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    Artificial Intelligence In Medical Billing: An Introduction

    Artificial Intelligence In Medical Billing: An Introduction

    Health care providers spend an estimated 25% to 31% on administrative costs alone—and medical billing and coding make up about two-thirds of these expenditures. A 2018 study found that billing and insurance claims drained up to a quarter of practitioner revenue. And despite these heavy investments, medical coding and billing remains fraught with errors.

    A 2018 study by Tranquilmoney estimates that billing mistakes cost U.S. physicians $125 billion per year. In the United States, coding errors can also leave physicians vulnerable to fines and penalties from regulatory bodies. The Centers for Medicaid and Medicare consider excessive coding errors to be “abuse,” a pattern that can lead to bans from these programs.

    It’s no surprise that medical coding and billing are so error-prone; there are tens of thousands of individual codes in the tenth edition of the International Classification of Diseases directory (ICD-10), and many more procedures, medications, and interventions that must fit into these codes. Even an experienced coder is bound to make a mistake here or there.

    The good news is that we already have a technology that’s ideal for making the sorts of data-driven decisions involved in medical coding and billing. Artificial Intelligence (AI) is improving the process from start to finish: reducing error rates, driving down costs, and completing tasks faster than ever. Here’s how artificial intelligence, medical billing, and new approaches to coding are changing the business of health care.

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    Types Of Artificial Intelligence In Medical Billing

    Artificial intelligence is a broad term, more of an approach to problem-solving than a discrete technology on its own. The term refers to the use of computers to imitate human decision-making—and there are many types of AI. Here are some of the specific AI technologies that contribute to stronger automation in medical billing and coding processes.

    Artificial intelligence medical billing - Machine Learning

    1. Machine Learning

    Prior to the development of practical AI, software engineers had to write a rule for every operation. You can’t write rules that match every possible medical intervention with an accurate billing code. But Machine Learning (ML) enables digital systems to make these connections by studying immense datasets.

    Machine learning is a form of AI that uses algorithms to “train” software, transforming inputs into increasingly accurate outputs. That allows a digital system to learn which codes are associated with specific medical tasks, and from the best teacher available: the data itself.

    Artificial intelligence medical billing - Natural Language Processing

    2. Natural Language Processing

    In an AI-powered coding application, the key input is the clinician’s reports. These are written in human language. Natural Language Processing (NLP) is the AI technology that allows software to translate this human language into meaningful data.

    Essentially, NLP takes unstructured data (raw human language) and organizes it into structured data (a form of information that software can plug into its algorithms). With NLP, AI transforms the clinician’s notes into actionable data—data the system uses to assign accurate codes.

    Artificial intelligence medical billing - Computer Vision

    3. Computer Vision

    Digital text may appear on PDFs, within images, as plaintext, or in dozens of other formats. It’s tricky to write software that’s capable of extracting text from each of these presentations. Computer Vision (CV) is the AI technology that allows a digital system to recognize and collect relevant data, however it appears.

    With CV, an automated coding system can “read” text, or even draw meaning from images, across a broad range of digital documents. It recognizes the key data in insurer web portals, Electronic Medical Record (EMR) systems, billing software, scanned documents, and more.

    Depending on the practitioner’s Information Technology (IT) environment, one or more of these AI technologies may be necessary to automate coding and billing processes. But with all these solutions available, the future of medical coding and billing is bright.

    AI And The Future Of Medical Billing And Coding

    So far, we’ve limited our discussion to medical billing AI. But the industry is headed toward a hybrid approach that blends AI technology with another tech trend: digital automation. This combination of automation and AI is called intelligent automation or hyperautomation, and it’s where the industry as a whole is heading.

    With an intelligent automation solution like the Nividous platform, practitioners can fully automate both coding and billing processes. That creates an end-to-end system that improves accuracy, saves significant costs, and speeds up turnaround considerably.

    Many AI solutions focus on specific tasks: Generating the right code, or creating an invoice.

    But medical billing isn’t just a series of tasks. It’s a complex process that strings each step along to achieve the desired outcome. The Nividous hyperautomation platform offers everything you need to automate both coding and billing, combining three key technologies into a single experience:

    • Robotic Process Automation (RPA). RPA bots draw data from existing digital systems, operating them through user interfaces. So there’s no need for extensive coding knowledge or fresh (and costly) engineering.
    • AI including ML, NLP, and CV. Nividous Smart Bots are RPA systems that access AI components to expand capabilities as outlined above.
    • Business Process Management (BPM). To make the leap from task automation to automating end-to-end processes, the Nividous Platform includes a robust digital BPM component. This system organizes processes into a workflow diagram, and triggers each step in order to manage the entire billing cycle—including accurate coding.

    Medical coding automation doesn’t just improve accuracy; it also reduces turnaround time for the whole billing process. A human coder may take around five minutes to look up the appropriate code. A bot can complete the same task in a minute or even less, quickly referring to its database of all the medical codes.

    That already saves a lot of time. Plus, bots can work 24/7. With RPA and AI operating on an end-to-end process automation platform like Nividous, a single supervisor can complete the work of an entire coding department—leading to significant savings in both time and cost, all with measurable accuracy improvements.

    Medical Coding And Billing With Nividous

    The future of medical billing and coding has already arrived. The advantages of AI and automation aren’t hypothetical; Nividous provides them to medical practitioners across the globe every day.

    For example, a major wound care group was concerned with revenue loss associated with inaccurate coding. Company leaders saw that manual coding and billing was too slow and error-prone. They reached out to Nividous for a solution.

    The Nividous platform deployed Smart Bots to retrieve medical data from an existing EMR system. With data in hand, the bots used AI technology to generate accurate codes. They then forwarded this output to the company’s billing system.

    Intelligent automation with Nividous delivered ongoing benefits to the company, including:

    • Coding accuracy improvements of 90%
    • An 85% faster turnaround time on the billing process
    • 95% less manual work in the coding department

    Read the full case study.

    Artificial intelligence for medical billing isn’t out of reach. If your health care business would benefit from fewer coding errors, lower administrative costs, and a faster billing cycle, intelligent automation is the solution.

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