Home Health & Hospice Week

Knowing the basics is the first step.

Artificial intelligence continues to dominate the healthcare landscape, including as a major tool oversight agencies use to target you for scrutiny. Now’s the time to get a grip on key AI terms prevalent in healthcare.

“AI” refers to computers, machines, and devices acting intelligently and performing functions similar to human beings. The healthcare industry uses AI in a plethora of ways. Most relevant for home care providers may be the way entities such as the HHS Office for Inspector General or various Medicare contractors use it to target providers for stepped-up scrutiny. But billing staff can harness AI when capturing and collecting revenue, physicians garner diagnosis and treatment data from it, medical devices utilize it to monitor patients’ health, and more.

As AI is used more and more, 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.” The Centers for Medicare & Medicaid Services and other federal agencies have been touting their data mining technology to identify potential fraudsters.

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 clinician, 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 clinician 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 and provide solutions.

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.