While AI holds a lot of promise, it has also turned into a buzzword that is frequently misunderstood and misapplied to both our lives and our businesses. This is especially true in healthcare, where the promise of better patient outcomes is often overshadowed by the threat of compromised jobs or regulatory red tape. To help set the record straight on the current state of AI in healthcare, we’ve put together a list of the top 10 myths we hear most frequently.
1. There is just one type of AI
In fact, AI is a blanket term that comprises multiple types of technology
, including optical character recognition, natural language processing, and machine intelligence. At its core, artificial intelligence refers to technology that mimics sophisticated human processes in a way that makes it indistinguishable from a human. In this way, our definition of AI comprises not just computer vision and machine intelligence, but also Robotic Process Automation
, which automates repetitive, rule-based tasks.
2. Automation will take away more jobs than it creates
In fact, industry experts speculate that the opposite will happen. The reason why is because AI adoption won’t happen overnight––and when it does happen, it will primarily replace repetitive, lower-skilled jobs that don’t require human traits such as creativity or empathy. This won’t invalidate the need for a human workforce; on the contrary, it will provide an opportunity for jobs to become less menial and more thoughtful.
3. AI is first and foremost a way to cut costs
Some organizations may turn to AI as a way to cut costs, but that also can be a side-effect of so many other benefits that automation can bring. AI can help organizations improve their efficiency and KPIs, reduce risk, improve employee satisfaction and retention, and more…while also cutting the costs associated with them.
4. AI’s ROI is difficult to calculate
Because AI is still a new business tool and ROI may not be as cut-and-dry as it is for other, more conventional business tools, many mistakenly think that AI’s ROI is difficult to ascertain and they choose to avoid the perceived risk. However, the key to assessing ROI is to be diligent in measuring your current-day spend––not only in terms of salary and benefits, but also in terms of risk, of extra days in A/R, and any other subsidiary metrics that might be positively influenced by AI. If you choose to work with an automation vendor,
they will be able to help you think through the metrics to consider when evaluating AI’s ROI potential. Then, they will help you understand how AI will impact those metrics so that you can generate buy-in from your team––while also feeling confident yourself.
5. AI is a magic fix for your business
Many organizations think that AI is the “secret sauce” that will help them improve efficiency, reduce costs, and make their employees happier. And while all of those things can and do happen when AI is executed properly, those effects don’t come quite as easily as some may believe. Many AI companies sell insights, not action
, to their clients, which means that your organization still needs to do the work in order for AI to have a tangible impact on your business.*
6. AI requires large amounts of data
It’s true that more data is always better when it comes to artificial intelligence––after all, the more historical data you have at your disposal, the more opportunities you have to “train” an algorithm to act a certain way based on similar data in the future
. However, depending on the task that you intend to automate, certain AI frameworks are flexible enough to work with limited subsets of data.
7. Only large companies with in-house IT teams can benefit from AI
Even as recently as a few years ago, organizations needed to employ a sophisticated internal IT team in order to build, customize, and implement an AI model for their organization. Not so today. At Olive, we’ve seen AI successfully implemented
everywhere from the nation’s largest health systems down to 17-bed rural clinics. If your organization is on the smaller size but is still interested in implementing AI, you might consider contracting with a third party to build and manage your automations rather than bringing on extra in-house support.
8. You can’t build AI in-house
With the advent of cloud technology, it’s easier than ever to create data-intensive automations and harness the power of AI for your organization. This, coupled with robotic process automation and AI platforms, provides organizations with more of a “do-it-yourself” option so that they can harness the benefits of AI without having to rely on external consultants. If you’re trying to determine whether building in-house or hiring an external consultant would be more beneficial for your organization, ask yourself whether AI is a part of your organization’s core competency (meaning, it directly aligns with the value proposition with which you face the market). If AI is a key component to your organization’s value statement, you might want to consider if you have the skills in-house to execute on your workflows. If that isn’t the case for your organization (which it likely is not for a provider), outsourcing your workflow to an automation vendor will help you harness the same impact without straining your internal resources.
9. AI inherently possesses the ability to learn from itself
AI “learns” by analyzing test data and determining which inputs translated to a given output. It will make its best educated guess based on the data it has at its disposal, but just like humans, it needs additional support to help it differentiate “right” from “wrong.” Often, humans will re-train the algorithm to help it refine its predictions.
10. AI is still too risky to apply to healthcare
There are too many applications of AI in healthcare––from diagnostics to imaging to revenue cycle management––for reservations to pervade the entire healthcare industry. It’s true that in a clinical setting, AI can pose a greater risk because the stakes of being “right” are much higher. However, administrative AI is a burgeoning subset of healthcare AI that focuses on improving operational efficiency by optimizing data transfers between healthcare tools and systems. In our experience, the impact potential is undeniable: one of our customers was able to reduce their Days in Accounts Receivable by 34% in the first 180 days of using AI to manage their organization’s insurance eligibility checks.
Even though AI in healthcare still is a new concept and will continue to be defined over time, early signs indicate that the sky is the limit in terms of its potential to benefit an organization’s operational efficiency. If your organization is looking for ways to harness the power of automation and AI, reach out to us today to learn more.
*That said, Olive isn’t one of those “Brain in a Jar” companies (as we like to call them). Our AI solutions
automate your repetitive and high-volume tasks so that you can reap the rewards of AI without having to sacrifice your team’s time and bandwidth.