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Will AI Replace Medical Coding Careers? Unveiling 9 Reasons Why Not - Deep Dive Case Study

Updated: Nov 6, 2023


AI in medical coding demo

The Advancement of AI in Healthcare and Its Implications for Medical Coders

Can AI Take Over Medical Coders' Jobs?

In the healthcare industry, where providers handle millions of medical records, the role of medical coders is crucial for accurate documentation and coding. However, with the advent of Artificial Intelligence (AI) technology, there is growing speculation about whether AI can potentially replace medical coding careers. In this article, we delve into the question of whether AI can compete with experienced medical coders or if it can enhance the efficiency of medical coders. Before that we will see, - What is Computer assisted coding, - How does computer assisted coding work.

What is Computer Assisted Coding?

Computer-Assisted Coding (CAC) is a technology that leverages natural language processing (NLP) and artificial intelligence (AI) to help automate the medical coding process. In healthcare, coding refers to the process of translating clinical documentation, such as doctor's notes and medical reports, into a standardized code used for billing and reporting purposes. These codes are used to represent diagnoses, procedures, and other relevant information. Below is the sample medical record, listing various condition of a patient:

Medical record of a patient sample

CAC systems are designed to improve the accuracy, efficiency, and consistency of the coding process. By automating the coding process, CAC can help to reduce the risk of human error, streamline the billing process, and ensure that healthcare providers are reimbursed accurately and promptly for the services they provide.

How Does Computer Assisted Coding Work?

Computer-Assisted Coding works by using algorithms and natural language processing to analyze clinical documents and automatically assign the appropriate codes based on the information contained in the documents.

Here is a step-by-step breakdown of how it generally works:

  1. Input: The process starts with the input of clinical documents, such as electronic health records (EHRs), into the CAC system.

  2. Analysis: The CAC system analyzes the text in the documents using NLP techniques to identify and extract relevant information. This involves recognizing medical terminology and understanding the context in which terms are used.

  3. Code Assignment: Based on the analysis, the system automatically assigns the appropriate codes to the diagnoses, procedures, and other relevant information identified in the documents.

  4. Review: After the initial code assignment, a human coder reviews the codes assigned by the system to verify their accuracy and make any necessary adjustments. This step is crucial to ensure the quality and reliability of the coding process.

  5. Integration: The verified codes are then integrated into the healthcare provider's billing system, where they are used to generate bills and claims for reimbursement.

  6. Feedback Loop: Many CAC systems also include a feedback loop, where the system learns from the corrections made by human coders to improve its performance over time.

How computer assisted coding system works demo

By automating the initial code assignment process, CAC systems can significantly reduce the time and effort required to code clinical documents, while also helping to improve the accuracy and consistency of the coding process.

To provide a comprehensive analysis, we will explore the capabilities of our advanced NLP tool, Emedlogix, in meeting the demands of medical coding.

1: Efficient Handling of PDF Medical Records

Medical records are predominantly available in PDF format, requiring coders to meticulously read through various sections. As per Coding Productivity Benchmarks emphasize the importance of measuring common repetitive activities such as chart review, claims coding, claims submission, and denials appeals.

Productivity rates for medical practice coders vary across specialties. For example, orthopaedic and pain management coders exhibit the highest per-day average of claims coded at 94 and 93, respectively, while otolaryngology, urology, and gastroenterology have lower average numbers of claims coded per day.

Can AI process scanned medical records? In an average, a medical record has nearly 20 to 100 pages, Some record may go upto 1000's of pages too.

In Manual coding, coders needs to analyse the records for-

1. Valid medical record(History of present illness{HPI}, Reason for visit, Family history, Social History,LAB, review of system, physical examination, valid signature of provider, Date of Service{DOS}). 2. Medical record formats - SOAP {Subjective, Objective, Assessment, Plan}, CHEDDAR format. This is a huge herculean task of medical coders to analyse.

Our tool extracts the conditions accurately using AI, ML, and NLP.

To ensure optimum performance and efficiency, medical coders, in conjunction with Emedlogix NLP tool, can process approximately up to 250 charts in an 8-hour shift.

This helps in reducing the workload on medical coders.

2: Enhanced Accuracy in Capturing ICD-10 Codes

Inaccurate coding of family conditions, such as missing an ICD-10 code for diabetes mellitus (Z833) or colon cancer (Z800), can have a significant impact on prospective risk adjustment and medical reimbursement from Medicare.

Say for example in below picture, you can see missing certain conditions in medical records will result in lower RAF.

Risk adjustment factor score calculation demo

With extensive training and utilization of Emedlogix NLP, our tool ensures the accurate identification and population of relevant ICD codes. This allows medical coders to cross-verify charts and ensure the inclusion of appropriate codes, minimizing errors and maximizing reimbursements.

3: BMI Calculation for Accurate Risk Adjustment

BMI calculator for risk adjustment coding

BMI (Body Mass Index) plays a crucial role in risk adjustment, where deviations above or below specific thresholds can impact the RAF (Risk Adjustment Factor) score. Missing or incorrectly capturing BMI information can lead to a loss in premiums from Medicare.

Emedlogix NLP is adept at extracting BMI data, even from low-quality PDF charts, ensuring precise risk adjustment calculations.

4: Efficient Extraction of Social History Information

scoial history extraction for HCC coding

Extracting social history information, such as alcohol or drug dependence, from medical records can be time-consuming, particularly when dealing with multi-page documents.

Missing or incomplete social history descriptions can result in financial losses for providers.

In below image, you can see in a 491 page medical record, social history is located at 300th page. Medical coders needs has to go through all the pages in order to locate the social history.

Social history from medical record using CAC software

However, with Emedlogix NLP, a 60-page medical record can be analyzed and relevant social history codes can be populated on the screen within 60 to 90 seconds.

This significantly improves efficiency and accuracy, reducing the burden on medical coders.

5: Accurate Capture of Past Surgical History

Surgical history coding extraction Emedlogix

Failure to capture past surgical history, such as an amputation (e.g., resection of the right great toe Z89411), can lead to decreased provider premiums.

Say for example in below medical record, the patient has surgical history of thumb amputation[Z89019], eye surgery, cataract lens implants, right, laser surgery on both eyes.

Using CAC coding tool, only Z89411 should be taken into consideration for RAF scoring

patient medical record past surgical history in CAC coding tool

Surgical history details can be dispersed throughout medical records, making it challenging for coders to locate and code them correctly.

Leveraging the power of Emedlogix's rules engine, the tool swiftly identifies and extracts surgical history codes from charts, enabling accurate documentation and coding.

6. Searching Family history

Family history extraction in medical records is pivotal in healthcare settings. It aids in precise risk assessment, facilitating preventive care strategies. For instance, using ICD-10-CM codes like "Z80.3" (Family history of malignant neoplasm of breast) can help in early cancer screenings. Moreover, it supports accurate HCC coding, which is crucial for risk adjustment in insurance claims; for example, recognizing a family history of diabetes (ICD-10-CM code "Z83.3") can influence a patient's risk score.

Additionally, it assists in personalized treatment plans, helping healthcare providers to anticipate potential health issues and tailor interventions accordingly. Thus, family history extraction is indispensable for proactive healthcare and streamlined medical billing processes.

For example, in the below 137 page medical record, we can find family history at page 6.

family history medical record CAC software

Using CAC application like Emedlogix NLP tool, Medical coders can see the family history even in a 1000 page charts in a few seconds time, instead of running through all the pages manually, and helping healthcare providers to ascertain the patient diagnosis.

7: Keeping Pace with Code Updates


Medical coding relies on accurate and up-to-date code sets. With the constant updates in coding systems (e.g., ICD-9 to ICD-10 and upcoming ICD-11) and certain types of codes are updated annually, medical coders must continuously stay informed and trained to ensure compliance.

For instance, in ICD-9, the code for malignant neoplasms of the colon was only numeric (153.9). However, in ICD-10, it became alphanumeric (C18.9), and in the upcoming ICD-11, the code is updated to 2B90.

Staying abreast of these changes is essential for medical coders. Emedlogix NLP simplifies this process by swiftly updating codes along with HCC coding guidelines and policy updates, RADV guidelines, allowing medical coders to focus on their core responsibilities.

8: Integrated Search Functionality


Recognizing the reliance of coders on online coding tools, Emedlogix provides its own search tool. Coders can easily search for codes or descriptions within the tool itself, eliminating the need to browse external resources.

ICD 10 serach

This streamlined workflow ensures HIPAA compliance and enhances productivity.

9: Handling of Varied Chart Quality


The quality of Electronic Health Record (EHR) medical charts can vary, and handwritten charts pose additional challenges for coders.

Emedlogix NLP is equipped with trained models and Optical Character Recognition (OCR) capabilities that can accurately process charts of different quality levels, including handwritten notes.

This ensures that coders can effectively extract ICD-10 codes from a wide range of charts.

10: Security and Compliance


Maintaining the security and compliance of medical records is of paramount importance for healthcare providers. Emedlogix NLP is certified in CERTIN and ISO 27001, demonstrating its commitment to data security.

Additionally, the tool offers the option for medical charts to be processed on the client's server, further enhancing data security measures.


While concerns about the potential obsolescence of medical coding careers due to AI advancements persist, our case study demonstrates that AI, specifically Emedlogix NLP, can complement and enhance the capabilities of medical coders.

The efficient handling of PDF records, accurate identification of codes, improved risk adjustment calculations, expedited extraction of social history and surgical details, code update management, integrated search functionality, chart quality handling, and robust security measures all contribute to the continued relevance and efficiency of medical coders in the era of AI-driven healthcare.

With the integration of AI technology, medical coders can achieve higher efficiency, accuracy, and compliance while maintaining their essential role in the healthcare industry. The future of medical coding is not one of obsolescence but rather of transformation and growth.

*Note: This article is intended to provide a case study and does not encompass all aspects of AI in healthcare or medical coding. Further research and exploration are recommended for a comprehensive understanding of the topic.*



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