Industry Insights from a Revenue Cycle Leader: RCM, Healthcare Technology and AI

Industry Insights from a Revenue Cycle Leader: RCM, Healthcare Technology and AI

As part of an interview series with healthcare Leaders across the country, the Olive team had the chance to interview industry leader James McCurley about the biggest challenges facing healthcare today. Previous to his current role as Vice President, Revenue Cycle, James worked in a number of finance positions at Community Medical Centers. He received a Bachelor’s degree in biomedical physics and an MBA from California State University, Fresno.

Q: To begin, tell us about your background in healthcare and what brought you to Community Medical Centers?

I’ve been with Community Medical Centers (CMC) for almost 8 years now but I started my career in science – I was a physics major at California State University, Fresno. When I graduated, I was very interested in finance because it was during the peak of the financial crisis in 2009/10. I thought I wanted to work in investment banking, so I enrolled in an MBA program as a bridge between science and business. Once I graduated from the MBA program, I realized that Wall Street wasn’t for me, and I started working at CMC as a financial analyst. I was lucky to work under the C-Suite and got involved in a lot of really cool projects with fantastic mentors. I’ve been fortunate enough to be put in a series of progressively challenging jobs at CMC, eventually leading to being the VP of Revenue Cycle.

 

Q: What advice do you have for someone trying to build a strong revenue cycle team? 

Aside from the obvious answers like hiring the right people and having the right management and training in place, the revenue cycle inherently is built around analytics and data, and I really want people in leadership positions who understand and embrace that – it should be a very quantitatively heavy profession. So, I really try to push that idea through our team and make it part of our culture. 

Second, the revenue cycle is a perfect example of when leaders really need to see the forest through the trees – in the revenue cycle that’s really important because, for example, many of the day to day functions within the revenue cycle are repetitive and time consuming, so it’s easy to get tied up in the day to day. But as a leader of this team, you need to tie those tasks and everything we do back to supporting our organization’s goals and our mission to provide a greater patient experience, all while still receiving correct reimbursement.

 

Q:  If you could eliminate one of the healthcare industry’s challenges overnight, which one would it be? 

That’s a difficult question; I think I’m going to start asking that in my own interviews! (laughs) But to answer your question, you really see in my position how big the administrative burden is – especially around prior authorizations, eligibility, etc. – and if you take that entire section of work and look at the value of those tasks, the inputs are greater than the outputs on a macro level. This is one of the big reasons why healthcare costs so much – an entire conference room full of smart people trying to figure out why a claim was denied and spending so much of their resources to do so. Those people could be better utilized doing more valuable work, and utilizing technology to offload those burdensome processes frees up time for healthcare employees to focus on tasks that require a human touch. 

 

Q: How do you see artificial intelligence and machine learning impacting your revenue cycle over the next five years? 

I think the current status on a lot of AI in revenue cycle today is heavily focusing on eligibility, claim status, and prior authorization using RPA, but the next step is for AI to take over tasks on the billing side where machine learning is utilized to make more meaningful impact to the entire billing cycle – that would be a game changer. 

For instance, the idea of how you can build machine learning models to analyze your denials data – this claim has a high probability of denial based on x,y,z, for example –  where that knowledge came from the historical data of all of your past denials. Because of opportunities like this, I think the next step for AI is going to be continued adoption on the billing side. 

Q: In your opinion, what are some of the untapped opportunities to improve patient experience today?

My particular view into patient experience is largely on the financial side of things. There is a tremendous amount of work currently being done around sending appointment reminders, enabling transportation services, and much more. But an area of opportunity I see is not having to remind patients multiple times of how much they owe or are paying for their services. Minimizing the amount of times patients are touched by the financial aspect of receiving care would tremendously improve the patient experience. 

I think it’s easy to lose sight of the basic premises of being a consumer of services, such as knowing your liability and having the ability to resolve that liability in an easy manner. Additionally, maximizing our patients’ knowledge and use of financial assistance and counseling, prior to receiving services, is essential.

 

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Frequently Asked Questions About Olive At Becker’s HIT + Revenue Cycle Conference 2019

Frequently Asked Questions About Olive At Becker’s HIT + Revenue Cycle Conference 2019

We’re back at Olive HQ after an amazing few days at Becker’s HIT + Revenue Cycle Conference, and we have to say, this was our favorite year yet. We had the chance to talk to over 300 leading healthcare executives at the Olive booth, and we’ve compiled a list of frequently asked questions to help you better understand how Olive is transforming the future of healthcare with AI.

Q: Will AI take healthcare jobs?  

One ongoing conversation about the implementation of AI stems from the fear and trepidation about whether or not AI will take human jobs. But that’s not what AI should be doing at all – AI is automating the robotic tasks bogging down our most inundated industries, so humans can focus on more meaningful, human-centric initiatives such as patient care. For example, in healthcare today, workers spend more time in front of screens than they do in front of patients – a real problem that AI can effectively solve. 

At Olive, we’ve already seen the workforce landscape fundamentally changing because AI and automation are being used to supplement the work that human employees don’t have time to get to. We call it “Shiftwork” because it’s the trajectory through which our jobs as human employees will evolve over time to more meaningful tasks with the assistance of AI. For instance, at one hospital that hired Olive, employees that previously spent their days checking claim statuses now work on quality assurance and other tasks that are more suited for a human. 

Q: How does Olive go beyond RPA? How/where is AI actually used? 

An AI-powered digital workforce goes beyond traditional RPA in three very important ways: 

  1. Not all automations a digital employee does can be performed by a RPA or a human – in many cases, a digital employee uses AI-powered deep learning techniques to accomplish far more complex tasks. 
  2. Unlike a static RPA bot, a digital workforce can learn and adapt to change their work based on new intelligence.
  3. A digital employee interacts with their managers to provide business intelligence and recommendations on improved ways to handle tasks, so they continue to generate value long after deployment. 

As you begin your journey with AI, starting with workflows that leverage less complex technologies such as Robotic Process Automation and Computer Vision provides value and return on investment quickly and helps lay a solid foundation for your long term AI strategy. That’s because, while RPA can quickly and accurately process large volumes of data, AI-powered digital employees layer artificial cognition on top of an automation, using that data to make decisions or take action with cognitive “thinking” involved.

To learn more about how Olive goes beyond RPA, read CEO Sean Lane’s perspective on the topic here.

 

Q: How do you think about vendor selection? 

Going live with your first AI or automation project should be a step towards a longer term AI strategy. And in a time when nearly every technology vendor is touting AI-enabled products and pilot programs, it can be difficult to determine which vendor is invested in your organization’s long term success. As you go into the implementation process, work with your vendor’s team to ensure all stakeholders understand the technology and level of commitment and engagement required for successful custom development of your AI technology.

Here are just a few of the key questions to ask potential vendors to help you take an impact-driven approach to AI implementation:

1) How much experience does the vendor(s) have in health systems or in healthcare?
2) What is their approach for putting healthcare data security first?
3) What is their previous experience as a partner and trusted advisor?

No two healthcare organizations are the same. And because the challenges healthcare workers face are unique – like complex software integrations, overburdened staff, shrinking margins and increasingly strict security and compliance requirements – the industry needs an AI solution made specifically for them. Read more about what to look for in the right AI & automation vendor in this full article.

Q: What are your primary use cases? How many hospitals are you deployed in?

Olive is proud to partner with more than 40 healthcare organizations made up of more than 500 hospitals, handling tasks that are high-volume and error-prone. Although Olive has deep experience and expertise in the revenue cycle, she frequently works in information technology (IT), supply chain, human resources and more!  Here are just a few of Olive’s common roles: 

  • Benefit & Verification Discovery
  • Prior Authorization Management
  • Denial & Rejection Management
  • Vendor Contract Management
  • Invoice Processing
  • Inventory Management
  • Periodic Reporting

To learn more about Olive, visit Our Solution.

 

Q: How do you price?

Olive is the only healthcare-specific artificial intelligence solution sold as a service (AIaaS) – that means one annual subscription price and an all-in-one approach to increased efficiency and improved revenue. The simplest way to think of Olive is as a digital employee – she earns an annual salary, completes a defined and pre-agreed upon job, and is expected to not only perform, but excel. 

We created AIaaS because we think the current purchasing model for AI is broken. Before AIaaS, if an organization wanted to use AI to automate something, they had to worry about licensing fees, software purchases, consultants, integrators, implementation costs, support, maintenance – not to mention the internal resources needed to manage it. And if the software they’re automating changes, or if business rules change, the customer is on the hook to pay more money to consultants and integrators to get everything working again, making it difficult to calculate the true cost and ROI of AI investments they’re considering. 

 

Q: How much involvement from my internal resources are required? 

Even when implementing a new software could be extremely beneficial in the long run, the resources required can sometimes be shocking. That’s why Olive is ushering in a new approach to technology integration – one that significantly lessens the burden on employees and resources across all departments.

When you hire Olive, our team at Olive HQ will work with you to identify the scope of work and jobs that she will complete (taking lessons from all the other Olives we have deployed at healthcare organizations similar to yours).  Next, our team will train her across those processes, ensuring she’s fully functional before she starts full time. Once live, she works autonomously to manage her ongoing responsibilities, automating routine, high-volume, error-prone tasks. Because an organization’s commitment to this work is critical to Olive’s success, during this process, there are normally two to four stakeholders being kept in the loop on Olive’s progress as four to eight subject matter experts help inform the work that Olive will be taking over. 

Q: Can I have those Olive socks?

Sorry, the Olive merch isn’t for sale… yet.

Olive AI Merch at Becker’s HIT + Revenue Cycle Conference

Industry Insights From a Leader in Artificial Intelligence

Industry Insights From a Leader in Artificial Intelligence

As part of a new interview series with healthcare leaders across the country, the Olive team had the chance to interview Dr. Aziz Nazha about the biggest challenges facing healthcare today and the growing potential of AI. Previous to his current role as Director, Center of Clinical Artificial Intelligence at Cleveland Clinic, Dr. Nazha completed a hematology and medical oncology fellowship, also at Cleveland Clinic, and a leukemia fellowship at The University of Texas MD Anderson Cancer Center where he served as an instructor in the Leukemia Department.

 

 

Q: To begin, we know that you are currently leading the Center for Clinical Artificial Intelligence at the Cleveland Clinic. Can you give us a high-level overview of what you’re working on there?

We launched the center in March of 2019 and the whole mission of the center is to harness the power of artificial intelligence in healthcare, but really we envision the center becoming a hub of collaboration between academia and industry to bring the best of technology to healthcare to build models that can actually make a difference in our patients lives and in medical research. That was the whole purpose of the center – we built a platform for collaboration both inside and outside of Cleveland Clinic. 

I’m a physician – I treat patients with cancer and my specialities are leukemia, particularly myelodysplastic syndromes (MDS), but also a programmer because we program all of our models in house to develop what we call physician data scientists – physicians who actually see patients in a clinical practice and are able to understand code and develop the machine learning and deep learning models. My team now consists of medical students, residents and fellows who we’ve taught to code and use the technologies of everything we develop. At the moment we have 24 ongoing projects, 12-13 in cancer space and others in medicine, medical operations, ICU, some genomic projects, as well as new conversations about cardiology projects – we’re continuously expanding and adding more projects. 

Q:  Can you tell us a little bit about the new course at Cleveland Clinic’s Lerner College of Medicine that’s focusing on integrating artificial intelligence into the curriculum?

As you know and experience yourself [at Olive], AI talent is difficult to find. And if you talk about people that understand the complexities of healthcare data compared to other industries – that’s extremely difficult to find. Why is healthcare behind in the adoption and application of AI?  The simplest answer is you have individuals that don’t speak the same language trying to understand each other. So, the biggest problem for AI today in healthcare is that you have computer scientists and statisticians who can look at models, understand the models but don’t necessarily understand the clinical implications of that. And they’re speaking to healthcare providers, physicians or nurses for instance, and they understand the clinical implications but don’t understand the algorithms. The whole purpose of what we’re doing is to bridge this gap and have people speak the same language. And of course, the best way to find this talent is to build it – so, it’s why we put out these 3 courses.

[To build this talent] you really have to start from Medical School –  we want to help students understand the technology and most importantly use it in their work. So, these courses teach students about python, machine learning, deep learning, and of course all applications for healthcare. The last module is emerging technologies, cloud computing, internet of things and their application in healthcare. And really we have brilliant students that continue to blow me away by what they can do, and say they’ll consider using this in their research throughout medical school.

 

Q: Although you’re focused in the clinical space, how do you see artificial intelligence playing a role in other functional areas?

The applications of AI are widely used, and the center lies under what we call enterprise analytics: the financial arm, and also the operational arm which is medical operations. I think there’s always an intersection between all these applications. When I think about AI, AI becomes a tool that gets me to where I need to go, and most of the time – or actually all of the time – if I’m posed with a question, and if I can’t answer that question using linear algebra or traditional statistics, our focus is solving these problems with AI.

One of the projects which will be helpful in the future is around no-shows, for example and building models around no-shows. Can we learn something from AI about why a patient doesn’t come back? If we take the clinical data, we can learn if a patient showed up, or did not show up. What we really need to do is take that data and compare it to other data like a baseball game, or a football game for instance, especially in Cleveland, and that will give you much better of an idea on why they didn’t show because previously you were missing other parts of information. The opportunity for AI is huge. The problem is people using this technology in the wrong way or overselling the technology, that’s a great example of IBM Watson, and we don’t want that. Taking these models and making the data explainable, focusing on explainable AI, so physicians can easily understand and adopt – I think that’s the whole key for the success of AI in healthcare.

Q: If you could eliminate one of the healthcare industry’s challenges overnight, which one would it be and why?

Anytime I give a talk about AI in healthcare, I always leave the audience on this note, “The lightbulb was not invented by continuous improvement of candles.” So, in order for us to really advance healthcare, we need to completely change the way we think about healthcare and how we do research today, and everytime we say that, people get excited. But to actually do that is really, really difficult. That’s the challenge – change gets people out of their comfort zone, and the way that they practice. It’s both the challenge and the opportunity. It takes a lot of time and patience. 


Q:
We saw that you recently rode in VeloSano to support cancer research, what motivates you to participate in that event? 

I’m a cancer doctor and I’m also a researcher, so VeloSano funded my ride, and that fund was very helpful for us. Ultimately, I ride for my patients. I am not a biker, but I became one and love biking now because of VeloSano. I’m hoping next year I can do 25-50 miles. 

 

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