Preventing Healthcare Fraud with GenAI

In today’s digital era, healthcare businesses are increasingly relying on technology to improve patient care and streamline operations. However, this reliance also opens new avenues for healthcare fraud, posing a significant challenge.

In this blog, I’m going to help you understand everything about healthcare fraud and its types. Moreover, I will provide insights to help your organization effectively navigate and combat fraudulent activities, with a special focus on how Generative Artificial Intelligence (GenAI) can play a crucial role in this endeavor.

So, let’s get started!

What are Healthcare Frauds?

Defined by CMS (The Centers for Medicare and Medicaid Services), healthcare fraud refers to the intentional deception or misrepresentation perpetrated by individuals aiming to obtain unauthorized benefits within the healthcare system.

Healthcare fraud can greatly increase costs for patients, insurers, and the entire healthcare system. This is why many authorities investigate and prosecute it heavily.

This deceptive practice involves various actors, like:

• medical providers
• patients
• people who exploit the system for personal gain

Let’s understand each of the above frauds on a deeper level next!

Common Types of Healthcare Fraud

The diagram below, along with the accompanying explanation, expands on the context discussed above:

Fig: Types of Health Care Fraud

1. Fraud Committed by Medical Providers

Here are some types of fraud committed by dishonest providers that you should be aware of:

• Billing for services never provided: Using genuine patient information, sometimes obtained through identity theft, to create fake claims or add charges for services that never happened.
• Upcoding: Charging for more expensive services or procedures. It is often by falsely inflating the patient’s diagnosis to match the higher-priced treatment.
• Performing unnecessary services: Carrying out medical services solely to get insurance payments. It is commonly seen with diagnostic tests like nerve conduction or genetic testing.
• Misrepresenting treatments: Billing non-covered treatments as medically necessary ones to get insurance payments. It is often seen in cosmetic surgery scams, like billing a nose job as a deviated septum repair.
• Changing patient’s clinical records: Altering patient’s diagnosis and medical record to justify unnecessary tests, surgeries, or procedures.
• Unbundling: Charging for each step of a procedure separately instead of as a single procedure.
• Overcharging patients: Billing patients more than their required co-pay for services that have already been covered by their health plan.
• Accepting kickbacks: Taking illegal payments in return for referring patients.
• Waiving co-pays or deductibles: Claiming financial hardship to waive patient co-pays or deductibles, then overbilling the insurance carrier or benefit plan.

2. Fraud by Patients and Other Parties

Here are some types of fraud committed by patients or other individuals that you should be aware of:

• Bogus promoting: Persuading an individual to provide their health insurance identification number and other personal information to bill for services that are not provided. This may result in identity theft or enrolling them in a fake benefit plan.
• Identity theft/identity swapping: Illegal practice such as using another person’s health insurance or allowing another person to use your insurance.
• Impersonating a health care provider: Charging for health services or equipment without a license.

3. Fraud Involving Prescription Medicines

Here are some types of fraud involving prescribed medicines that you should be aware of:

• Forgery: Creating or using falsified prescriptions.
• Diversion: Using prescribed medications for unauthorized purposes, such as selling them.
• Doctor shopping: Visiting multiple providers to obtain prescriptions for controlled substances or obtaining prescriptions from medical practices engaged in unethical practices.

You might be wondering how to navigate through the numerous types of fraud.

Worry not as we’ll explore both primary and modern preventive approaches to help you and your business access crucial information.

Primary Approaches to prevent Healthcare Frauds

Here are three methods that can help you avoid healthcare frauds:

1. Healthcare Fraud and Abuse Law

I recommend familiarizing yourself with the following laws to understand and file claims accordingly:

• Federal Civil False Claim Act (FCA)
• Anti-Kickback Statue (AKS)
• Physician Self Referal Law (Stark Law)
• Criminal Health Care Fraud Statue
• Exclusion Statue
• Civil Monetary Penalties Law (CMPL)

The above Medicare and Medicaid Fraud and Abuse Laws outline the criminal, civil, and administrative penalties and remedies that the government may impose on individuals or entities that commit fraud and abuse within the Medicare and Medicaid programs.

Violations of these laws can result in a variety of consequences, including:

• Non-reimbursement of fraudulent claims
• Civil Monetary Penalties (CMPs)
• Exclusion from all federal healthcare programs
• Criminal and civil liability

The enforcement of these laws falls under the purview of various government agencies, such as:

• The U.S. Department of Justice (DOJ)
• The U.S. Department of Health & Human Services (HHS)
• The HHS Office of Inspector General (OIG)
• The Centers for Medicare and Medicaid Services (CMS)

These agencies are responsible for investigating, prosecuting, and imposing the appropriate penalties for violations of the Medicare and Medicaid Fraud and Abuse Laws.

2. HFPP

The Healthcare Fraud Prevention Partnership (HFPP) is a voluntary public-private collaborative effort aimed at identifying and mitigating healthcare fraud through the strategic sharing of data and information. This partnership brings together various stakeholders, including:

• Federal government
• State agencies
• law enforcement
• Private health insurance plans
• Healthcare anti-fraud associations

This collaborative approach can help enhance the detection, prevention, and prosecution of healthcare frauds.

3. Probe Healthcare Fraud

The Federal Bureau of Investigation (FBI) plays a crucial role in investigating healthcare fraud, which impacts both federal and private insurance programs. To combat this complex and pervasive crime, the FBI collaborates with a range of partner organizations, including:

• Federal, state, and local agencies
• Healthcare Fraud Prevention Partnership
• Insurance groups like the National Health Care Anti-Fraud Association, the National Insurance Crime Bureau, and insurance investigative units.

This multifaceted approach allows the FBI to leverage the expertise and resources of various stakeholders. This enhances the effectiveness of healthcare fraud investigations and enables a more comprehensive response to this pressing issue.

To enhance the rigidity and efficiency of these primary approaches, Generative AI has been a boon.

Keep reading to know how!

Modern Approach to Prevent Healthcare Frauds: The GenAI Impact

1.1. What makes GenAI a game changer?

As the healthcare industry faces challenges such as rising costs, increasing patient expectations, and the demand for better patient outcomes, GenAI, a transformative approach, emerges as a solution.

All stakeholders in the healthcare ecosystem, including payers, patients, providers, and other stakeholders, are involved in essential financial management tasks and accessing necessary healthcare services. Generative AI significantly simplifies these tasks by:

• Enabling the creation of new content
• Extracting insights from complex information
• Resolving challenging issues autonomously

Unlike traditional AI, which relies on predefined rules and structured data, generative AI utilizes deep learning algorithms like natural language processing techniques and predictive analytics models to interpret and generate data with human-like creativity and intuition.

1.2. How does GenAI assist healthcare payers in detecting fraud?

A major concern for healthcare payers is fraudulent activities by healthcare providers, which drain valuable resources and undermine trust in the system. Here, Generative AI provides a powerful solution by quickly and accurately analyzing vast datasets to detect subtle patterns indicative of fraudulent behavior.

The diagram below summarizes how GenAI can positively impact to prevent such frauds:

Fig 2: Usage of GenAI to prevent healthcare frauds

Note: Generative AI’s remarkable ability to learn and adapt to new fraud tactics empowers payers to fortify their defenses, minimize risks, and protect their finances.

For example, a study by the National Health Care Anti-Fraud Association (NHCAA) suggests that healthcare fraud costs the United States approximately $68 billion annually, emphasizing the critical need for robust fraud detection solutions like generative AI.

1.3. Why is Generative AI a strong asset in healthcare fraud detection?

Here are the strengths of GenAI that makes it an asset:

Understand each of the above points in detail:

• Advanced Pattern Recognition: Algorithms like machine learning and deep learning can be trained on historical data to understand normal behavior patterns. They can then compare new data in real-time to detect any deviations from these patterns, flagging them as potential anomalies.
• Anomaly Detection Models: AI techniques like statistical modeling and clustering create models to automatically identify abnormal data points or behaviors that differ significantly from expected patterns. These models can be customized for specific areas like finance, networks, or user behavior.
• Real-time Monitoring: AI-powered systems can continuously monitor incoming data streams and detect anomalies in real-time. This capability is crucial for fraud prevention as it allows for prompt identification and response to fraudulent activities. This minimizes potential losses.
• Behavioral Analysis: AI analyzes historical data to create normal behavior profiles. By comparing current behavior against these profiles, AI can identify deviations indicating fraudulent activities like identity theft.
• Risk Scoring and Decision Making: AI algorithms can assign risk scores to transactions, customers, or events based on the likelihood of fraudulent activity. These risk scores help prioritize investigation efforts and make informed decisions regarding fraud prevention measures. Examples include flagging transactions for review or blocking suspicious activities.
• Continuous Learning and Adaptation: AI models can continuously learn and adapt to new patterns and evolving fraud techniques. By incorporating feedback from fraud analysts and new data, AI systems improve their accuracy over time and stay updated with emerging fraud patterns.
• Unsupervised Learning: AI algorithms can perform unsupervised learning to discover anomalies in data without prior labeled examples. By learning the inherent structure of the data, artificial intelligence models can identify outliers or anomalies that deviate from the expected data distribution.

So, given the significant financial, administrative, and reputational risks involved, stakeholders across healthcare must prioritize and invest in effective fraud risk management practices.

In a nutshell, leveraging AI/ML for fraud detection can analyze vast datasets, identify patterns, and enhance fraud prevention efforts. Payers can benefit from these technologies by anticipating fraudulent activities and implementing collaborative tools for data analysis and investigation. This ultimately improves fraud detection and prevention capabilities.

Wish to learn more about the best digital healthcare solutions to safeguard and scale your business securely? Reach out to Nitor Infotech today!