The International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10) is a clinical cataloging system which went into effect on Oct. 1, 2015 containing codes with many more classification options to healthcare providers than its predecessor, ICD-9. It’s important to note the World Health Organization (WHO) owns, develops and publishes ICD codes. According to recent industry surveys, ICD-10 coding accuracy isn’t perfect, but is improving.
Even if your medical practice, hospital, or clinic feels confident in its auditing procedures, a coding audit can be beneficial to ensure that your coding practices are as close to accurate as possible. According to ICD-10 Watch, here are some tips to get the most out of coding audits:
- Set realistic expectations
- Dive more deeply into audit findings
- Monitor unspecified codes
- Validate 4th and 5th characters
- Consider the effects of computer-assisted coding (CAC)
It’s also important to be aware of the areas with the worst ICD-10 coding accuracy. According to another article in ICD-10 Watch, these are the areas with the worst accuracy:
- VOO-Y99 (external causes of morbidity)
- ROO-R99 (symptoms, signs and abnormal findings)
- S00-T88 (injury, poisoning and other external)
- Q00-Q99 (congenital malformations)
- D50-D89 (diseases of the blood and organs)
It’s important to remember that a hospital’s reimbursements are highly dependent on how accurately its coding staff assigns its codes. With the transition from ICD-9 to ICD-10 coding, healthcare institutions across the country still have a long way to go in terms of achieving greater coder accuracy. Strides in this transition can be made by implementing improved coder accuracy measurement methods and performing analysis on coding data.
Is your organization in need of better ICD-10 accuracy? Let MedPartners help you find talented candidates who can fulfill your coding needs efficiently and accurately. We also offer Coding Compliance Audit Services designed to identify deficiencies that may impact revenue and data quality by focusing not only on incorrect coding but also data abstraction errors and gaps in provider documentation.