May 13, 2021
Originally published Aug 12, 2019 | Updated on May 13, 2021
Healthcare needs a digital revolution— the past year has certainly made that clearer than ever. Manual processes and siloed technologies are holding back the industry from operating at peak efficiency and with optimal outcomes. For healthcare to reach its potential in the future of work, we need a new way of thinking about work: an AI workforce to pair with our human workforces. An AI workforce will unlock the potential of our human employees and the data that is currently in disparate systems.
Over the past few decades, enterprises have been furiously digitizing data and processes, and these efforts have unlocked a pantheon of capabilities to offer new products and better experiences. Partially driven by government mandates and subsidies, the healthcare industry systematically bought large electronic medical record systems (EMRs and EHRs) and other softwares 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 of information, within each organization and in healthcare as a whole.
Health systems are doing the best they can with the technologies they have in place right now. But 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 payers. No connection to other providers and, until recently, almost no connection to patients.
Instead, healthcare employees have taken on the job of data routers, trading off hours spent in front of patients with equal time spent in front of computers shepherding patient data into the right fields. This administrative burden alone is skyrocketing costs, raising attrition and creating a backlog of work in an industry already suffering from razor thin margins. Additional challenges posed by the pandemic simply exacerbated the problem.
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 research to eradicate diseases.
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 (AI), RPA alone will not allow them to realize the technology’s full benefits. We need to shift from the idea of a ‘digital workforce’ to an ‘AI workforce.’ Artificial intelligence empowers true interoperability in healthcare by connecting disparate systems, connecting humans to data and connecting datasets to be analyzed for insights.
An AI workforce is much more than a digital workforce using robotic process automation. It goes beyond RPA in four very important ways:
Our artificial intelligence automation solution, Olive Works, leverages these advanced capabilities to drive significant advantages for her customers. Let’s explore what these mean:
Although an AI workforce depends on RPA as a building block of its capabilities, it leverages other advanced technologies, including deep learning, to handle more complex tasks that RPA can’t accomplish alone. Unlike RPA bots, an AI workforce continues to collect and synthesize the data it processes. Using this data — with techniques similar to data mining and predictive modeling (but automatic and using AI) — an AI workforce can identify patterns and correlations that humans cannot. And the data is not limited to a single process; when an AI workforce is deployed, the data can be synthesized across all applications.
A deep learning system works better and faster than humans and can provide information that improves efficiency, capacity and reduces costs by uncovering bottlenecks. It goes even further to understand the reason behind these bottlenecks, identifying systemic, recurring issues and making adjustments or recommendations to solve them. And while RPA can quickly and accurately process large volumes of data, bots that use artificial intelligence leverage some degree of artificial cognition on top of automation, enabling them to make decisions or take action with cognitive “thinking” involved.
Most of the value of an AI workforce is created after a bot is deployed. This is 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 and provides perpetual lasting value.
An AI workforce 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, it can find trends and data anomalies in your workflows and learns to respond the same way a human would — only smarter, faster and more accurately. An AI workforce makes continual improvements to provide better, more meaningful data and insights. And by pairing an AI workforce with key hospital administrators, those administrators can streamline and improve the management of data-heavy tasks, like insurance eligibility checks or patient scheduling.
Sophisticated artificial intelligence automations can interact with human managers, communicating actionable insights about the work being completed. An ‘always on’ analysis of information allows an AI workforce to proactively offer new solutions for workflow improvements as it gets smarter over time. That intelligence gives organizations a ‘decision advantage’ over where and how it applies automation resources — towards current workflow improvements or new processes.
For instance, at one health system, Olive was hired to automate claim status checks. But unlike a traditional RPA bot, as soon as she 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 focusing on a specific subset of denials, which led to another key discovery: millions of dollars of denials stemming from a specific drug denial due to missing prior authorizations and medical necessity. This insight allowed the hospital to actually resolve this recurring issue.
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 challenges are a consistent and growing problem facing healthcare. The ability for our AI workforce to transcend those silos opens up great opportunities. At the most basic level, AI can create a network of data throughout a hospital or health system, connecting EMRs/EHRs to other datasets and other processes, like prior authorizations.
Taken further, artificial intelligence has the capability to create a healthcare-wide network, or global awareness. It can connect payers and providers (which it already is through our end-to-end prior authorization solution). It can adapt across hospitals, taking learnings about a portal change at one hospital and applying it to all hospitals across the network. 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 patients are monumentally important to building the interoperability our industry so desperately needs.
As AI becomes more advanced, the opportunity to build and scale an AI workforce is greater than ever before. The future of work is changing, and AI and automation need to be at top of mind for leaders looking to meet today and tomorrow’s challenges.
We think healthcare employees should handle the functions that are uniquely suited to humans, not the job of a data entry clerk or data router. AI 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 she 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.
To learn more, contact us to schedule a 1:1 chat with an AI and automation expert.
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