Enterprises have been digitizing data and processes furiously over the past few decades, and these efforts have unlocked a pantheon of capabilities to offer new products and better experiences, and healthcare is no exception. Partially driven by government mandates and subsidies, healthcare systematically bought large electronic medical record systems (EMRs and EHRs) and other systems to bring them into the digital era. Unfortunately, this tidal wave of adoption, although extraordinarily valuable, had negative side effects as well:
The digitization of healthcare created silos. Database fortresses were built at every organization. They weren’t built to share. They weren’t built to interoperate – not between software systems and certainly not between organizations. No connection to insurers. No connection to other providers and until recently, almost no connection to patients.
Instead, healthcare employees have taken on the job of the data router, shifting hours spent from being in front of patients, to being in front of computers, shepherding patient data into the right fields. This administrative burden is driving skyrocketing costs, rising attrition and a backlog of work in an industry already suffering from razor thin margins. Healthcare can’t continue to operate like this – there must be a solution to rescue nearly a trillion dollars of administrative costs and reallocate these precious resources to the delivery of care, the creation of new drugs and therapies, and the research to eradicate diseases.
The answer is an AI-powered workforce.
Today, most healthcare executives are familiar with robotic process automation, or RPA – it’s used to automate common workflows or business practices like patient scheduling, supply chain management, claims management, and more. That’s because many of the time-consuming, manual processes that make up healthcare administration are simple, rule-based and high volume – the perfect candidate processes for automation. But for many organizations looking to deploy artificial intelligence, RPA alone will not allow them to realize the technology’s full benefits – an AI workforce is required.
An AI workforce goes beyond traditional RPA in 3 very important ways:
- An AI workforce has deep learning
- Gets smarter over time & adjusts work
- Interacts with human management
Not all automations an AI workforce does can be performed by a human – in many cases, an AI workforce uses deep learning techniques to accomplish far more complex tasks.
Although an AI workforce depends on RPA as a building block of its capabilities, it leverages other advanced technologies to handle more complex tasks that RPA can’t accomplish alone. For instance, while RPA can quickly and accurately process large volumes of data, Olive, the first AI-powered workforce built for healthcare, leverages some degree of artificial cognition on top of an automation, allowing her to make decisions or take action with cognitive “thinking” involved.
The processes involved in deep learning are similar to that of data mining and predictive modeling – this is how Olive gets smarter over time. Leveraging deep learning techniques provides better and faster information that improves efficiency, capacity, and reduces costs by providing insights into bottlenecks – and the reason behind these bottlenecks – identifying systemic, recurring issues and making adjustments or recommendations to solve them.
An AI workforce can learn, adapt to change its work based on new intelligence.
Most of the value of an AI workforce is created after a bot is deployed – that’s because, much like a human employee, if a bot is doing the same thing on day 100 of employment as it was on day 1, a huge opportunity is lost. Through predictive analytics, deep learning, and a continual stream of insights, an AI workforce gets smarter over time, providing lasting value.
Olive turns insights into actionable intelligence, identifying potential problems from a mile away, so organizations are learning about solutions before they even learn about the problem. By consuming large amounts of historical data already in your system, Olive finds trends and data anomalies in your workflows and learns to respond the same way a human would – only smarter, faster, and more accurately – making continual improvements to provide better, more meaningful data and insight as she learns. And by pairing an AI workforce with key hospital administrators, they can streamline and improve the management of data-heavy tasks like insurance eligibility checks or patient scheduling, using data to uncover and resolve recurring issues.
An AI workforce interacts with managers to provide business intelligence and recommendations on improved ways to handle tasks, so they continue to generate value after deployment.
Olive works with human managers to determine the best way to communicate actionable insights, and that intelligence gives organizations a ‘Decision Advantage’ over where and how they apply their resources towards current workflow improvements or new candidate processes for automation.
For instance, at one health system, Olive was hired to automate claim status checks. But unlike a traditional RPA bot, as soon as Olive was live she started collecting data that became actionable insights – like dollar amounts associated with denials – to communicate back to her manager for process improvement opportunities. Based on these learnings, her manager recommended that she focus on a specific subset of denials, which lead to another key discovery: millions of dollars of denials stemmed from a specific drug denial due to missing prior authorizations and medical necessity. This insight allowed the hospital to target a specific department in their organization where this recurring issue could be resolved. This “always on” analysis of information allows an AI workforce to proactively offer new solutions for workflow improvements as it gets smarter over time.
An AI workforce has Global Awareness and can connect disparate sets of information
Lastly, “global awareness” is another important concept that’s core to an AI workforce – the understanding or awareness of information across multiple networks, systems, databases, or contexts. Interoperability is a consistent and growing challenge facing healthcare and the ability for our AI workforce to transcend those silos opens up great opportunity. One example is quickly identifying a portal outage and alerting managers before a failure, as well as other organizations where Olive is employed. In the future, it could mean knowing a particular patient’s identity across multiple doctors’ offices or hospitals – even across different systems globally. This identification and matching of people is monumentally important to building the interoperability our industry so desperately needs.
That’s why we built Olive: to work side-by-side with healthcare employees with access to a limitless amount of data.
As AI becomes more advanced – using applications humans have already developed to organize and interpret larger datasets than a human ever could – the opportunity to build and scale an AI workforce is greater than ever before. And at Olive, we think healthcare employees should handle the functions that are uniquely suited to humans, not the job of data entry clerk or data router. Olive can perform these tasks much more accurately and efficiently, working to resolve recurring issues over time and allowing human employees to focus on higher-value initiatives.
Working alongside healthcare employees, Olive is trained to think and make complex decisions that are driven by data. She never misses a day of work. She never makes unprogrammed mistakes. And every Olive learns collectively, like a network, so that healthcare organizations never have to solve the same problem twice.
We’re making healthcare more efficient, more affordable, and more human with a growing AI workforce, so humans finally have the time, energy, and bandwidth to focus on what matters most: the patient experience. Just think of all the time AI will give back to our human employees – clinicians, providers, administrators, payers, and more. And with every organization that employs an AI workforce, our ability to carve millions of dollars out of the cost of healthcare will become closer to reality.
If you want to learn more about Olive, contact us to schedule a demo.