HCPro, JustCoding Outpatient - 2016 Issue 12 (March)
Post-implementation survey shows time spent coding increases for most ICD-10 record types
March 29th, 2016
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Article Overview
This article reviews survey findings on coding time and productivity after ICD-10 implementation, comparing results with earlier ICD-9 benchmarks and other published studies. It is aimed at coding professionals, HIM leaders, CDI staff, and others interested in workflow impact, benchmark comparison, and how survey methodology and record type differences affect productivity interpretation. The discussion also places the results in the context of broader documentation, auditing, and electronic record factors.
Why This Topic Matters
It helps readers understand how ICD-10 affected coding workload across settings and why productivity benchmarks may differ by record type, setting, and survey method.
Article Sections
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Survey background and respondent profile
Introduces the post-implementation survey, who responded, and the general productivity context across healthcare settings.
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Comparison of 2016 survey results with 2009 ICD-9 benchmarks
Summarizes how the more recent survey data were compared with earlier benchmark information and discusses broad factors that may influence observed changes in coding time.
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2016 ICD-10 versus 2009 ICD-9
Discusses the survey’s comparison of ICD-10 timing against prior ICD-9 results across surveyed record types and the broader themes raised by those comparisons.
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2016 ICD-10 versus 2016 ICD-9
Reviews the article’s comparison of same-year ICD-10 and ICD-9 timing results, including references to other studies and benchmark sources.
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Ambulatory surgery and other record-type considerations
Covers selected record-type survey considerations, including how some settings may involve multiple classification systems or additional abstracted data elements.
What You Will Learn
- How ICD-10 implementation affected reported coding time across different record types
- How survey results were compared with earlier ICD-9 benchmark data
- What broader workflow and documentation factors may influence coding productivity studies
- How multiple published studies and benchmark sources are discussed in relation to coding time
- How certain record-type categories can involve additional abstracted data or multiple classification systems
Who Should Read This
- Medical coders
- Coding managers and supervisors
- HIM directors and managers
- Clinical documentation improvement specialists
- Healthcare revenue cycle professionals
- Auditors and quality professionals
Codes Discussed
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