It's no secret to anyone who's read previous posts here that artificial intelligence (AI) and machine learning are a boon for revenue cycle management -- probably one of the strongest use cases for this type of technology, in fact, as reported by RevCycle Intelligence.
“Integrating artificial intelligence into an RCM solution allows us to have data driven approaches to automate each step in the process, whether it's when that first letter is going out, that first contact is happening, or that payment is going into a payment plan. Each step, there's a model supporting that decision which helps a person to take the right action at the right time,” says Greg Allen, senior director of data science and product management at Ontario Systems.
One of the issues that effective AI and machine learning can address is growing patient financial responsibility, which is at an all-time high. The Kaiser Family Foundation recently reported a 111% increase in the burden of deductibles across all covered workers. Even worse, a 2020 HealthCareInsider.com survey revealed that over half their respondents worry about out-of-pocket healthcare costs leading to household bankruptcy.
AI technology to the rescue. RevCycle goes on to report that AI technology is poised to help providers accelerate patient collections while ensuring a positive experience for patients.
“Using data to understand people is a very strong use case because when you can understand a person and their financial position, you can help them and work with them all while providing a positive experience when they are in a difficult position,” says Allen.
“Maybe it's to understand when you should be contacting someone or what kind of individualized payment plan they should be provided based on their unique situation.”
Given the difficulty of effectively creating consumer profiles to optimize the patient financial experience, AI technology -- designed to create and analyze personalized consumer profiles at scale -- is the answer.
By pulling data from disparate data sources, including third-party sources, the technology can identify patterns of behavior for every patient and direct staff to create a personalized financial experience for them, whether it be calling them in the morning because they work nights, emailing them a secure link to a digital statement because they prefer electronic communications, or even not contacting them at all because they frequently use the patient portal on their own to pay medical bills.
“From a data science perspective, if I can't understand my consumer and closely replicate the world around them, then I can't succeed,” adds Allen.
One of the hurdles for AI and machine learning adoption is data security.
A 2020 KPMG survey reveals that three-quarters of healthcare insiders worry about data security and privacy issues associated with implementation of AI and machine learning tools. However, the numbers are on the side of privacy: 86% of respondents to the survey reported they are taking steps to protect patient privacy in their AI implementation.
“That is incredibly important in order to create widespread adoption across this industry, and others, as well, because we can't have a black box solution or hide behind the veil of proprietary technology. Being able to explain what we're doing in a digestible way is another way we combat the existing lack of transparency,” Ontario Systems' Greg Allen noted.
We can see how essential AI is to the patient financial experience. In order to create a full "profile" of a patient, the healthcare industry must continue to utilize AI technologies to electronify paper-based remits and EOBs/EOPs. This not only streamlines payment processing, but also enables BI systems to incorporate the complete scope of payments for patients. In order to accomplish this task, healthcare providers, RCM companies, billers, and medical lockboxes are leveraging cloud-computing services as a means to deploy the technologies -- and also ensure that patient data is processed in a secure, HIPAA-compliant environment.