How AI in Medical Billing is Boosting Practice Revenues

AI in medical billing

Compared to other industries, such as manufacturing and retail, the healthcare sector has typically lagged in adopting new technologies. In our blog ‘Must Have Medical Practice Technologies to Boost Revenues,’ we talked about the essential client experience-impacting technologies for a modern medical practice ( EHR, robust practice website, patient portals, and RPM or remote patient monitoring). In continuation of the theme of tech in healthcare, let’s look at the role of artificial intelligence (AI) in medical billing and claims collections.

AI has undoubtedly made inroads into clinical settings. You must have heard of the fascinating results that AI has achieved in diagnosing diseases such as cancer, retinal disorders, and even COVID-19. But did you know that AI is also being used in medical billing and claims collection by medical billing companies and payers to improve the efficiency and accuracy of revenue cycle management (RCM)?

The highly transaction-oriented and dynamic nature of medical billing makes it one of the most cumbersome administrative aspects of managing a medical practice. As a practice grows, the only way to scale medical billing (without using technology) is to hire highly trained billing staff, which is often difficult to achieve. Many healthcare entrepreneurs and medical billing companies are investing in AI-powered medical billing software to streamline the process and reduce human dependence.

AI in medical billing has proven to improve information collection, analysis, and decision-making at every stage of the transaction-centric RCM process, thereby significantly impacting practice revenues and patient engagement. Here’s how AI is improving medical billing and revenue collection outcomes.

Role of AI in Prior Authorizations

According to the Council for Affordable Quality Healthcare, Inc. (CAQH), the cost of manual authorizations increased by $4.31 between 2018 and 2019, which makes prior authorizations the most expensive administrative process in medical billing. Automating prior authorizations can reduce this per transaction cost to $2.11 and lower processing time by four minutes. Overall, it’s estimated that fully electronic prior authorizations could reduce the administrative expenses in healthcare by billions of dollars.

The transactional nature of prior authorizations makes it the best use case for artificial intelligence in healthcare. AI in authorizations includes real-time analytics, machine learning (ML) to identify medical billing cases that require prior authorizations, checking claims statuses, and payer follow-ups.

AI helps providers know who is cleared by payers for receiving medical treatment and who is not, making it easier to make informed decisions. The real-time reports indicate where each patient is in the financial clearance process and what needs to happen next. Greater accuracies translate to fewer claims being reworked and higher revenues as every dollar possible is captured.

Role of AI in Patient Eligibility

Over the last couple of years, the U.S. government has mandated advance estimates for patient care. While buyer cost estimates may seem normal in other industries, providing this information in healthcare is highly cumbersome. A manual process of patient-cost calculation requires the practice staff to check numerous systems with limited precision. With AI-powered software, patient eligibility checks are performed in real time while considering the contracted rates and patient-specific costs. Once implemented, the AI software incorporates the practice’s patient data to further improve the accuracy of patient eligibility estimates.

Since AI-powered patient eligibility software generates accurate estimates of out-of-pocket costs before patients receive care, it gives patients better control over their medical expenses as they know what they are responsible for paying. Providers are also happy because it minimizes last-minute cancellations due to the cost of treatment. Also, the patient’s payment options can be discussed in advance, leading to higher point-of-service collections at the medical practice.

Role of AI in Claims Tracking

AI is also being leveraged to automate the tracking of outstanding insurance claims. Manual claims tracking involves a medical biller logging into multiple payer websites to check the status of submitted claims. Now RPA (robotic process automation) technology is being used to mimic the user login into the payer website. The collected claims status data is directly integrated with the practice’s medical billing software to alert the collector if a claim is denied or requires some intervention to get paid. Claims that are cleared for payment never hit the collector’s work queue.

Software companies are already using RPA technology where the robots ping or call payer firms to check on claim denial appeals and other statuses. And increasingly, the payer firms are having robots handle the response.

Some AI claims processing software also provides information on the likelihood of a claim being rejected before it is submitted to the payer; this allows the practice staff to be proactive in scrubbing claims before submission and achieving quicker turnarounds on claim submissions.

Change Healthcare and ENGINE Insights conducted a study on the role of AI in healthcare spanning 200 revenue cycles, IT, finance, and C-suite decision-makers. Nearly all respondents stated they plan to use AI in RCM pervasively over the next few years. It’s estimated that by 2023, prior authorization (68%) and payment amount/timing estimation (62%) will be the top AI applications in RCM.

Technology in RCM will be essential for medical practices to maximize revenues while minimizing costs. As a medical billing company that has worked with diverse healthcare providers for nearly two decades, we have witnessed significant improvements in revenue collections using AI automation. Connect with us today to turbocharge the medical billing and revenues at your medical practice.   



Parul Garg, CEO and co-founder of PracticeForces, has significantly contributed to the growth of over 1,000 U.S. medical practices through her expertise in medical billing and coding since the company’s inception in 2003. With a background in Computer Science and an MBA in Human Resources, her leadership and AAPC-certified coding skills have been pivotal in managing the company’s operations effectively.

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