Medicare Compliance & Reimbursement

As a Medicare provider, you may be utilizing artificial intelligence (AI) every day to track your patients’ progress, streamline and check your billing processes, and harness data for long-term practice planning. But it’s important to remember this same data is used by the feds for oversight — and enforcement.

Reminder: As part of its new “Program Integrity Enhancements to the Provider Enrollment Process,” the Centers for Medicare & Medicaid Services (CMS) plans to use AI to combat fraud and abuse before it starts (see Medicare Compliance & Reimbursement, Vol. 46, No. 1). Plus, the HHS Office of Inspector General (OIG) continues to track fraudsters through AI, and Medicare Administrative Contractors (MACs) utilize it for claims reviews, too.

That’s why it’s critical that you understand the basics and get a grip on your practice output. Read on for a quick overview of the top AI-related terms prevalent in healthcare.

What Is Artificial Intelligence?

AI refers to computers, machines, and devices acting intelligently and performing functions like human beings. There’s no doubt that AI shapes and impacts healthcare daily. For example, you might be using AI in your practice when you do the following:

• Accumulate and discern data for research and clinical trials.
• Enhance diagnosis and treatment data from its compilation.
• Monitor patients’ health via medical devices.
• Capture and collect office revenue.

As more and more products are imbibed with AI, you may want to master the fundamentals and understand the lingo. Pocket these seven baseline terms to improve your AI glossary.

1. Algorithm: Simply put, an algorithm is a set of rules or instructions that are the foundation for AI. Machines follow these mathematical sequences, build on them, and learn from them.

2. Data Mining: According to the National Institute of Standards and Technology (NIST), this “analytical process attempts to find correlations or patterns in large data sets for the purpose of data or knowledge discovery.” In healthcare, this might mean connecting the dots in clinical trials to discover a treatment or cure for a disease.

3. Internet of Medical Things: Also known as the IoMT, the Internet of Medical Things relates specifically to the array of medical devices and applications in healthcare and their connection to the internet and each other. For example, when a heart monitor collects stats on a patient and sends it electronically to the doctor, that would be an example of the utilization of both IoMT technology and AI.

4. Machine Learning: This may be the most important part of the AI puzzle because machine learning happens when algorithms are introduced and then the machine — without any further programming or information — learns from the patterns and predicts future outcomes. For providers, machine learning could be used to predict future illnesses and treatments based on a patient’s past experiences and history.

5. Natural Language Processing: Often referred to as NLP, Natural Language Processing assists machines to understand human language. When a physician dictates notes and the device uses voice recognition to document the service and offer solutions, that is an example of NLP.

6. Real-Time Health Systems: RTHS is the culmination and coordination of computer applications, devices, and EHR technology to offer healthcare advice — in real time. RTHS uses AI to quickly assess problems, provide solutions, and revolutionize the industry.

7. Robotic Process Automation: The utilization of Robotic Process Automation (RPA) allows workers to pass on repetitive, simple, and sometimes annoying work to bots, allowing healthcare workers to focus on patients.