Fraud in medical billing is a significant concern for medical practitioners, healthcare workers, insurers, and patients. Medical fraud involves illicit activities such as billing for a service not provided, upcoding, duplicate billing, etc. Not only can medical fraud lead to diminished reputations, but it can also undermine the integrity of the healthcare system. However, with the introduction of machine learning, a new way has emerged to combat and reduce instances of fraud. This article explores machine learning for fraud detection in medical billing.
Machine Learning and Fraud Detection
The advent of machine learning has created several opportunities across industries with many incorporating technological advancement where possible. Machine learning involves training algorithms to recognize patterns in data. These algorithms are constantly fed data so that they can identify irregularities that may indicate fraudulent activity.
Regarding fraud detection in medical billing, take a deeper look at the following:
Data Collection and Processing
When you deploy machine learning for fraud detection, the first step in the process entails data collection. In other words, all billing data, be it from your reliable ABA billing company or electronic health records, insurance claims, and patient data, is gathered for analysis.
Once this has been collected, the next step is to “clean” up the data, so to speak. This will involve removing duplicates, correcting errors, and handling missing values. Thereafter, the data is standardized to ensure consistency. The next step is to identify relevant variables that may help with detecting fraudulent activities, such as patient demographics, procedure and billing codes, cost of procedures, etc.
Data Analysis
This is a crucial step as the aim is to better understand patterns in data. This process includes visualizing data through histograms, scatter plots, and correlation matrices, as these then identify trends and any other issues you may have flagged. Exploratory data analysis has proven vital as it reveals important insights that some may not have previously considered.
The Algorithms
One of the benefits of using machine learning algorithms is that there are several algorithms to choose from, each one with specific strengths and weaknesses. Keep in mind that the algorithms selected depend on what is required; in this case, it is fraud detection and data analysis. Here are some common algorithms:
Supervised Learning Algorithms:
- Logic regression: Works well for binary classification tasks. In other words, it is concerned with classifying the input data into two mutually exclusive categories.
- Decision trees: These focus on non-linear relationships. In other words, it focuses on a hierarchy and branches from there.
- Random forests: This is an assembler option that gathers multiple decision trees for improved accuracy.
- Support vector machines: This one uses supervised learning models to solve complex classification, regression, and outlier detection problems.
- Neural networks: Captures large and complex patterns but requires large data sets to complete its function.
Unsupervised Learning Algorithms:
- Clustering algorithms: This groups similar scenarios together and then identifies outliers as potential instances of fraud.
- Auto-encoders: This neural-network base model learns to reconstruct input data through reconstruction errors that indicate any discrepancies.
- Isolation forests: This is an ensemble model that isolates any discrepancies by “randomly” selecting data.
Training the Algorithm
The beauty of machine learning is that it can be taught to do exactly what is required. Once you have identified the algorithm, it is then trained on the labeled data set. This includes feeding the algorithm with examples of fraud and legitimate claims. The idea is for the algorithm to understand both aspects so that it can identify the anomalies.
For the training to be effective, the data must be split into training and validation sets. The training is used to train the model, while the validation sets evaluate performance. Then, there is cross-validation, which simply means that the system uses a technique called k-fold cross-validation (a technique for evaluating predictive models in data science) to ensure the model is robust. If the algorithm needs to up its performance, that is where hyperparameter tuning comes into play as it adjusts the algorithm’s parameters to improve its performance.
Detection and Analysis
Once the training is completed, the model is deployed, and it starts doing its job by analyzing new claims and billing data to detect fraud. The model then assigns a probability score to each claim, and this score shows the legitimacy of the claim. Claims with high scores are flagged for further investigation.
Some Challenges Do Exist
While the benefits of machine learning for fraud detection in medical billing far outweigh the challenges, one seems to be an issue. However, with every challenge, there is a solution. The key challenge has been the interpretability of the models. It is known that neural networks become problematic when trying to interpret why a claim was marked as fraudulent. In other words, they cannot provide a reason for the flag.
The way it can be addressed is through the Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations. These methods provide clear insights into each of the model’s predictions, allowing you to investigate and draw your conclusions.
How Machine Learning Can Solve Medical Billing Issues
If you still need motivation to adopt machine learning, understand that medical facilities face numerous challenges, most notably billing fraud, billing errors, fraudulent claims, and inefficient billing practices. Machine learning offers one of the best solutions since it analyzes large amounts of data to identify said issues and provides a solution for them.
Think about it this way, algorithms have been trained to identify trends in upcoding, duplicate billing, and even phantom chargers, which in turn leads to a reduction in fraudulent claims. Additionally, machine learning streamlines the coding process and goes as far as to suggest billing codes for the appropriate procedure.
Another advantage is that the model can also predict reimbursement delays since it analyzes historical data to provide a proactive solution. Lastly, by introducing machine learning at your medical facility, the overall billing process will be enhanced, saving time and effort, and thus allowing medical staff to focus on patient care.
Will Machine Learning Work in My Facility?
This powerful tool will most certainly work and provide much-needed relief to combat medical fraud. However, it is crucial to understand what the machine learning model can do and how it works. Consider the benefits, such as scanning large data sets to flag instances of fraud in real time, and how this can work in your facility.