AI and the revenue cycle: Why automation should be your next core competency


Effectively managing a hospital's revenue cycle requires the completion of time-consuming, cumbersome tasks that are prone to human error. However, automation shows promise for streamlining a significant amount of this labor-intensive work.

During an Aug. 29 webinar sponsored by Olive and hosted by Becker's Hospital Review, Olive's Chief Marketing Officer Rebecca Hellmann and Senior Director of Engineering David Landreman explained how hospitals can select which workflows are best to automate and evaluate automation's return on investment.

Automation, defined

For many, the prospect of implementing automation in the revenue cycle seems daunting. Some of this hesitancy may be fueled by the uncertainty generated by buzzwords such as artificial intelligence and machine learning. To strip away some of the uncertainty, Mr. Landreman divided different types of automation into two main buckets:

  • Rule-based automations: These automations are driven by process and should be done the same way every time based on certain parameters and triggers. One example of this is Robotic Process Automation, which entails training software algorithms that mimic how an employee would complete a specific task.  
  • Judgement-based automations: These automations change based on the context they run in, similar to how artificial intelligence is able to understand data like a human would and then interact with it. Judgment-based automation could take the form of machine learning, which is a type of AI that allows software to become more accurate on its own, over time, without being programmed by a developer.

"In general, with the use of AI techniques … RPA becomes more than just rules-based automation," Mr. Landreman said. "In effect, RPA becomes a way of leveraging AI-based decision-making throughout an organization."  

Getting started with automation

Choosing which business operations to automate, though, can be a challenge. Ms. Hellmann said this challenge can be solved through a methodology created by Olive

The best workflows to automate are those marked with repetitive, high-volume, rules-based tasks — which are also more prone to human error. Eligibility checking, for example, often requires copying and pasting data as well as simple yet time-consuming interactions with predetermined applications.

To help make that decision, Ms. Hellmann suggests two approaches and a method devised by Olive. The first approach is problem-oriented — hospitals should ask themselves, "What challenges or bottlenecks are negatively impacting our organization and what are the composite workflows of those bottlenecks?" In contrast, a solution-based approach considers, "What are the strategic goals within our organization and which key performance indicators show the most potential?"

The third method is what Ms. Hellmann calls Scaling Humans with Advanced Process Engineering, or the SHAPE method. This involves creating a laundry list of all the candidate processes for automation, and subsequently ranking and rating them based on standardized criteria such as the hours of human work required, error vulnerability, potential to increase profit, actionability and volume per day.

Next, organizations must evaluate whether to buy or build an automation solution. Ms. Hellmann proposes four key questions for hospitals to consider when making this decision:

  1. Is automation one of our core competencies?
  2. How much will it cost?
  3. How long will it take?
  4. What level of expertise do we have available?

"In order to have a comprehensive view of what your automation project could look like, you'll likely want to talk to several AI solution providers," Ms. Hellmann said. "We always suggest that organizations start out by casting a wide net — doing their own research, as well as asking colleagues for their recommendations."

Automation with Olive

Ms. Hellmann described how Olive automates a common workflow: prescription prior authorization. After a new prescription order is faxed to a pharmacy, Olive uses machine learning to classify the fax type. Then, with computer vision — a type of AI that gives algorithm eyes to understand and interpret visual or text-based information — Olive is able to understand and extract relevant data. Olive then routes the prescription through the appropriate prior authorization portal and writes the determination back to a designate location before alerting necessary staff to it.

Hospitals have already realized the promises of automation with Olive. At the Heart of Ohio Family Health in Columbus, Olive has successfully taken over 90 percent of eligibility checks — saving the organization significant dollars on staffing.

But automation can generate ROI in more ways that just financial, said Ms. Hellmann. When tasks are automated, employees' time is freed up to focus on higher value tasks, which ultimately leads to greater job satisfaction.  

To access the webinar recording, click here.

To learn more about Olive, click here.

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