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AI in Healthcare Collections: Using Data Analytics to Forecast Repayment

No doubt you're reading many things about artificial intelligence lately, including how close it's becoming to a real HAL 9000. Despite exciting advancements, AI may seem limited in the industries it can serve. If you're in healthcare, you'll be amazed at what AI is doing today. It's not about voice-activated Alexa-like platforms helping improve healthcare management.

AI also has internal use for building reliable forecasting models. Through machine learning, these programs are already helping health care facilities deal with the problems of late payments.

Reneged payments on payment plans are obviously the bane of every health clinic's existence. Sending out late notices and never receiving payments only strains cash flow.

So how is AI helping amend this? Here's a look at how it's working.

The Value of AI in the Healthcare Industry

Going by recent statistics, you'll see just how fast the growth of AI is in healthcare and where's it's being used. Here's some interesting things to study:

  • Key areas where you'll see AI used include improving patient outcomes, improving communication with patients, reducing costs, and precision medicine.
  • Patient data-mining is where you're also seeing the biggest AI usage. An AI platform can help shape data analytics for a more complete picture on payment issues you face.

Being able to look into the future and basically "predict" patient repayment possibility can help your medical practice survive.

Becker's Hospital Review quotes various IT professionals in healthcare who predict what AI is going to do. Jehan Hamedi, Founder and CEO of Adhark in Boston notes how AI can better understand patients:

"One of the most exciting areas of potential we see in AI is in understanding people in context: who they are, what's unique about them and how to specifically influence their behavior."

Repayment Based on Demographics

Much like how you gather big data to help with patient treatments, AI can now look at demographic information to see repayment history. Many AI systems now use this to scope out unnecessary services so patients can easier pay their bills.

A company called Hindsait is at the forefront of this through an SaaS program. Using natural language processing, clinical guidelines, a knowledge base, plus predictive analytics, the program gives a score on what services are actually necessary.

As you can see, AI works on a symbiotic level where you help not only your own finances, but also the patient's.

With AI looking across a broader spectrum of demographics, you'll also see reneged payment patterns. AI expands on this by looking at things like predictive denial management.

Predicting Which Patients Get Insurance Refusals

Statistics from the last few years show a couple of eye-opening facts:

AI-compiled data analytics gives you a predictive denial report listing patients most apt to have insurance refusals. Various healthcare organizations countrywide now use some form of AI-based predictive analytics. Organizations like NYU Langone Medical Center and Atrius Health are good examples in how effectively they apply this data.

Using predictive platforms allow you to flag problem areas and receive suggestions on how to fix them. It also goes into treatment to avoid certain procedures insurance companies are less apt to cover.

Data Analytics for Treatment Type

Late this last year, The Department of Veterans Affairs partnered with a tech company called Flow Health to create an AI program addressing veteran health issues. The program designs custom health plans exclusively for veterans so they receive more targeted care.

AI-based platforms like this are a major start in improving treatment options for patients to predict what they'll pay. Through smart metrics, these programs better help you understand why a patient may or may not pay their bills.

Once again, it helps the patient because you'll create a treatment plan best designed for their budget. One of the true gifts of AI is being able to predict things before they happen. Doing so lessens burdens on your staff who have to send out late payment notices regularly.