the average required. However, the results from the per record
method indicates the coding professional has five correctly coded records, resulting in an 83.3 percent accuracy rate. Using this
method, the coder would be subject to disciplinary action.
Some prefer the per code method, believing it is more indicative of an individual’s coding acumen and reveals important
coding quality data.
Types of Data Gleaned from Coding Audits
The most frequent data provided from an audit is an overall accuracy score for the coding team. Other elements identified include:
Percentage of discharge disposition changes
Missed query opportunities
Changes to present on admission (POA) indicators
Identifying these items yields positive change throughout the
organization. For example, one hospital experienced a high
error rate in the collection of accurate discharge dispositions.
Upon review, it was discovered there was no consistent location
in the EHR where discharge disposition was collected. The clini-
cal documentation improvement (CDI) team was deployed to
ensure discharge disposition was consistently documented in
the EHR, and the error rate plummeted.
Coding audit data also reveals specific areas that coders struggle with. Depending on the issues identified, further action can
be taken, including:
Place the coding professional on 100 percent quality review
Increase the coding professional’s audit volume from five to
10 percent the following month
Conduct a focused audit for the type of case causing dif-
ficulty, such as spinal surgeries
Auditors take twice the time to review cases as coding profes-
sionals need to code them. Therefore, HIM leaders are encour-
aged to maximize every auditor finding by requesting specific
reports. HIM leaders should:
Ask auditors to provide a full narrative that specifically
Note exactly where auditors found documentation in the
record to justify the finding. For example: “In progress note
dated 1/1/17 Dr. Smith noted…”
Ask coding professionals to review findings and provide
insight for their initial code assignment before the audit report is finalized
Sleep, Creep, and Leap!
Landscapers have a saying to describe newly planted gardens
where the result of hard work is not immediately seen: “The first
year they sleep, the second year they creep, and the third year
they leap!” The adjustment to ICD- 10 has been similar.
The industry dedicated significant effort prior to October 1,
2015 and throughout the first year adjusting to new ICD-10-CM/
PCS codes and fine-tuning systems. Seeds were being planted.
In 2017, coder productivity slowly increased as the industry
crept forward with ICD- 10, bill holds decreased, and consistent
payer payments were received.
2018 will be the year that ICD- 10 coding leaps with even
greater coder productivity, accuracy, and data outcomes. Organizations will mine specific clinical data based on ICD- 10’s
granularity to track performance and identify national disease
Other predictions for how coding audits will progress in 2018
More internal auditing to ensure coding professionals are
capturing all the specificity that ICD- 10 allows
An uptick in quality audits from outside agencies to ensure
a deeper level of code accuracy
Closer scrutiny of Medicare Recovery Audit Contractor
(RAC) activity as the four contractors increase reviews and
Focus on optimization of EHRs and legacy systems to implement new coding workflows and streamline the billing
1. Alakrawi, Zahraa et al. “New Study Illuminates the Ongoing Road to ICD- 10 Productivity and Optimization.”
Journal of AHIMA 88, no. 3 (March 2017): 40-45. http://bok.
Sarah Humbert ( email@example.com) is corporate coding and compliance manager at KIWI-TEK, Inc.
How to Choose the Right
Coding Audit Method
Figure 1: Accuracy Results by Audit Method
Audit Method Total Records Reviewed Total Codes Present Total Errors Accuracy Result Accuracy Percentage
Per Record6 not applicable to final results1 5/6 83.3%
Per Code not applicable to final results 72 1 (weighted: 2) 70/72 97%
Quiz ID: Q1828903 | EXPIRATION DATE: MARCH 1, 2019
HIM Domain Area: Clinical Data Management
Article—“How to Choose the Right Coding Audit Method”
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