Narrowing the Communication Gaps: How to Improve Speed and Accuracy in Medical Coding

Narrowing the Communication Gaps: How to Improve Speed and Accuracy in Medical Coding

When making any decision, it’s a good idea to consider all the available information. When that decision involves a person’s health, careful evaluation of data is vital. To ensure medical professionals and healthcare providers can make well-informed decisions and deliver the highest-quality care to patients, it’s important to accurately and efficiently document patient interactions, treatments, and procedures.

A few obstacles often prevent that goal from being achieved, unfortunately.

Electronic health record templates dictate much of the information created during a patient encounter, but these fields are sometimes insufficient for recording the full picture of a patient’s health. When payers request a document review before payment or conduct an audit afterward, missing information that didn’t fit the EHR mold can lead to a delayed or rejected reimbursement, clogging a hospital’s revenue streams and making it difficult to provide care.

Accurate risk scoring is also dependent on the capture of historical diagnoses, and those diagnoses might be overlooked by a physician if not part of the current encounter. Depending on the gaps present in the patient’s medical records, diagnoses could be missing from documentation entirely. Incomplete documentation doesn’t only cost hospitals money — it threatens the foundation of healthcare decision-making and patient outcomes.

Automation Improves Accuracy

Despite the obvious importance of documentation, it’s an increasingly large burden on healthcare providers. When the ICD-10 coding classification was introduced in 2015, providers had to adopt a new set of secure coding standards and practices — standards that the Centers for Medicare and Medicaid Services changes as often as every three months.

According to a study from CMS, 80% of improper payments stem from incorrect coding, illustrating the financial magnitude of the problem. Even when improper coding is unintentional, it’s a violation of the Federal Civil False Claims Act and can lead to charges of fraud that could cost three times as much as the false claim. Fortunately, advanced tools can help alleviate the strain that coding errors place on providers — and ultimately provide better patient outcomes as well.

Documentation specialists are responsible for reviewing assigned codes and identifying supporting documentation in physician notes, but computer-assisted coding amplifies their efforts by relieving the burden of tedious chart review. As a result, coders can think more holistically and critically about how they are representing the patient on a claim and play more of an auditor role.

AI systems can also help support codes and capture more accurate risk scores by accessing historical records and connecting the dots between medications and conditions that were not fully documented. By leveraging the capabilities of natural language processing to extract clinical indicators from raw data such as a doctor’s note or the transcript of a patient encounter, AI can fill in gaps in documentation and build a more complete picture of a patient’s medical history and the procedures necessary to provide care.

Better, Faster, Cheaper

CACs allow coders to achieve the same quality and accuracy benchmarks they would on their own, but with a  22% reduction in time required, according to a study from the Cleveland Clinic. With the integration of AI into such solutions, the potential is even greater.

To help healthcare providers unlock efficiency benefits and more, EZDI combines computer-assisted coding and clinical documentation improvement into a lightning-fast interface that allows coders to work faster and smarter in every setting of care. By leveraging NLP and knowledge graphs, EZDI helps coders make sense of charts, attachments, and other unstructured but valuable data far beyond the abilities of any of its competitors. Unlocking these additional insights means that EZDI can reduce time and cost but also minimize errors and payer delays, streamlining the care delivery process and improving patient outcomes in a wide range of settings.

As the healthcare industry transitions to value-based care, it’s imperative that providers accurately document patient encounters. Fortunately, EZDI can improve the coding process while unlocking additional insights with the power of AI.



By designing a next-generation clinical NLP engine supporting advanced documentation and coding functions, EZDI turned their vision to reality. Their CAC and CDI solutions received tremendous feedback, providing system accuracy and ease of use. EZDI removes the data complexity and highlights what matters for healthcare professionals.

EZDI is a provider of AI-based mid-revenue cycle management solutions to Hospitals and Health Systems.

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