Leveraging NLP/AI-based Coding Assistance to Simplify E/M Coding

Leveraging NLP/AI-based Coding Assistance to Simplify E/M Coding

The evaluation and management (E/M) coding guidelines were established in the 1995 & 1997 versions of “Documentation Guidelines for Evaluation and Management Service”. Since then, this alphanumeric coding protocol has played a pivotal role in the transformation of the healthcare reimbursement system. They have allowed healthcare institutions to document services accurately in order to monitor and manage care quality and claim reimbursements from insurance companies.

While these codes have been instrumental in classifying services in a value-based care ecosystem, they have been met with mixed reactions from the physician community. The stringent requirements and onerous procedure have burdened physicians with managing an increasing barrage of measurements that drive quality, satisfaction, and efficiency. This stress is further exacerbated due to additional responsibility to log every single aspect of patient condition and patient care in a prescribed coding format.

In 2018, the Center for Medicare and Medicaid Services (CMS), the founding body of the E/M guidelines, announced their plans to revamp the E/M coding structure in order to make them more physician friendly. And in March 2019, the CMS and the American Medical Association (AMA) jointly published the revised Current Procedural Terminology evaluation and management (CPT E/M) coding set to come into effect on 1st January 2021.

Despite the promises of the CMS and the AMA, physician and the healthcare community are still uncertain whether these guidelines will ease their coding burden and the situation remains highly complex. Technology, on the other hand, has proved to be a more effective solution to the increasing coding burden on physicians. And AI-powered computer assisted coding (CAC) is one of the most profound areas that are not only helping physicians cope with the coding pressure but improving hospitals’ entire clinical documentation process. 

Leveraging AI/ NLP based CAC to counter challenges

With the advent of value-based care models, healthcare providers have had to maintain robust clinical coding practices in order to support and manage their revenue cycle. And the introduction of ICD-10 medical classification list has left hospitals dealing with an increasingly complicated documentation workflow. This largely due to the inefficiency of legacy systems and methodologies leveraged by hospitals across the US. This is where Artificial Intelligence (AI) and Natural Language Processing (NLP) enabled computer assisted coding can help. 

AI- and NLP-powered CAC along with Natural Language Processing (NLP) use specific linguistic algorithms that can extract clinical indicators from unstructured data, such as notes and prescriptions, and assign the appropriate E/M code. Moreover, voice-based NLP can create a transcription of a dictation. From there it can identify medical data and translate them into the recommended E/M coding format. 

The successful implementation of these technologies in tandem with the modern HIM coding framework improves the efficiency of the process by reducing manual errors, cutting down physician coding burden, and reducing insurance claim denials. As a result, AI and NLP enabled CAC simplifies the overall clinical coding process, help reduce the burden on the physicians, and improve hospitals’ revenue cycle.

Advantages of ezDI CAC

The advantages of implementing an AI-powered CAC solution are numerous. However, they could prove to be a costly affair if done wrong. As the only born-in-the-cloud, AI-based, fully integrated CAC solution provider on the planet, ezDI helps hospitals prevent such a scenario. Our NLP and AI-based computer-assisted E/M coding software (ezCAC) is capable of easily navigating unstructured data sources to extract relevant information and automatically suggest accurate E/M codes.

Moreover, the built-in E/M calculator provides real-time access to all relevant information from the patient medical records and the autosuggestion of CPT codes. As a result, physicians and coders are able to perform with higher efficacy and deliver error-free coding.  

The highly advanced and intuitive AI platform can read and analyze the entire patient history as well as address essentials of documentation factors such as Chief Complaint (CC), History of Present Illness (HPI), Review of System (ROS), and Medical Decision Making (MDM). Some of the prime feature-linked benefits of ezCAC can be outlined as: 

  • Integrated platform for inpatient, outpatient, and Professional-fee coding
  • Computer-assisted Audit module for compliance
  • Autosuggestion of codes and queries through AI-assistant
  • Accurate and efficient NLP enabled coding that is CDI compliant
  • Inter-departmental collaboration to maximize process efficiency and optimize revenue cycle

Partnering with ezDI does not end with getting the technology. We take complete ownership of the implementation phase so that you can focus on providing quality care to your patients.

To learn more about ezDI’s AI-based mid-revenue cycle management solutions visit www.ezDI.com and to see the live product demo of our Clinical Documentation, Coding, Compliance/Auditing, Quality Measures, Encoder, and Enterprise Analytics request a live demo.

Hardik Kevadiya

Hardik Kevadiya

Hardik Kevadiya is a Senior Manager, Business Development and Marketing at EZDI, Inc. - an AGS Health Company. Hardik is passionate about Healthcare Information Technology and Innovations - Innovations that enhance human lives. He is also a healthcare trend and news follower, his passion for helping Healthcare IT professionals in all aspects of online trends and research flows through in the expert industry coverage he provides. In addition to writing for EZDI, Hardik also mentors a team of Data Analysts and Marketing professionals.

Subscribe to our Newsletter