NLP: The Technology behind CAC
The technology commonly behind a CAC engine is known as
natural language processing. NLP is a computer process that
analyzes text and extracts implied facts as coded data. 2 Coding
professionals then review the results.
NLP results typically have a confidence factor associated with
them. The confidence factor is a rating on how likely the produced NLP code is considered accurate by a coding professional
given similar documentation.
The higher the confidence factor, the more likely a coding professional will determine the code is accurate. The lower the confidence factor, the less likely the code will be deemed accurate.
In the context of confidence levels, a false negative is an instance when a code should have been assigned by the CAC
engine but was not. This often indicates the NLP engine had
difficulty processing the documentation. One reason coding
professionals review NLP output is to identify false negatives.
Precision is the percentage of correct codes reported, and
recall is the percentage of codes found that should have been
found. 3, 4
Formal educational programs
should evaluate current curriculum
to determine competencies
necessary to work in CAC
environments.
Not all NLP engines use the same method for producing the
code; however, common methods usually consist of a mix of
dictionaries, rules, and statistical analysis. These methods allow
for the system to “learn” from previously produced results in
order to produce a more accurate result the next time it is presented with the same information.
CAC Workforce Education
Ensuring coding professionals are properly prepared to work in
automated coding environments—regardless of coding experience—is a key factor in ensuring CAC meets all the expected
organizational outcomes. The amount of detailed education required will depend on an individual’s current skill set.
Formal educational programs should evaluate current curric-
ulum to determine competencies necessary to work in CAC en-
vironments. Educators should review their coding curriculum
to ensure the following areas are included:
Those who graduate from coding programs should have
knowledge of and skills associated with these topics prior to en-
tering the workforce.
Coding Notes
Top Questions to Ask about CAC
http://journal.ahima.org
EVALUATING CAC SYSTEMS can be challenging for organizations. Visit the Journal Web site ( http://journal.ahima.org)
for ten questions to ask perspective CAC vendors about
how their technology integrates and functions. See the story
“ 10 Questions for CAC Vendors.”
CAC Capabilities
Automating the coding workflow offers organizations a number
of benefits. While results will vary with the organization, CAC
engines have common capabilities, such as coding consistency,
audit trails, and locating documentation. Demonstrating the
benefits of workflow automation will require the organization
first document its current coding processes.
A CAC engine will provide consistent code suggestions, because it generates the same suggested code given the same input. Coding professionals should validate the suggested code
since it will be suggested every time the CAC engine reads the
same input.
Audit trails allow for a coding manager to review and analyze
the data to ensure an efficient and effective coding process occurs. The trail will show who has accessed the health record and
the activity, such as adding a code. In addition, the engine typically highlights the documentation that was used to determine
a suggested code. ¢
The “CAC 2010–11 Industry and Resources Report” was sponsored by 3M, Nuance, Artificial Medical Intelligence, QuadraMed,
and Ingenix.
Notes
1. AHIMA e-HIM Work Group on Computer-Assisted Coding. “Delving into Computer-assisted Coding.” Journal of
AHIMA 75, no. 10 (Nov-Dec 2004): 48A–H.
2. AHIMA. “CAC 2010–11 Industry Outlook and Resources
Report.” 2011. Available in the AHIMA Body of Knowledge
at www.ahima.org.
3. Ibid.
4. Ibid.
Additional Reading
The July 2010 Journal of AHIMA offers more reading on CAC
technology and uses:
AHIMA. “Automated Coding Workflow and CAC Practice
Guidance.”
Dimick, Chris. “Achieving Coding Consistency.”
Peterson, Kathleen, et al. “Ready, Set, Automate: Preparing for
Automation in Coding Workflows.”
Rollins, Genna. “Lean Coding Machine: Facilities Target Pro-
ductivity and Job Satisfaction with Coding Automation.”
June Bronnert ( june.bronnert@ahima.org) is director of professional practice
resources at AHIMA.
