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www.nursingmanagement.com Nursing Management • March 2019 15

NURSING INFORMATICS

The synthesis of nursing knowledge and
predictive analytics

By Whende M. Carroll, MSN, RN-BC

A
s healthcare organizations
enter the maintenance
and optimization phases
of electronic health record

(EHR) implementation, the time
has come for us to leverage the
vast amounts of data generated
by the EHR and associated tech-
nology to improve information
sharing and deliver excellent clin-
ical care and patient experience.
The evolution from simple data
collection to aggregating, track-
ing, trending, and analyzing big
data to enhance care is in flight.
Now, the ability to use even more
advanced data manipulation tech-
niques for care planning and de-
livery is, in many cases, required
to meet the needs of modern
nursing practice.1 Through the ap-
plication of emerging technolo-
gies, such as predictive analytics
and machine learning, nurses can
add tremendous value to the fu-
ture of care delivery and opera-
tions.

Nurses as knowledge workers
Nurses are knowledge workers,
performing highly variable, fo-
cused work that involves a signifi-
cant amount of information.2 In
our daily work, we use our spe-
cialized nursing skills to compile,
sift through, and find actionable
solutions using disparate data
sources and large datasets. With
explicit knowledge of clinical sci-
ence and by applying the nursing

process and critical thinking,
nurses instinctively take discrete
data elements and organize them
into information to use in every
patient experience. The applica-
tion of our nursing knowledge
and experience, married with suc-
cessful data handling, allows us
to make critical decisions at the
point of care. The result is nurses
disseminating wisdom and the

improved application of evidence-
based practice, adding immeasur-
able value to the clinical setting
and moving toward improving
the health of populations and
communities. Through advanced
data analytics, we can use this in-
formation to our advantage and
distribute the subsequent wisdom
with greater impact.

Studies have shown that nurses
spend upwards of 50% of their
time recording and managing this
assimilated information.3 By using
acquired patient data, nurses gain
information and apply knowledge
to guide practice.4 Nursing
knowledge identifies information
and creates relationships so it can
be synthesized and formalized.2

These relationships leverage the
nurse’s ability to apply inferences
to information and make a judg-
ment to determine patient prog-
ress toward expected outcomes or
identify nursing problems and in-
terventions appropriate for the
challenge. A set of vital signs is
information; however, the inter-
pretation of that information as
abnormal indicates knowledge.5

Increasingly, new ways of using
data enhance the clinical experi-
ence by allowing nurses to make
informed, data-driven decisions.

Today’s nurses need constant
involvement in technical innova-
tion to stay current and forward-
thinking in care delivery.6 To that
end, technologic advances en-
abled through the EHR, medical
claims, patient prescription his-
tory, and digital sensor data now
allow nurses to provide more pre-
cise, higher quality, and safer care.
The application of emerging tech-
nologies enables nurses to reap
the benefits of data manipulated
though nonhuman processing, ac-
celerating and expanding nursing

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

16 March 2019 • Nursing Management www.nursingmanagement.com

NURSING INFORMATICS

knowledge generation and priori-
tizing care based on patient needs.

Applied predictive analytics
Advanced computational analysis
of healthcare data, particularly pre-
dictive analytics, can help nurses
unearth unidentified trends within
multiple sources of data. Predictive
analytics is the statistical science of
data analysis that discovers vari-
ous patterns.7 By applying compu-
tational models and analysis,
nurses can draw on historical,

present, and simulated future data
to provide actionable insights into
real-world clinical and operational
problems.8 Predictive analytics al-
lows a machine approach to refine
these data and extract hidden
value from the newly discovered
patterns to dynamically inform
data-driven decision-making so we
can know what will happen in
healthcare settings, when, and
what to do about it.9

Further robust exploration of
data is needed to harness the
power of prediction in clinical care.
The addition of advanced algo-
rithms through machine learning is
a way to guide and standardize
best practices and expedite treat-
ment. Machine learning is the
study of computer algorithms that
improve automatically through ex-
perience.10 It’s a form of artificial
intelligence that enables software
applications to become more accu-
rate in predicting outcomes with-
out being explicitly programmed.11

Machine learning methods take
historical data and compare them
with current data to predict what
will happen in the future. With
every refresh of new data from
designated sources, the machine
learns how to be more precise in
predicting.10

Predictive analytics and machine
learning in clinical care function as
“assistive intelligence.”12 Nurses’
critical thinking is still needed to
assess the clinical situation, synthe-
size the derived information to

make the best decision, and put the
decision into action. Although
human judgment is paramount to
the success of predicting trends
and identifying variation, the use
of algorithms is promising in at-
taining the best outcomes, expound-
ing on existing clinical decision
support systems, and adding a
helpful layer of precision. Look-
ing toward the future, nurses can
count on advanced technologies
to drive cutting-edge, enhanced
practices and research-based evi-
dence to the point of care to help
make the most complex clinical
decisions with a higher degree of
confidence.13

Using data for prediction
Nurses have the influence to pro-
actively adopt and expertly apply
emerging technologies, adding
value to care delivery by making
the best data-driven decisions to
improve outcomes and patient ex-
perience. Using the assistive intelli-

gence of predictive analytics and
machine learning along with nurs-
ing knowledge can keep patients
from:
• rapid deterioration. Predictive
analytics can help nurses identify
when a patient is declining by
sending a warning or risk score
based on patient-specific data,
such as vital signs and lab or ra-
diology results, along with exter-
nal data sources from sensors and
remote devices.14 A machine-
assimilated risk score, in addition

to patient assessment and presen-
tation, quickly enables nurses to
determine if the patient’s status is
indeed declining, which allows us
to begin immediate care, prevent
further deterioration, and move
the patient to a higher level of
care if needed.
• staying in the hospital for too
long or not long enough. An aggre-
gate of the patient’s demographics,
comorbidities, number of medica-
tions, and lab and vital signs val-
ues derived from the EHR can de-
termine the risk of readmission.
Understanding a patient’s risk of
rehospitalization powered by ad-
vanced analytics such as machine
learning will better enable nurses
to personalize care, discharge plan-
ning, and outpatient care needs
earlier—all factors that can prevent
rehospitalization.15 Conversely,
with predictive analytics, nurses
can recognize what may inappro-
priately lengthen a patient’s stay,
such as ineffective medication

By applying computational models and analysis, nurses can draw on historical,
present, and simulated future data to provide actionable insights into

real-world clinical and operational problems.

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

www.nursingmanagement.com Nursing Management • March 2019 17

NURSING INFORMATICS

management, missed treatments
and procedures, and not meeting
discharge criteria.
• failing to receive the best op-
tions at end of life. Predicting
mortality using machine learning
is also on the horizon. Machine
learning algorithms interpret mul-
tiple data sources, including the
EHR, medical claims, and geo-
graphic data, to discover patterns
indicating imminent mortality in
patients.16 Predictive analytics can
help nurses lead data-driven criti-
cal conversations to ensure that
patients receive appropriate care.
These knowledge-derived discus-
sions help patients and family
members consider the best care
options approaching death, in-
cluding palliative and hospice
care. Using analytics can aid
nurses to engage patients and
families with end-of-life choices
to improve quality of life.17

Into the future
The value of nursing knowledge
synthesized with predictive analyt-
ics enables the provision of evidence-
based care and the promotion of
safety, quality, and appropriate pa-
tient outcomes—the end goal of
using all health information tech-
nology. Emerging technologies,
such as predictive analytics and
machine learning, will strengthen
our ability to collect data, assimi-
late these data into information,
apply newly discovered knowl-
edge, and gain wisdom to improve
care delivery. Moving forward,
we’ll use these technologies to en-
hance EHR clinical decision sup-
port tools and help optimize oper-
ational workforce issues such as
inadequate staffing through more
precise scheduling. We’ll also de-
crease inefficiencies that hinder
caregiver satisfaction, such as
breakdowns in multidepartmental
processes and patient throughput,

and become key players in solving
the challenges of transitional care.
Harnessing the power of using
data to extract valuable patterns to
inform better decision-making
gives nurses an edge in healthcare.
We’ll collectively add influence as
we provide appropriate, evidence-
based care and advance the nurs-
ing profession. NM

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Whende M. Carroll is the founder of
Nurse Evolution and the senior editor
of the Online Journal of Nursing
Informatics.

The author has disclosed no financial
relationships related to this article.

DOI-10.1097/01.NUMA.0000553503.78274.f7

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

For this discussion, you will use information from your assigned readings, the self-paced tutorial and leaders in your organization. Discuss the following:

· How are quality outcomes measured in your organization? Describe the process of data collection, variance investigations, changes in protocols and service delivery, the implementation process, and post-implementation monitoring. Include personnel involved in each step of the process.

· Give an example of a continuous improvement project that occurred in your work area. What worked well?  What did not work well?  How could the process be improved? 

Your initial post must be posted before you can view and respond to colleagues, must contain minimum of two (2) references, in addition to examples from your personal experiences to augment the topic. The goal is to make your post interesting and engaging so others will want to read/respond to it. Synthesize and summarize from your resources in order to avoid the use of direct quotes, which can often be dry and boring. No direct quotes are allowed in the discussion board posts.

Post a thoughtful response to at least two (2) other colleagues’ initial postings. Responses to colleagues should be supportive and helpful (examples of an acceptable comment are: “This is interesting – in my practice, we treated or resolved (diagnosis or issue) with (x, y, z meds, theory, management principle) and according to the literature…” and add supportive reference. Avoid comments such as “I agree” or “good comment.”

Points: 30

Due Dates:

· Initial Post: Fri, Nov 19 by 11:59 p.m. Eastern Standard Time (EST) of the US.

· Response Post: Sun, Nov 21 by 11:59 p.m. Eastern Standard Time (EST) of the US – (the response posts cannot be done on the same day as the initial post).

References:

· Initial Post: Minimum of two (2) total references: one (1) from required course materials and one (1) from peer-reviewed references.

· Response posts: Minimum of one (1) total reference: one (1) from peer-reviewed or course materials reference per response.

Words Limits

· Initial Post: Minimum 200 words excluding references (approximately one (1) page)

· Response posts: Minimum 100 words excluding references.

Textbooks:

Sullivan (2017) Chapters 6

·

Articles:

Carol, W. (2019). The synthesis of nursing knowledge and predictive analytics.Nursing Management, 50(3), 15-17. 
https://doi.org/10.1097/01


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