OR Manager August 2023 - 3

Editorial
www.ormanager.com
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he topic of artificial intelligence
(AI) makes headlines almost daily.
Eliminating any fatalistic doomsday
scenarios, current literature is raising
interesting points around the useability
of AI and the ethical considerations
regular users of AI should not
ignore.
T
In the healthcare space, the number
one question seems to be, Is it safe?
There are concerns about relying on
AI-and other innovative technology-
too much and not know what to do or
how to react should that technology fail.
An April 23 article from STAT News covered
such a scenario when it detailed a
message from a trauma surgeon who
worked with an AI model " designed to
predict mortality risk for patients in the
emergency department " to a data specialist.
The message read: " Does the
[AI] model take the weekend off? "
The message was sent to Steve Miff,
CEO of the Parkland Center for Clinical
Innovation, the data science arm of the
Parkland public health system in Dallas,
Texas. According to Miff, because of a
simple password issue, the AI model on
which the surgeon and the trauma team
was relying had suddenly crashed. This
tool that was " meant to help triage patients
and gauge their stability " turned
into something disruptive when it failed.
It doesn't sound as if patient care was
ever at risk-and the fix to the crash
was expedient once the problem was
discovered-but protocols were compromised,
and a leader who is not used to
making decisions without AI input might
have failed at solving the issue before
safety became a concern.
Different types of AI: ML, NLP
The applicability of machine learning
(ML) technology is multifaceted, and
healthcare is only just beginning to understand
how to use ML efficiently-
and without breaking a facility's budget.
OR Manager published a two-part series
on ML, titled " State of natural language
processing in surgery, " in the December
2022 and January 2023 issues. As
part 1 of the series explains, natural
www.ormanager.com
language processing (NLP) is a " specialized
branch of [AI] focused on the interpretation
and manipulation of humangenerated
spoken or written data " that
is able to extract data through ML.
NLP specifically is a booming market.
In healthcare and life sciences
alone, the projection is that NLP will
grow from a market size worth $1.8
billion in 2021, to $4.3 billion in 2026,
to $6.8 billion in 2028-a predicted
compound annual growth rate of 20.3%
from 2026 to 2028. NLP applicability in
the healthcare industry is varied. In surgery,
its main potential lies in speech
recognition, and how that might give OR
staff the ability to dictate notes in real
time-including inside the OR, during a
procedure-and eliminate the need for
manual data entry.
Other surgery-related uses of NLP,
according to part 2 of the OR Manager
series, include:
* support clinical decision making by
facilitating access to information
* promote more accurate billing by assisting
with billing code assignment,
ensuring a facility " receives optional
reimbursement "
* determine prior authorization for surgical
procedures
* help limited radiology staff interpret
more radiographs
* incentivize more robust research by
extracting and disseminating large
data sets.
Elsewhere in the perioperative journey,
ML has also been making waves. A
January 2023 study published by Frontiers
in Pain Research details how using
ML-based nociception level monitoring
intraoperatively resulted in patients feeling
less pain in the first 90 minutes
post-procedure.
By using ML as opposed to traditional
blood pressure and heart rate
manual monitoring to guide the OR
team on when to administer pain medication
to the patient during surgery, the
study authors showed:
* improved postanesthesia care unit
(PACU) pain scores
* fewer patients with severe pain
OR Manager | August 2023
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OR Manager August 2023

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