Central Learning Releases 1st Nationwide ICD-10 Coder Performance Data

Over 550 coders registered into Central Learning to code 1,859 cases during the September 2016 nationwide coding contest. The web-based application automatically scored ICD-10 accuracy and productivity levels using real medical record cases against a vetted, standardized answer key based on published coding guidelines. The contestants were also provided with official coding guidelines for each patient type.  Winners for each patient type were determined by calculating the highest average accuracy scores for their assigned cases.

"The contest provided the industry's first real glimpse into baseline ICD-10 accuracy and productivity for a broad range of coders nationwide and included the coding of 10 inpatient, 30 ambulatory surgery and 30 emergency department cases," mentions Peña.  "The contest measured raw knowledge of coders in an open setting," Peña explains. All participants were new to Central Learning and the selection of cases.

"The results indicate a much lower level of coder accuracy than previous findings," he adds. Earlier data analysis by Central Learning was conducted across highly-trained coding teams in a controlled, managed HIM setting. During these initial knowledge assessments, coders had already been participating in targeted education and quality improvement efforts through the Central Learning application. "However, for the coding contest the system was opened up to coders nationwide to determine real-world performance benchmarks across a new group of coding professionals," adds Peña.

In lieu of any significant payer denials or reimbursement recoupments from government and commercial auditors thus far in 2016, contest results provide an important snapshot into revenue risks due to coding quality. The following key findings were released today:

• Average inpatient coder accuracy: 55%
• Average ambulatory surgery coder accuracy: 46%
• Average emergency department coder accuracy: 33%

Two key findings were identified during initial analysis of contest results. There was a converse relationship between productivity and accuracy. Coders with high productivity levels (compared to ICD-9 productivity benchmarks) scored much lower in ICD-10 coding accuracy during the contest. It was also noted that emergency department coders are not consistently assigning external cause codes, even though information was available in the records and contest guidelines recommended use of external codes.  

Based on contest data, Central Learning advises the following steps for HIM directors and coding managers:

• Continue assessing coder knowledge in ICD-10
• Provide targeted ICD-10 education and training to address knowledge gaps
• Supplement internal coding reviews with monthly external coding audits
• Balance coding productivity and accuracy performance metrics
• Monitor coding denials from payers as they are predicted to increase in 2017

Full contest results and more detailed information is available at Booth #2104 at the AHIMA16 Convention & Exhibit or by visiting the company's website at: https://www.centrallearning.com/codingcontest/

About Central Learning:
Central Learning is a web-based application that automatically grades and scores coder skills using real cases and actual clinical documentation—versus hypothetical coding scenarios and traditional check-the-box tests. Central Learning assesses an organization's progress with ICD-10 coding quality and productivity without the need for expensive, manual, and labor intensive coding audits. For more information about Central Learning, visit the company's website at: www.centrallearning.com.

About AVIANCE Suite:
AVIANCE Suite is an integrated platform of web-based software applications that help hospitals and health systems make better revenue cycle and clinical coding decisions. The AVIANCE Suite solution includes capabilities for DNFB management, Coding Analytics, Audit Management, and Central Learning—an online coding assessment tool. With the AVIANCE Suite, healthcare finance executives receive real-time business intelligence to build stronger bottom lines, and successfully transition to value-based reimbursement.

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SOURCE Central Learning

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