Work Smarter, Not Harder: The Future Of AI Agents

Forbes

19-05-2025

Navaneeth Nair is the Chief Product Officer at Infinx Healthcare and has over 21 years of experience in healthcare technology. getty
If you have ever wished you had a way to autonomously and proactively complete tasks that are taking up too much time, chances are you could benefit from an AI agent. Unlike the commonly used AI bots or AI assistants, AI agents are unique in that they use their own superpower—intelligence—to carry out thoughtful and sometimes complex, multistep tasks completely independently.
As someone who has spent the better part of two decades helping healthcare companies strengthen their operations and patient experience through technology, I've found great use cases for AI agents. Short on staff? No problem—there's an AI agent to fill in the gaps. Need help navigating complex payer portals that change their requirements daily? Don't sweat it—AI agents are made for this kind of work.
Unlike bots and agents that require predefined rules or in most cases actual humans to make decisions, AI agents demonstrate three unique abilities: understanding, reasoning and action. With language learning models (LLMs) as their guide, AI agents cut through the obstacles that many of us on the back end of healthcare delivery face (i.e., patient registration, eligibility, insurance verification, accounts receivables) and move the needle in efficiency. Read on to learn how … there's an agent for that too but you don't want to miss this.
Anyone who works in a healthcare facility's revenue cycle or back-end billing office knows the job's challenges. And while automation has come a long way in lessening the load for revenue cycle workers, the reality is that many of these rule-based models can only take them so far. At some point, there's a glitch in the system, be it a difficult payer mix, an uncommon patient scenario or a clinical dataset that is anything but straightforward. This is where AI agents come on the scene.
Using cognitive reasoning and predictive capabilities as their guide, AI agents are able to shed light on specific scenarios, such as trends in denials. Answers to questions such as 'what's the root cause?' and 'how can this be corrected on the front end to avoid future headaches?' are within reach. In an environment that is plagued with denials left and right—850 million a year to be exact—technology like this has the power to transform processes that are not working in favor of those that are. AI Agents And Document Capture
One of the common reasons for denials that AI agents can pick up on is incorrect information being entered about a patient dating back as early as registration. The spelling of a person's name, place of residence or insurance information are all pertinent pieces of the puzzle, and if entered even the slightest bit incorrectly can lead to major hiccups. This is where AI agents and a special tool called document capture comes into play.
Document capture has the ability to classify, extract and validate data that is coming through provider offices via fax, scans and even handwritten documents. That data can then flow straight into the provider's electronic medical record (EMR) of choice in real time and populate next steps that are needed to complete the transaction, such as creating new orders or updating authorizations. Instead of administrators spending countless hours deciphering page after page of information and increasing risk of errors being made, AI agents are powering document capture for a better, more efficient way to work. Case In Point
Take for example the radiology industry. If you've ever been to get an MRI or CT scan, you know that it's not as easy as showing up for the appointment. There are many steps on the front end, including a referral from your doctor, approval from your insurance company and other boxes that must be checked before you arrive.
Unfortunately, despite the great progress made in healthcare technology and automation, many radiology practices are old-school. They rely on manual, paper-based processes and limited optical character recognition (OCR) tools. Eliminating these time-consuming processes and taking advantage of tools like document capture allow radiology practices to put their patients on the fast track for a better care delivery experience. Gone are the days of sifting through stacks of paper and hastily written orders. In Closing
If you are ready to help take your team to the next level and elevate your processes so staff members can focus on the more valuable tasks, AI agents are there to help you bridge the gap. Like any technology, it's important to remember to weigh the pros and the cons. AI agents have the ability to save your team valuable time and expedite delays, but they can't possibly replace the human touch that is so vital in making patients feel appreciated and cared for. If automation is part of your team's strategic plan moving forward, here are several considerations to keep in mind:
Memory Matters: AI agents are as smart as the memory bank of data they have access to, meaning they rely on past 'memory' from actions already completed to drive future outcomes. This is why AI agents are so good at breaking down tasks into subactivities and establishing workflows to complete an assignment—they have learned from past history what works and what doesn't to drive decision-making.
Bolstered Compliance: AI agents' ability to gather information from disparate sources automatically reduces the risk of error that comes from manual data entry and data collection time. They can also be used to screen and analyze data for risk of future security breaches and incidents down the road.
Explainability And Bias: A challenge that comes with AI agents is understanding the 'explainability' of how the tool makes decisions. Without this in place, it can be almost guaranteed that unexpected issues will arise. As previously mentioned, AI agents cannot replace the human touch, and explainability ensures that transparency, trust and human connection are key parts of decision-making. Explainability and bias go hand in hand, because without explainability, it is very easy for biased data and algorithms to be programmed into an organization's AI agents.
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