Researchers have developed an online mapping tool designed to help stakeholders transition from ICD-9 to ICD-10 code sets, EHR Intelligence reports.
The tool was detailed in a study published on Friday in the Journal of the American Medical Informatics Association, (Murphy, EHR Intelligence, 2/16).
Background on ICD-10
U.S. health care organizations are working to transition from ICD-9 to ICD-10 code sets to accommodate codes for new diseases and procedures by Oct. 1.
In April 2014, President Obama signed into law legislation (HR 4302) that pushed back the ICD-10 compliance date until at least October 2015.
For the study, researchers from the University of Illinois at Chicago and the University of Arizona used available general equivalence mappings from 2014 to create a bidirectional map of the code sets (Boyd et al., JAMIA, 2/13). The researchers identified 36 network patterns for the transition from ICD-9 to ICD-10 code sets and arranged them into one of five categories:
- Convoluted; and
- No mapping.
The researchers identified:
- 4,127 ICD-10 codes with a clear ICD-9 translation;
- 536 class-to-subclass relationships;
- 7,478 subclass-to-class relationships;
- 57,013 convoluted relationships; and
- 669 codes with no mapping (EHR Intelligence, 2/16).
The researchers found the greatest percentage of convoluted diagnosis codes among:
- External causes of morbidity;
- External causes of injury; and
Meanwhile, blood diseases and conditions that occur in the perinatal period represented the smallest percentage of convoluted diagnosis codes.
According to the researchers, convoluted and complex codes will require more time and resources for stakeholders, but their tools are designed to help assess financial and compliance risk for such codes (Hall, FierceHealthIT, 2/13).
They wrote, “Examining the network graphs of individual ICD-10-CM diagnosis codes from the online tool can provide a quick view of the challenges facing administrators evaluating high-cost diagnoses.”
The researchers concluded that additional studies are needed to investigate how stylistic coding inconsistencies among stakeholders will affect reimbursements (EHR Intelligence, 2/16).