature showed how slight
changes in habits and vitals of elderly could be recognized as proxies of arising pathologies, like AD or PD. Deep learning and other
advanced machine learning algorithms can strongly help in modelling a normal behavior and detecting those abovementioned tiny
changes, despite the high intersubject and intrasubject variability
inherently present within any biological data.
Quantitative biomarkers for the aforementioned changes
should be identified in the large available datasets and can be used
(i) to reduce the number of individuals suffering from chronic pathologies in the future (preventive medicine), (ii) to design the most
suitable clinical intervention for each individual patient (precision
medicine) especially in case of closed-loop applications and, finally, (iii) to reduce the global healthcare-related costs (HCBME).

MEASURING ICT-BASED HRQOL
As mentioned in the previous sections, ICT can be expected to
provide benefits in terms of patients' satisfaction, improvements
in their physical, cognitive, and social abilities [14], as well as for
the reduction of the economic burden for hospitals and State health
organizations.
Nevertheless, such benefits have hardly been measured in a reliable way, due to the complexity of the health-related ecosystem
involving four levels of stakeholders, a manifold domain charac10

terizing the patients' conditions, as well as the lack of standardization of the technology used in the ICT-based solutions for health.
Before to evaluate the impact of the ICT on HRQoL, a significant effort is required to provide reliable tools for evaluating the
HRQoL itself. Up to now, only qualitative methods are available.
The most important tool for such evaluation has been developed by
WHO few decades ago. Indeed, in 1991 WHO formed the WHOQoL workgroup with the aim to provide a definition of QoL and
to design tools for its quantification. The WHOQoL workgroup
supplied a comprehensive and cross-cultural questionnaire, the
WHOQoL Questionnaire, which included questions spanning a
large variety of well-being attributes [1]. The questionnaire was
self-administered to a large population of individuals, including
people suffering a variety of diseases, experiencing different severities of illness, and belonging to several cultural subgroups.
The well-known five-point Likert scale was used in the questionnaire to quantify the relevance of selected domains scanning all
the aspects of the human life, both in healthy and pathological conditions. Either four or six domains summarized the physical and
psychological health of the individual, the level of independence,
and the quality of his/her social relationships, together with other
broader aspects, e.g., the environment characteristics, and even the
personal spiritual conditions [15]. Incidentally, despite its qualitative characterization, literature assessed a good validity and reliability for the WHO Questionnaire, afterwards [2], [16], [3].
Following the WHO experience, other works provided other
parameters how to quantify the QoL in a large variety of individuals. For example, Walter and Schlapfer [17] identified 71 different
amenities that could contribute to well-being. Among others, labor,
housing, education, quality of public goods, healthcare services,
remoteness of facilities, but also crime rate and crime protection,
attractive landscapes, urban development, and atmospheric emissions have been included in the list.
Interestingly, specific outcomes related to health and diseasespecific were developed, also: the HRQoL and the disease-specific HRQoL were tested on different populations, both in case of
patients and caregivers, with particular attention to "dimensions
commonly omitted from other generic QoL measures" [3], [4].
Despite the accuracy and the detailed analysis yielded by the
WHO-QoL workgroup and the HCBMEs, the quantification of
HRQoL still remains a challenge. The broad range of attributes and
circumstances to be taken into account, as well as the rapid evolution of the society and the social and historical context ("country
in transition", see [18]) make it difficult to concisely quantify the
HRQoL. Not least, the frenetic development of ICT is deeply pervading our life, inducing further complex changes in the society
behavior.
Therefore, in the try to measure the impact of ICT on HRQoL,
ICT itself is found to strongly influence the individuals' behavior:
thus, methods for evaluating the manifold aspects of the HRQoL
ecosystem must be needed.
Besides, IoT-health is expected to significantly improve the
HRQoL; nevertheless, key features such as interoperability, security, and system integration [19] have to be ensured in order to
reduce the overall costs for IoT-health services and to allow the
spread of this new generation ICT-based healthcare.

IEEE A&E SYSTEMS MAGAZINE

MAY - JUNE 2018



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