Discussion Week 5 - NURS 6051
ligiarivera
Reply2-instructions.docx**DEADLINE: FRIDAY 4/2/2021 BY 08:00 PM EST**
INSTRUCTIONS: Respond to your colleague, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
**At least 2 references per reply, and they need to support information in the reply**
STELLA ADIMA
Top of Form
In our world today, technological innovation has become so fast grown and the world cannot do without them in terms of data collection and storage. Healthcare is one of the business fields with the highest big data potential. According to the prevailing definition, big data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies (Stefan Rüping 2019a). The technological trends drive big data: business processes are more and more executed electronically; consumers produce more and more data themselves for example in nursing and in social networks and finally ever-increasing digitalization (Stefan Rüping 201b). Big Data also is a term used for the accumulation of information regarding the health-related data from an individual such as health history and current medical issues, diagnostics results. sociodemographic, lifestyle choices, etc. This information is compiled for a target population and can be used in such ways as clinical decision making, disease management, and preventative measures (Shanthagiri 2014). For this week’s discussion post, I am going to be talking about the benefits, challenges, and mitigation strategies for using big data in a clinical setting.
When care givers are caring for patients, they must not only look at the current information rather the clinician/care giver has to look at specific detail of the patient’s current illness, health history, and cause of the disease that is being treated. One of the benefits of being able to use big data in a clinical setting is the ability to know these important details when making clinical decisions. For example, if a patient who is hypertensive and has been seeing the doctor regularly comes in to the clinic with an elevated blood pressure level, the ability to see the past data will allow the clinician to compare the data and map out the disease process. This would give the clinician the knowledge needed to form the best treatment plan (Laureate Education 2018).
There are many benefits for using big data as well as challenges. One of the challenges centers around nursing terminology. The available data can be most effective when there is a standardized language among clinicians. The lack of standardized language within the collection and storage of data presents a problem when common data cannot be used by all disciplines. If the data are not easily translated to a vocabulary that can be used across disciplines the contribution of nursing information to patient outcomes cannot be measured (Office of the National Coordinator for Health Information Technology 2017). The inability to collect, store, retrieve and/ or understand the data can lead to unsuccessful treatment for the patient. Another challenge in using big data in the clinical setting is the technological differences between facilities. It is very important that one system will or must be able to interface with another for the data to be retrieved. The inability to retrieve the appropriate information from the electronic health record leaves the clinician to treat only the current issue without knowing the past treatments or path of the disease.
The strategies for mitigating the challenge of the language barriers in utilizing big data begin with learning more about the extent of the issue. The use of standardized nursing terminologies (SNT) would enable the data to be used by a multidisciplinary team of clinicians with a more positive outcome for patients (Macieirs, Smith, Davis, Yao, Wikie, Loez, & Keenan 2018). The proper storage and dissemination of big data in the electronic medical record (EHR) is another way to mitigate the challenges of using big data in a clinical setting. The integration of nursing datasets and the analysis of the information into an EHR that can be standardized for use between facilities would enable the clinician to retrieve the information and begin the appropriate treatment (Macieirs et al 2018).
In conclusion, healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being (Pastorino, R.; De Vito, C.; Migliara et al 2019)
References
Laureate Education (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.
Macieira, T., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2018). Evidence of Progress in Making Nursing Practice Visible Using Standardized Nursing Data: A Systematic Review. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017, 1205–1214.
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Office of the National Coordinator for Health Information Technology. (2017). Standard nursing terminologies: A landscape analysis. Retrieved 03/28/2021 from
https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf
Pastorino, R.; De Vito, C.; Migliara, G.; Glocker, K.; Binenbaum, I.; Ricciardi, W.; Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. Retrieved on 03/29/2021 from
https://pubmed.ncbi.nlm.nih.gov/31738444/
Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? Online Journal of Issues in Nursing, 13(1), 1–12. doi:10.3912/OJIN.Vol13No01PPT05
Shanthagiri, V. (2014). Big Data in Health Informatics [Video file]. Retrieved 03/28/2021 from
https://www.youtube.com/watch?v=4W6zGmH_pOw.
Stefan Rüping (2015). Big data in medicine and healthcare. Retrieved on 03/29/2021 from
https://pubmed.ncbi.nlm.nih.gov/26063521/
Bottom of Form
**DEADLINE:
FRIDAY
4/2
/2021
BY
08:00
PM
EST**
INSTRUCTIONS:
Respon
d
to
y
our colleague
, by offering one or more additional
mitigation strategies or further insight into your colleagues’ assessment of big data
opportunities and
risks.
*
*
At le
ast 2 references
p
er
reply, and they need to support information in the reply
**
STELLA
ADIMA
In
our
world
today,
technological
innovation
has
become
so
fast
grown
and
the
world
cannot
do
without
them
in
terms
of
data
collection
and
storage.
Healthcare
is
one
of
the
business
fields
with
the
highest
big
data
potential.
According
to
the
prevailing
d
efinition,
big
data
refers
to
the
fact
that
data
today
is
often
too
large
and
heterogeneous
and
changes
too
quickly
to
be
stored,
processed,
and
transformed
into
value
by
previous
technologies
(Stefan
Rüping
2019a).
The
technological
trends
drive
big
data:
business
processes
are
more
and
more
executed
electronically;
consumers
produce
more
and
more
data
themselves
for
example
in
nursing
and
in
social
networks
and
finally
ever
-
increasing
digitalization
(Stefan
Rüping
201b).
Big
Data
also
is
a
term
used
for
t
he
accumulation
of
information
regarding
the
health
-
related
data
from
an
individual
such
as
health
history
and
current
medical
issues,
diagnostics
results.
sociodemographic,
lifestyle
choices,
etc.
This
information
is
compiled
for
a
target
population
and
c
an
be
used
in
such
ways
as
clinical
decision
making,
disease
management,
and
preventative
measures
(Shanthagiri
2014).
For
this
week’s
discussion
post,
I
am
going
to
be
talking
about
the
benefits,
challenges,
and
mitigation
strategies
for
using
big
data
in
a
clinical
setting.
When
care
givers
are
caring
for
patients,
they
must
not
only
look
at
the
current
information
rather
the
clinician/care
giver
has
to
look
at
specific
detail
of
the
patient’s
current
illness,
health
history,
and
cause
of
the
disease
th
at
is
being
treated.
One
of
the
benefits
of
being
able
to
use
big
data
in
a
clinical
setting
is
the
ability
to
know
these
important
details
when
making
clinical
decisions.
For
example,
if
a
patient
who
is
hypertensive
and
has
been
seeing
the
doctor
regular
ly
comes
in
to
the
clinic
with
an
elevated
blood
pressure
level,
the
ability
to
see
the
past
data
will
allow
the
clinician
to
compare
the
data
and
map
out
the
disease
process.
This
would
give
the
clinician
the
knowledge
needed
to
form
the
best
treatment
pl
an
(Laureate
Education
2018).
There
are
many
benefits
for
using
big
data
as
well
as
challenges.
One
of
the
challenges
centers
around
nursing
terminology.
The
available
data
can
be
most
effective
when
there
is
a
**DEADLINE: FRIDAY 4/2/2021 BY 08:00 PM EST**
INSTRUCTIONS: Respond to your colleague, by offering one or more additional
mitigation strategies or further insight into your colleagues’ assessment of big data
opportunities and risks.
**At least 2 references per reply, and they need to support information in the reply**
STELLA ADIMA
In our world today, technological innovation has become so fast grown and the world cannot do
without them in terms of data collection and storage. Healthcare is one of the business fields
with the highest big data potential. According to the prevailing definition, big data refers to
the fact that data today is often too large and heterogeneous and changes too quickly to be
stored, processed, and transformed into value by previous technologies (Stefan Rüping
2019a). The technological trends drive big data: business processes are more and more
executed electronically; consumers produce more and more data themselves for example in
nursing and in social networks and finally ever-increasing digitalization (Stefan Rüping
201b). Big Data also is a term used for the accumulation of information regarding the health-
related data from an individual such as health history and current medical issues, diagnostics
results. sociodemographic, lifestyle choices, etc. This information is compiled for a target
population and can be used in such ways as clinical decision making, disease management,
and preventative measures (Shanthagiri 2014). For this week’s discussion post, I am going
to be talking about the benefits, challenges, and mitigation strategies for using big data in a
clinical setting.
When care givers are caring for patients, they must not only look at the current
information rather the clinician/care giver has to look at specific detail of the patient’s current
illness, health history, and cause of the disease that is being treated. One of the benefits of being
able to use big data in a clinical setting is the ability to know these important details when making
clinical decisions. For example, if a patient who is hypertensive and has been seeing the doctor
regularly comes in to the clinic with an elevated blood pressure level, the ability to see the past
data will allow the clinician to compare the data and map out the disease process. This would give
the clinician the knowledge needed to form the best treatment plan (Laureate Education 2018).
There are many benefits for using big data as well as challenges. One of the challenges
centers around nursing terminology. The available data can be most effective when there is a
