CCR Innovations | Volume 1, Issue 1 - page 6

California Cancer Registry
Volume 1, Issue 1
As the Auditor for California Cancer Registry (CCR), I coordinate and conduct a variety of audits throughout the
year. The results of each audit prioritize guidelines and clarify rules that you use daily to abstract your cases.
They also offer a great opportunity to identify educational needs. Therefore, I look forward to working with all of
you as I disseminate the results of the upcoming Audits through a variety of formats including, webinars and
email announcements.
Late last year, the CCR was audited by the National Program of Cancer Registries (NPCR). The NPCR is the Cancer
Division of the Centers for Disease Control and Prevention (CDC). We received the final results of the NPCR audit
in May. I was able to analyze and write a detailed report on their findings this July. Our overall result was an
accuracy rate of 98.7%. The CCR had one of the best results of all 50 states! I will be presenting the results in
other training events over the next year, or you may be hearing of some of these results from your regional
I want to bring to your attention two major points identified through this audit process:
Text Documentation
Coding Treatment
The audit process followed by the NPCR auditors was outlined in the final report. Text was a major factor in the
NPCR auditor’s ability to code the data variables. Interestingly enough, one of the leading reasons for the
identified audit errors was the lack of text documentation in the abstract:
“The evaluator reviewed every data element related to the evaluation and its associated text for each abstract-
level case associated with each of the CCR unique patient identifiers/merged cases. If the text did not support
the data element code, the evaluator recoded the data element based on the text provided and then provided
a reason for recode to explain the new coded value. If the text was missing entirely, the evaluator recoded the
data element to “unknown” (9, 99, or 999) and provided that explanation in the Reason for Recode field.”
During the reconciliation process, my role was to provide a rationale for data field codes. Without text
documentation, I had no option then to agree with their recode as 99’s. This was hard for me because I know
that good data could have been lost. I had to remember that if I cannot verify it, how do I know the code is the
correct code? Section I.1.6.2 of Volume I does state that text is a required element of the abstract.
I understand we have a lot of data variables to complete. However, I encourage all abstractors to consciously
enter as much text as you can to cover all of the codes in the abstract, specifically, treatment information,
staging information, lymph node information, etc.
The other major point identified during the NPCR audit process was that over half of the errors were identified
in treatment fields. The radiation treatment modality field lead the data elements in the number of errors,
followed by surgery and chemotherapy summary. This is concerning because treatment related data variables
are one of the most commonly requested data variables by researchers.
I have found over the years, that in audits including treatment variables, those variables are commonly in the
top five data variables with errors. Therefore, I have begun developing training modules to address coding
treatment in an attempt to help resolve some of these coding issues. You can look for the first of these modules
during the coming months.
In the next issue, I will be discussing the results of the audit we are currently completing on Non-Small Cell Lung
Kyle’s Corner:
Kyle Ziegler, CTR
Quality Control Data Analyst
1,2,3,4,5 7,8,9,10,11
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