Industry Insights from a Leader in Patient Experience: Improving Patient Care, Healthcare Technology & AI

Industry Insights from a Leader in Patient Experience: Improving Patient Care, Healthcare Technology & AI

As part of a new interview series with healthcare leaders across the country, the Olive team had the chance to interview Brian Carlson about the biggest challenges facing healthcare today. Previous to his current role as Senior Director of Patient Experience at Vanderbilt University Medical Center, he was the COO/CEO of a physician group practice and a Practice Manager at Northwestern. Before that, he received a master’s degree in health services administration and an MBA from Xavier University.


Q: To begin, tell us about your background in healthcare and how you became Senior Director of Patient Experience at Vanderbilt University Medical Center.

I took the traditional route of college followed by graduate school, and in college I found healthcare was something I was very interested in. I considered doing pre-med, but ultimately got my undergraduate degree in psychology and went straight to graduate school where I got a master’s degree in health services administration and an MBA.

One of the greatest things about the program was the year-long required fellowship at a health system. That fellowship was where I could get mentored by a seasoned administrator and gain experience with how our complex healthcare system works in a protected environment. I didn’t have decision making authority, but I was put on projects to help advance the mission of the organization and got a lot of experience doing so. I think any administrator entering the field should really go through a residency or fellowship program.

From there, I got my first job in group practice management and then moved on to be COO/CEO of another physician group practice and then I was recruited to Vanderbilt in 2007. I started as an administrator for the Eye Institute and moved into the patient experience role in 2014, where I’m helping the system figure out how to create a consistent experience for the patients and families that need our help.

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

The field of patient experience is evolving and I’m starting to think of it as just, “experience.” 

What I mean by that is that healthcare is complex to start with and dependent on the skills and expertise of our workforce to deliver care. So, the experience our workforce has every day has a profound impact on the care they’re able to deliver. In my mind, I’m broadening the term of experience because we need to consider the relationship between the patients and families that walk through our doors, and the experience our workforce is having every single day as they do their jobs. 

Healthcare is a calling – people come into this profession because they want to help people. But sometimes we get in our own way and make it difficult for those who are really skilled to do the most effective job that they can. The untapped potential is looking into the experience of our workforce and how can we help make that experience better, so they can do the best job possible.

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

If I had to choose one challenge to eliminate, I immediately think of our need to, in a consistent fashion, share data across organizations and across individuals. We have so many restrictions on the way we can share data – and we have so much of it. If we could unmask that data, in a secure and safe way,  yet make it available whether it’s payer, provider, patient, or consumer space, and really cumulate that data, we could put some profound learnings into place to understand what it is that drives us as humans. 

Ultimately: How can we anticipate ahead of time if someone’s heading down a path, and how can we prevent that from happening? Thus decreasing the cost of healthcare.

Other industries have figured out how to share and use data in a constructive way to learn about their users. They’re trying to sell a product, but were trying to help an individual, so the opportunity is significant.

Q: How do you see artificial intelligence in healthcare impacting the patient experience?

Our ability to use our data in an effective fashion is what will enable us to learn from those past experiences. At the root level, it is the machine taking data to understand and predict what will happen in the future to improve care. Think from an experience standpoint – AI can help both internally and externally. 

On the internal side, how do we make our workforce processes more streamlined by using AI to automate repetitive tasks, to use the machine to generate some very basic administrative functions that today require people to do it manually, or become backlogged as employees never have the capacity to work through them. They do not always know what to work first, or they need to hunt and find additional information to assist them. Can we get the right data in front of the right eyes when it is needed? This could be in everything from eligibility checks to claims processing and to the front office functions. 

Within the clinical side, the possibilities are limitless from using AI to accurately diagnose and reduce errors to developing new medicines and treatments.  

On the external side, let’s consider the consumer space. B2C companies use data to predict habits around who will need what and when. In the healthcare space, you can start to imagine the experience where we know as a health system someone’s potential needs in a way that prevents, for instance, their need to go to the emergency room or prevents them from having to receive additional care down the road.

We recently conducted a study here at Vanderbilt, and the number one piece of feedback we received from our patients is that they expect that we already know them. I think our society has a growing expectation that if I provide you data or insights about me I expect you to do something with it.  There’s an expectation that healthcare is technologically advanced and an assumption that we have data to make predictions. While we’re heading down that road clinically with genetics and DNA, from an experience standpoint, we’re not there yet. The challenge to the industry is, how do we create a system of learning that can advance the skills of our providers to help predict and prevent problems in the future?

Q: Tell us about a person who mentored, inspired, or impacted you during your career.

I’ve been lucky to have so many individuals mentor me and impact me throughout my career, the difficult part would be choosing just one. But the common thread between them all is, availability – they’ve all willingly made themselves available to me over both successes and failures throughout my career. They were there to listen and understand, and then provided a direction or pose a question to help me move forward. Throughout my career, I knew I could reach out and call them, and they’d always answer the phone. They also built trust, and that trust was a two-way street. I knew I could be vulnerable with them and in turn, I knew they would always be honest with me. Lastly, they took pride in my work and I think got fulfillment in my success. As I’ve mentored people in my career, I’ve tried to bring those same attributes to the table, as well.

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Industry Insights from a Chief Data Officer in Healthcare: The Potential of Big Data, Healthcare Technology & AI

Industry Insights from a Chief Data Officer in Healthcare: The Potential of Big Data, Healthcare Technology & AI

As part of a new interview series with healthcare leaders across the country, the Olive team had the chance to interview industry leader Aaron Black about the biggest challenges facing healthcare today and the potential for data to transform the industry. Previous to his current role as Chief Data Officer at Inova, he was the Director of Informatics dealing with genomics. Before that, he lived in Columbus, Ohio for 13 years, working at Nationwide Children’s Hospital and is a Miami University Graduate.


Q: To begin, we saw you’re an Ohio native and have worked as a consultant and manager in a few different industries prior to healthcare. What drove you to get involved with the healthcare space?

It was somewhat serendipitous. I was working in consulting in Columbus, Ohio and there was a company that was doing mental health billing. They were an EMR company, and they were the only ones in Ohio who could bill Medicaid and Medicare. They were growing so quickly that they didn’t have enough people to implement their software. I was a data person and was doing a lot of conversion software implementations at the time – the area was exploding, so that’s why I got into the field.  

Once I was in the industry, I quickly observed healthcare was facing a critical accounting challenge – the way the industry billed, the way it charged – it didn’t match common practice across other industries. And with my accounting background, the current processes just didn’t make sense to me. I saw a misalignment there and thought this was an area where I could have an impact. 

Q: Can you tell us about the Inova Translational Medicine Institute that you’re currently leading?  What are a couple of examples of ways you’ve seen data analytics fuel innovation within the Institute?

The Institute is part of the Inova Health System, which is a community health system outside of Washington D.C., and was originally founded to look at the research that was being done in the genomics space.  At the Institute we focus on more than sequencing individuals, we are considering the expression of those genes or microbiome, for example. Including that scope of work has allowed us to evolve and take the data that we’re now able to get from our patients and apply it to clinical care. We find ourselves frequently meeting and addressing challenges on how we make our data part of how we tackle the harder problems when it comes to predicting, preventing and treating people after they’ve gotten sick.

We’re leveraging analytics to sequence individuals and very large sets of data. We have custom applications that run, analyze and make sense of the collected data. Typically, we’re looking for a needle in the haystack, so we use literature and bring all kinds of data assets together, and then use the minds of the scientists and doctors that are treating the patients to understand if this is something we can actually treat, which is where we meet a distinct challenge – just because you find something doesn’t necessarily mean you can treat it. This work is really the application of genetics in practice to improve the health of our patients.

Q: What do you think has been the biggest impact in healthcare coming from data science?

Two distinct impacts come to mind. One is the ability to make discoveries a typical analyst or human couldn’t make on their own without the help of data science. Today, in healthcare we manage vast and unwieldy data sets that Excel or other traditional data analytics tools can’t handle with the power needed to analyze the data efficiently and effectively.  If you can build software with the expertise of a multi-disciplinary group of clinicians, statisticians, and software engineers, then you can analyze data in ways we haven’t been able to do previously. 

Secondly, data science is a motivator to accelerate the collection of data – it’s not just a transaction anymore. It’s no longer collected with the sole purpose of billing – it’s collected so we can use it to improve care, and not just for that specific individual – also for individuals that will be treated 5-10 years from now. This is a long game. How are we going to use this data to win the war on a specific disease, for instance, 5-10 years from now with the help of faster machines and better data? This is where we’re going to see the biggest impact, and it’s already started to happen. We’ve been collecting this data for over 10 years now, and we can look at that from a biological level and compare it to the patient outcomes, which will in-turn accelerate the adoption of data science throughout healthcare.

Q: Looking into the future, how do you see artificial intelligence helping you accomplish more with data science?

The term artificial intelligence is so often used as a branded term today, it’s easily misconstrued. For me, the machine learning and analytics aspects of AI is where I see the industry heading. In the future I see machines working alongside humans – discovering how to take the repetitive actions out, the ones that don’t scale.  Analytics and machine learning will work in tandem to take on the repetitive, easier tasks, while people tackle the harder problems – like the rare diseases and the cases that just don’t make sense to us today. This advancement is going to refocus our people – the doctors and nurses – to take on the more challenging cases. 

We’ll really change the way our healthcare system works, what we spend our money and time on, and where we spend our focus: the areas where we can drive change and have the biggest impact. 

Q: What do you think is the key to improving interoperability in healthcare?

It’s incentives. Incentivizing people to interoperate. It’s not a technology problem. What’s the incentive for a provider to share their data with another provider when a patient is coming to  them? If they share that data, they might lose market share. Today, the industry fails to find an inherent benefit without further motivation. 

On the technology side, what’s the incentive for a provider to strive for interoperability outside of their vendor base? Interoperability means a lot of things too – people can call a fax or a courier interoperability, so how are we defining the term and how, as an industry, will we determine its success? And it can’t be everything. As an industry our challenge is to align incentives to that definition of success, then it must become a part of everything we do, rather than simply checking a box. 

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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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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. 

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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Olive Set to Achieve Record Growth in 2019, Digital Employee Hired at More Than 500 Hospitals

Olive Set to Achieve Record Growth in 2019, Digital Employee Hired at More Than 500 Hospitals

Company behind healthcare’s AI-powered digital employee reports rapid adoption, as hospital leaders look to relieve administrative burden.

COLUMBUS, Ohio, September 26, 2019 – Cost and complexity of healthcare continues to rise, and hospitals and health systems increasingly turn to technologies like artificial intelligence (AI), machine learning and robotic process automation (RPA) to keep up with complexity and drive down the associated costs. Olive, the company that introduced healthcare’s digital employee, today announced a continued increase in company and customer growth.

Olive has expanded adoption of the company’s namesake digital employee this year, finding homes in more than 500 hospitals comprising more than 65 of the nation’s top healthcare organizations in the U.S. Olive customers, Centura Health and Yale New Haven Health, were both seeking artificial intelligence technology to help achieve mission-critical goals.

“Everything we do at Centura Health is centered around delivering the best care to our patients. We continually strive to innovate and implement solutions that are efficient, effective and aimed at improving the patient experience,” said Ramy Hanna, Senior Vice President of Operations and Shared Services at Centura Health. “Innovative solutions using artificial intelligence help us to achieve our mission of being there for our patient’s health and we look forward to realizing the value Olive will bring to our organization over time.”

At both Centura Health and Yale New Haven Health, Olive will play a key role in taking on burdensome day-to-day processes, so employees can focus on higher-value, more rewarding initiatives.

“At Yale New Haven Health we’re starting our Artificial Intelligence journey working with Olive to automate prior authorizations,” said Sharlene Seidman, Vice President of Patient Financial Services at Yale New Haven Health (YNHH). “At YNHH we believe our people are the pivotal component to providing the best patient financial experience. That’s why we embrace innovation, to remove transaction based work and free our staff to focus on the compassionate guidance of our patients through their healthcare journey. AI is poised to make a significant positive impact at YNHH and on the industry as a whole.”

AI Adoption in Hospitals is Increasing

Health systems, like organizations in many industries, are seeking to increase revenue, reduce operational cost and increase capacity to deliver meaningful work. In fact, twenty three percent of hospital leaders are looking to invest in AI/RPA today, while half plan to do so by 2021 (according to a survey conducted by Olive with Sage Growth Partners).

“There’s a growing, multi-billion dollar problem: healthcare doesn’t have the internet. Instead, healthcare uses humans as routers, forcing workers to toggle between disparate systems – they copy, they paste, they manipulate data – they become robots. They click and type and extract and import, all day long – and it’s one of the leading reasons that one out of every three dollars spent in the industry today is spent on administrative costs.” Olive CEO Sean Lane said. “Olive is on a mission to solve that challenge with health systems deploying a digital workforce that learns, adapts and improves over time.”

As leaders seek to scale and grow, they have realized that AI and RPA are essential tools to support handling and processing data more efficiently. The key to successfully building out an enterprise-wide AI solution is to begin with processes that are high-volume, and repetitive, where you can quickly feel impact; this helps build momentum to expand into more complex workflows.

Olive Learns, Adapts and Improves Over Time

As a digital employee that can complete tasks more than 60 times faster than a human, Olive has been hard at work. So far this year:

  • Olive has completed more than 350 million actions across all workflows and processes
  • For one 2,000+ bed customer, Olive has completed more than 450,000 claim status checks 
  • For another 2,000+ bed customer, Olive has processed more than 250,000 charge corrections

But processing mass amounts of data is not enough. To truly solve critical challenges such as cost savings, efficiency gains and revenue recognition, Olive has to adapt and learn over time. For example, by consuming large amounts of historical claim status data, Olive is already identifying appealable denial codes. Correcting the cause of just one appealable denial code can result in thousands, sometimes even millions, of dollars in revenue recognition. These valuable insights are what sets apart a digital workforce from traditional RPA software.


Olive Creates Jobs, Attracts Healthcare and Tech Industry Veterans  

The Olive team is growing rapidly, too. In the first five months of 2019, Olive met a two-year goal to hire 100 new team members, and the company says there will continue to be job growth to match company growth. Recruited talent is increasingly made up of those with healthcare backgrounds, enriching the company’s collective industry knowledge and expertise. Recent executive additions include:

  • Lori Jones, Chief Revenue Officer, is leading Olive’s revenue organization with decades of experience in healthcare at companies like McKesson Corporation and Connance, Inc.  Lori brings deep expertise in helping providers identify effective ways to harness the potential of technology.
  • Jim McCormick, Chief Financial Officer, is leading Olive’s financial, accounting and delivery divisions, with decades of experience in the IT and Software-as-a-Service space.  Jim brings to Olive experience leading finance and operations organizations at companies like Silverpop Systems and Harbinger Corporation. 

“We’re on pace to triple our business and have no plans to slow down,” said Lane. “There’s a clear and present need for operational efficiency in the healthcare industry and Olive brings it – ultimately, we’re leading a movement toward a smarter, more efficient, more connected healthcare experience.”


Olive is deploying the first digital workforce built specifically for healthcare, automating healthcare’s most robotic processes, so human employees don’t have to. Olive delivers healthcare organizations improved efficiency and speed while reducing costly administrative errors. Using the systems an organization already has in place, Olive operates as a digital employee intelligently routing information and data between systems automating repetitive, high-volume tasks and workflows, providing true interoperability. Olive is proud to partner with more than 65 healthcare organizations made up of more than 550 hospitals in over 35 states across the country, ranging from some of the nation’s top health systems to small regional hospitals.