Enhancing Payment Integrity With AI To Eliminate Surprise Bills

AI Reduces Surprise Bills

It’s common for people to feel surprised by the cost of hospital, medical imaging, or outpatient surgery bills. Unfortunately, it’s difficult for them to verify whether the bill is accurate or not. This is because incorrect medical bills have become increasingly common. To address this issue, organizations are turning to technologies such as artificial intelligence (AI) and machine learning to help reduce healthcare costs.

Various organizations like Healthcare Organizations, Medical Schools and Government agencies have identified several factors contributing to the problem of high healthcare costs. Some of these include:

– A majority of medical bills, specifically 80%, contain errors.
– 25% of claims for skilled nursing facilities are overpaid.
– Hospitals incur a cost of $68 billion due to billing errors.
– On average, bills exceeding $10,000 an error of $1300
– A Texas laboratory charged more than $2000 for a single Covid-19 test.

Paying claims accurately and promptly is crucial, but unfortunately, up to 30% of claims are paid incorrectly each year, leading to over $750 billion in waste. The use of multiple outdated technologies, constantly changing regulations, and member turnover all contribute to the difficulty healthcare payors face in identifying errors, resulting in incorrect payments, improper reimbursements, or payments for claims that shouldn’t be paid at all.

To reduce waste and increase payment accuracy, payors and providers need to put more effort into correcting and recovering claims efficiently. However, finding the necessary time and resources can be challenging. One way healthcare organizations are addressing these issues is by using technology to establish payment integrity and combat the combination of errors, waste, and fraud that cause significant losses for patients, providers, and payors.

AI and Machine Learning For Payment Integrity

AI is often associated with self-driving cars, but it’s also responsible for the movies recommended by Netflix, the content displayed on social media feeds, spell-check in messaging apps, Google search results, smart home devices, bank alerts, voice assistants like Siri and Alexa, music streaming services, and much more.

In healthcare, AI and machine learning can play a significant role in achieving universal payment integrity across the industry. These technologies are already being used to help achieve this goal. With payment integrity, payors can be confident that the bills they receive from providers accurately reflect the actual cost of care for each individual.

AI is designed to simulate human intelligence, enabling computers to reason and make decisions like humans. Machine learning, a subset of AI, uses mathematical models and algorithms that allow computers to learn without specific instructions. When applied to medical billing, AI and machine learning can help reduce human error or abuse by flagging unusual severity indices or payment anomalies where claims don’t match past experience or coding/billing rules.

The Appropriate Severity of Illness Index:

The severity of illness index is used by healthcare providers to determine appropriate treatments and potential outcomes by assigning a level of illness for each event where resources are consumed. For example, poison ivy or a twisted ankle could be Severity 1, while a broken leg could be Severity 5. In recent years, the severity numbers assigned in medical billing have appeared to increase, likely because higher severity numbers result in higher payments for care.

Machine learning can assist payors in evaluating the severity index numbers submitted with claims to ensure they are accurate and consistent with past knowledge. Previously, a human would need to review a claim to identify any issues. However, machine learning can identify these problems more quickly and accurately than a claims reviewer, and determine the correct severity index to ensure payment reflects the actual cost of care.

Identifying Payment Irregularities

If a payor receives a set of similar claims but one appears different from the rest, AI can flag it and alert the payor to review and modify it for greater accuracy. For instance, a patient may be billed for receiving twenty doses of the same vaccine, which would be an unusual data point that machine learning could detect. The payor could then request a corrected invoice. Similarly, there may be two CPT codes on a patient’s bill that should not be billed together due to overlap or duplication. Through anomaly detection via machine learning, these types of outliers can be identified quickly, resulting in fewer errors and lower costs.

The Lifecycle of AI in Healthcare

AI is currently being used in various areas of healthcare, including patient care, such as medical imaging, emergency room visits, and primary care. Its purpose is to aid in the faster and more accurate diagnosis of diseases and illnesses. This marks the beginning of the AI lifecycle in healthcare, which extends to payment integrity and the reduction of waste and abuse throughout the entire diagnosis, treatment, and payment process.

In medical diagnosis, AI can supplement human intelligence by applying insights gained from other cases to enhance what a single physician might be able to determine, thereby improving diagnosis accuracy and outcomes.

Improved diagnosis can lead to the identification and implementation of more effective treatment plans. This can reduce the complexity of care, ineffective or unnecessary treatments, and days of care that all contribute to a high number of medical claims and errors.

During medical management of an illness, AI-assisted processes are utilized, ranging from more accurate testing to precision therapeutics that can shorten treatment times, virtual consultations, and health monitoring that can improve outcomes.

Other applications of AI in medicine include health chatbots for diagnosis, AI-enhanced microscopes to quickly scan blood samples, identifying candidates for clinical trials, predicting fall risk, pharmaceutical development, AI-assisted surgeries, and much more. These are all ways that AI can ultimately help reduce waste and expenses beyond what is achieved through this technology applied to payment integrity.

Advance AI in Healthcare by Humans

The advantages of utilizing AI in healthcare are evident, including cost savings and enhanced outcomes. However, the speed at which AI is integrated into the healthcare industry is dependent on human decision-making. Despite the technology’s availability, individuals and institutions, both public and private, must be willing to acknowledge that long-standing healthcare practices may be less necessary than the transformation that AI can provide.