AI for Medical Billing and Coding

Medical billing and coding are the most crucial and challenging issues in the healthcare industry. Translating a patient’s complex symptoms and a clinician’s efforts to address them into a clear and unambiguous classification code was even more challenging in simpler times. But, at present, hospitals and healthcare professionals, as well as insurance companies, expect detailed information on what was wrong with a patient and the steps taken to treat them. It is needed for clinical record-keeping, for hospital operations review and planning, and perhaps most importantly, for financial reimbursement purposes.

Artificial intelligence is taking the world to the next level. It’s the advanced way of solving issues and aiding companies to commit to their high level of expectancy. With the help of AI, medical billers and coders can work efficiently and accurately, flagging down most of the human errors and mistakes. This article will learn about the ways AI helps and assists in medical billing and coding areas.


  • First of all, the initial stage of treatment starts with a physician or healthcare specialist appointment. Every visit of the patient has to be verified and validated by the system when it comes to automating the billing system.
  • AI analyzes the information such as the previous visits, patient demographic, insurance eligibility, billings, drug prescriptions, and payment details. The patient and hospital are informed of the eligibility of next visit based on this data.
  • One of the significant emerging technologies in AI is based on Computer-Assisted Coding. It also utilizes machine learning and Natural Language Processing.
  • The CAC automatically identifies and extracts data from documents and inserts them into the system. The automated AI solutions analyze physician documentation for the treatment and automatically recognize relevant medical codes.
  • Apart from processing codes and high data volumes, AI in medical billing and coding can also decrease the standard work hours and human errors.

AI for Medical Billing and Coding:

  • Medical codes are consistently increasing, and there’s a constant need to process as many charts as possible. It is a recipe for missed revenue and costly compliance mistakes.
  • Above all, the compounding issues presented by the Medicare Access and CHIP Reauthorization Act (MACRA) mean organizations could see negative payment adjustments for insufficient documentation.
  • There’s a lot to handle at once; several medical billing and coding companies struggle to manage their finances if they continue to rely solely on human skills.
  • Moreover, medical billing companies require an AI and ML coding solution if they hope to stay afloat in these turbulent times.
  • As discussed, the latest AI solutions for medical billing and coding collect the data from each stage of the patient visit and record the data. The newest price parameters are updated to the online system, and it automatically generates the bills as per the procedures completed.
  • The system also considers the insurance capabilities and eligibility of the patient. It keeps the insurance companies and the hospitals in the loop by notifying the concerned parties if any issues are found during the process.
  • AI also calculates and assigns the patient’s grades based on a patient’s past and current financial status.

Rapid Change in Medical Billing:

  • Few medical coders depend on computer-assisted coding (CAC) to initially find a working set of codes. Instead of reading the whole medical record to learn the story of the patient’s encounter, the computer proposes a short note of the patient’s history.
  • As a matter of fact, CAC fails to address a fundamental barrier to coding accuracy. Audits are another kind of issue. Traditionally, audits occur very late in the medical billing and coding industry.
  • In case an audit finds missed revenue after several months the bill has been paid, it might provide insights into coder accuracy. But it will be more expensive to realize the additional revenue.
  • Accuracy comes from the audit. It must happen frequently if organizations are to realize an efficient and stable revenue stream.

Artificial intelligence and machine learning will augment medical coders:

  • Artificial intelligence doesn’t replace medical coders but augments their ability to code accurately and efficiently.
  • Experienced medical coders need not spend hours each day coding simple charts when they could better focus their efforts on complex tasks that no machine could complete.
  • For example, a new coder makes a small mistake as he quickly codes a chart. In real-time, his AI assistant flags the error, recommends a solution and informs the coder of the monetary difference.
  • But, the AI assistant notification will go to a quality control reviewer. The point is to learn about the accuracy problem the same day while the case is fresh and certainly before it goes to billing.
  • Most often, accuracy is measured through post-bill audit findings. This follows the Check and Act steps of Deming’s quality improvement cycle of Plan-Do-Check-Act. If it’s too late, it costs more to realize the additional revenue.

With more codes comes more complexity:

  • The current International Standard for Medical coding ICD-10 has over 14,000 codes for diagnoses, and ICD-11 has already been formally adopted by WHO member states in May 2019.
  • It will be implemented from the month of January 2022. The new ICD-11 contains almost 55,000 diagnostic codes, four times the number of diagnostic codes in ICD-10.
  • As a matter of fact, there are several codes than the number mentioned above in the United States alone.
  • There’s no way that any human being can remember such a huge number- it is practically impossible.
  • For decades, medical coders have relied on “code books” to look up the right code for classifying a disease or treatment.
  • Over the past 20 years, the usage of computer-assisted coding systems began to increase across the healthcare industry as a means of coping with the increasing complexity of coding diagnosis and treatments.
  • Recently, machine learning and artificial intelligence occupied their place to enhance the system’s ability to analyze the clinical documentation, charts, and notes and determine which codes are relevant to a particular treatment or diagnosis.
  • Medical coders work hand in hand with AI-assisted coding systems to identify and validate the correct codes.

Artificial intelligence cannot compete with human intelligence but can do better in the medical billing and coding industry as it reduces errors and increases payments on the other hand. To learn more about AI in healthcare, visit our blog.

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