With the simultaneous rise in population, diseases, and healthcare facilities, organizations must adapt to the growing volume, variety, and complexity of data. The job of a medical coding company is becoming more specific and nuanced with time, and the amount of data being received is likewise increasing.

There are almost 70,000 terms to express information, according to MedDRA, the standard medical dictionary resource for regulatory communication and pharmaceutical medical coders. Medical coders must seek and select the most relevant code(s) for each clinical trial participant from these massive lists. Furthermore, increased use of decentralized trials in the future will generate even more unstructured data.

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The Need for Accuracy

Despite the volume and chaotic nature of available data and processes, accuracy in medical coding is always paramount in clinical trial operations. Medical codes for elements like medical history or adverse events in clinical trial participants might influence researchers’ trial revision decisions and help them collect more accurate data.

Accurate medical coding also has the ability to assist doctors in providing the highest quality treatment to patients throughout the clinical trial. These codes have a direct impact on patient care; for example, recording pharmaceutical allergies can assist physicians in preventing potentially harmful situations.

Dealing with Complexity

Apart from the 70,000 medical coding terms listed in the MedDRA, the ICD-11 that will be implemented in January 2022 by WHO member states, including the United States, will have almost 55,000 diagnostic codes, over 140,000 classification codes, and a similarly large number of treatment codes.

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No human being can recall all of the disease and treatment codes, especially since the number of codes has grown to tens of thousands over the decades. Medical coders have used “code books” to hunt up the correct code for designating a condition or treatment for decades. The procedure was obviously slowed by thumbing through a code reference book.

There are also concerns with interpretation. There are typically multiple ways to code a diagnosis or treatment with ICD-10 and previous versions of the classification scheme, and the medical coder must choose the most relevant options.

Simplifying and Advancing Medical Coding with Artificial Intelligence

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As a means of dealing with the rising complexity of coding diagnoses and treatments, the use of computer-assisted coding systems has progressively risen across the healthcare industry over the last 20 years.

More recent versions of computer-assisted coding systems have incorporated cutting-edge machine learning techniques and other aspects of artificial intelligence to improve the system’s ability to analyze clinical documentation—charts and notes—and determine which codes are applicable to a given case. Some medical coders are increasingly collaborating with AI-enhanced computer-assisted coding systems to discover and check the right codes with ease.

When it comes to billing and coding, advanced AI technology has the ability to contextualize unstructured data, compartmentalize EHR data, and connect pertinent data. To avoid coding and reporting errors, healthcare AI organizes data into a coherent timeline and makes sense of diverse records—events, diagnoses, and procedures.

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What is Artificial Intelligence Anyway?

Artificial Intelligence (AI) refers to a computer system that is capable of self-learning and doing computational tasks that would normally need human brain power. While early AI research began in the late 1950s, we have made significant progress in the recent decade.

AI analyses data and gives it meaning using advanced algorithms that grow increasingly intuitive over time, similar to how humans gain more influence and efficiency in their professions as they gain experience.

Most importantly, AI automates repetitious analyses and procedures, resulting in significant cost savings for businesses across the board. To put it another way, we’re replacing human intellect with artificial intelligence at an increasing rate.

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The Benefits of Using AI in Medical Coding

The process of reliably and quickly translating EHR data into codes takes years of experience, and the present medical coding workforce cannot keep up with the needs of the healthcare system. Enter AI.

  • For medical experts, AI automates and simplifies the process of assessing symptoms and generating objective diagnostic possibilities.
  • Backend systems can benefit from artificial intelligence. Administrative procedures such as filing claims or medical coding are important costs for health systems and sources of burnout. AI can be used to automate and optimize these processes.
  • By recognizing existing patterns and decoding links between known and new values, artificial intelligence may operate fluidly to construct understanding around new frameworks.
  • AI can conduct audits automatically, self-adjusting known values to the audit results, in addition to appropriately classifying EHR data.
  • Natural language processing (NLP) can transform physician notes into billable medical codes automatically, and researchers are developing algorithms that use historical data to predict the likelihood of a claim being refused.
  • AI technology will become more sophisticated as time goes on, narrowing margins of error and funneling income back into the healthcare system by greater accuracy.
  • Patients also gain from AI implementation in medical coding, as it ensures that they are never overcharged and can conveniently access their medical bills in a more manageable format.
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Progressing into the World of AI-Assisted Medical Coding

Artificial intelligence (AI) is playing a key role in the digital transformation of several medical coding companies and healthcare organizations as machine learning (ML) technology continues to advance. AI is becoming a more and more important tool in the medical coding industry for automated medical coding and evaluating large amounts of healthcare data in order to make accurate forecasts and judgments over time.

The need of the hour is to design AI around the specific infrastructure that it will use, instead of going for generic setups. Such specification and customization bringing the old records and new technology together in a focused environment will ensure better streamlining of operations. Medical coders, experienced and entry-level, also need to be trained in the implementation of AI in their day to day job not just through certifications but also on-the-job training by employers for effective outcomes.

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Medical coding companies and healthcare organizations must understand that stomaching the development expenditures is a means to a high-profit goal, and the resources saved by eventually making those manual processes obsolete using AI will far outweigh the resources spent.

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