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Why Do We Need Artificial Intelligence in Healthcare?

Artificial intelligence in healthcare can lead to immediate gains by reducing inefficiencies in the revenue cycle.

AI in Healthcare

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Sponsored by Olive

- Things are changing faster than ever in the world of business and technology. Society has reached a general consensus that we’ve entered the fourth industrial revolution, and the businesses that are willing to adapt and embrace new technology will be the industry leaders of tomorrow.

The world of healthcare is no exception, and there are a variety of opportunities and challenges facing the industry today. Many of the problems with healthcare stem from the disjointed nature of EHR systems and the number of interoperability challenges weighing down organizational processes, costing time & money, and negatively impacting patient experience.

While artificial intelligence and intelligent automation can solve these problems today, one of the biggest hurdles to implementing AI in healthcare is simply overcoming the aversion to change. In the lifecycle of any innovation, this is to be expected. However, for a modern healthcare business, letting aversion to change win out over good sense can be devastating. In this piece, we’ll explain the Diffusion of Innovation Curve (and why it’s relevant here), the costs of not adopting AI now while we’re are still in the relatively early stages of its potential, and help you build a business case for implementing AI in a way that solves today’s problems and helps future-proof your business going forward.

Understanding the diffusion of innovation curve

Diffusion of innovation theory explains how a given idea or technology gains momentum and spreads. Developed by Everett Rogers in the 1960s, the theory has become vital to understanding and explaining how paradigm-shifting innovations grow from conception to becoming the norm.

In short, the process of a new innovation going from exciting new idea to an accepted and widely adopted technology or idea involves it being accepted by five discrete groups of people (i.e., adopters) and the market growth takes the shape of an elongated S):

  • Innovators (2.5%): This group is at the cutting edge, taking risks on new technology others may be unaware of or otherwise unwilling to adopt. Often innovators will have significant technical knowledge in the field.
  • Early adopters (12.5%): The early adopters are next to use new technology and often have significant status and authority on a given subject. Using the world of social media as an example, influencers will often fall into this category.
  • Early majority (34%): The first stages of mass adoption involve the early majority beginning to use the new technology.
  • Late majority (34%): The late majority is a little more risk-averse than the early majority and therefore takes to the new concept later in the cycle.
  • Laggards (16%): Laggards are change-averse and are the last group to participate. That family member who just got around to getting a cellphone would fall into this category.

It’s important to note that there is generally a large gap in time (or “chasm”) between early adopters using a given innovation and the early majority following suit, from there this is an explosion in popularity that wanes down while the laggards trickle in. This is what creates the aforementioned elongated S shape in the curve. When a new technology experiences exponential growth, it is often the innovators and early adopters that reap the most benefits (for a recent example, consider the explosion in popularity of Bitcoin).

While the curve is important to marketers and historians for clear reasons, what does this have to do with healthcare? The answer is simple: The healthcare industry is facing major operational and administrative challenges related to poor interoperability between EMR systems and wasted labor-hours on mundane, tedious tasks. There are new innovations — namely intelligent automation technologies — that can solve these problems today. The early adopters that are able to overcome objections and roadblocks to AI deployment will be able to streamline workflows and optimize their administrative workflows in ways never before possible.

The takeaway here is: We’re currently in the early stages of realizing the potential of AI, both in healthcare and society as a whole. The early adopters will be well-positioned to maximize the benefits of this powerful technology. Not only will they have more time to learn how to best leverage AI in the long term, but they will also gain a competitive advantage in the near term as well. The laggards, on the other hand, will find themselves struggling to keep up.

THE COST OF INACTION

Let’s suppose your organization is one of the change-averse laggards, realistically, what are the downsides? If you’ve made it this far without automation, why change now?

While the easy answer to this would be to simply stress the importance of innovation and adaptability to any business and point to case studies such as Blockbuster or Kodak, asking these questions is important in the decision-making process. You can’t jump on every new idea that comes your way, so understanding the opportunities you’d miss out on if your organization is slow to adopt intelligent automation is pragmatic.

The demands patients place on healthcare organizations are always increasing. Additionally, the healthcare industry is becoming more and more crowded and competitive, and the multitude of options are raising customer expectations even further. If you’re not able to adequately serve your patients and meet or exceed their high standards, you can expect to lose them to the competition.

The organizations that offload mundane, work-intensive administrative tasks to software will be able to focus more of their human capital on interacting with patients, improving patient experiences, and innovating. Those that continue to spend thousands of human hours on administrative tasks will have fewer resources and less flexibility to focus on what really matters — making sure patient care, service, and experience are optimized.

With government and payer regulation on the rise, more technical complexity is being added to revenue cycle workflows. Simply throwing more labor hours at the complexity isn’t a cost-effective or scalable way to handle it. Fortunately, automation technology enables healthcare organizations to significantly reduce administrative burden.

What’s so compelling about this aspect of applying intelligent automation to healthcare operations is that the amount of change possible here isn’t just iterative in nature, it’s paradigm-shifting. To help drill home the potential impact, consider these statistics:

  • Denials cost hospitals and health systems over $262 billion annually
  • 61% of denials are due to missing information, a problem easily addressed by software
  • AI-powered robotic process automation can reduce eligibility processing costs by 200%

The key to understanding why AI is so effective in streamlining and scaling these administrative is understanding what you’re doing when you implement an automated solution for a healthcare work process. In a nutshell, you are offloading a repetitive, data-driven task from a human and giving it to software. Humans are much more prone to errors in data entry, need to take breaks, and simply cannot operate as fast as A.I. can when it comes to these sort of tasks. Not only can AI reduce the amount of errors you encounter, but it can also help position you to scale much more economically.

FOOD FOR FUTURE THOUGHT

As we have seen, AI and other automation technologies will play a key role in helping healthcare organizations solve the problems facing the industry and scale their processes despite the enhanced competition and complexity in the space. However, aversion to change is inevitable when attempting to implement a solution that creates such a fundamental shift in how things get done.

That being said, for every early adopter of a technological revolution, there’s a matching laggard. For every CIO evaluating dozens of potential use cases for AI in their organization, there’s a CIO digging for technical holes to poke to ensure AI stays far, far away.

Pushing a laggard to become an adopter is no easy task, but it’s often necessary to move large-scale AI projects across the finish line.

About Olive:
At Olive, we strive to build revolutionary artificial intelligence and robotic process automation solutions for the healthcare industry to help improve business productivity. Our efficient cost-reducing options deliver positive results with Olive overseeing repetitious high traffic processes and workflows. These specialized tools empower our customers by delivering the freedom to do the hard work of building the future of healthcare. To learn more, visit our website.

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