Many EHRs come with automated evaluation and management (E/M) coding tools, the suggest a level of service to a provider either during or at the end of an encounter. Many of the systems that have been reviewed by the author have not met a standard that allows them to be used reliably. In addition, there is no formal testing process or certification for automated coding tools within EHRs. This could put users at risk if they rely on their automated E/M coding tools. They may be entering codes that are above or below what is justified by medical necessity and documentation.
In general, EHR coding engines do not have the level of sophistication to capture nuances in the history, physical exam, and complexity of medical decision-making sections of the medical record. This results in a suggested code that may be lower than the level that would be accurate based on a review. If this is occurring, it can lead to significant revenue loss for the practice. For example, choosing an E/M code is one level lower than would be justified by medical necessity 3 to 4 times per day would lead to approximately $25,000 loss in annual income. This is a relatively common scenario, likely affecting the majority practices in the U.S.
Template driven documentation weighted towards default negative findings may also bring in elements of the history and physical examination that may not have been addressed during the encounter. This can lead to coding levels that are based on documentation of findings that were not actually captured during the encounter.
This can be addressed by engaging a third party with extensive coding and EHR coding engine development experience to evaluate your documentation, coding and EHR coding tools. In many instances, this will result in significant and justified revenue benefits for the practice. It reduces the risk of negative audits that could lead, in our experience, to accusations of fraud.
A structured and detailed process has been developed for evaluating EHR automated coding tools. This approach identifies strengths and weaknesses in the coding engine. Feedback is provided to the practice that will allow them to address potential limitations in their coding engine. Providers can then be aware of situations where the coding engine does not suggest accurate codes. A key component of this process is modification of templates, macros and other text entry methods used by the practice. We recommend this be prefaced by an audit that will allow for the identification of common EHR generated coding errors.
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