What Is Actionable AI and Why Does It Matter?
The newest evolution of generative AI
While AI as a tool has, in some form, been around since the 1950s, it didn’t become mainstream until ChatGPT, our beloved large language model (LLM), was launched in 2022. Since then, many businesses have viewed LLMs and generative AI as a tool to achieve greater efficiency and new levels of innovation. However, just as we’re getting used to one new tool, there already seems to be something else coming down the pipeline — actionable AI.
Below, we’ll explore what actionable AI is, how it works and the potential benefits actionable AI may offer in the medical space.
What is actionable AI?
Before we go head first into actionable AI, we must first clarify some of the other versions of AI currently available.
Traditional AI
Traditional AI, for instance, can complete preset tasks using pre-established rules and algorithms, but it has restricted learning capabilities. Therefore, while it does a great job of performing specific tasks based on the data provided to it, traditional AI can’t come up with new ways to get jobs done. This tool focuses mainly on analyzing data to make predictions, like what you’d see for a computerized chess game.
Generative AI
Then, there is generative AI, which can develop new and unique material using the information provided to it. Large language models, like ChatGPT, Dall-E 2 and Gemini, are all innovative examples of generative AI that can produce images, text, videos and/or other forms of content.
Generative healthcare AI has even been implemented to assist with medical imaging, research and administrative operations. However, this AI system is vulnerable to privacy and security issues and has biases that could lead to incorrect diagnoses and treatments.
Actionable AI
Actionable AI is a subfield of artificial intelligence and a broader category of generative AI that may be able to pick up where LLMs left off. Although large language models have a profound ability to understand and generate content, actionable AI powered by large action models (LAMs) is meant to take information and turn it into autonomous action. Integrating actionable AI could be revolutionary for businesses, but it’s still new to the game.
How does actionable AI work?
Actionable AI is powered by LAMs, a technology designed to bridge the gap between insight and action. LAM’s data modalities differ from LLMs as they’re more versatile and multimodal, use advanced training methodologies and interpret data across a broader spectrum. This type of AI system is equipped with tools capable of performing tasks, making decisions and adapting autonomously to various situations.
The potential benefits of actionable AI in healthcare
From a business standpoint, actionable AI could offer several advantages that enhance a healthcare outcomes. Here are some examples:
1. Enhanced efficiency
Thanks to its decision-making skills, this tool could optimize efficiency by discovering waste in inventory, transportation, motion, overproduction, defects and overall processing - to name a few.
This form of healthcare AI might also be able to analyze claims data, uncover a potential error, determine a solution and autonomously correct the problem. In comparison, Large language models are able to suggest an adjustment, but aren’t able to resolve it without human intervention.
2. Improved patient outcomes
Research suggests that almost 400,000 hospitalized patients experience some form of preventable harm annually, which costs the healthcare system billions. Many of these adverse events result from poor information flow, such as a patient moving from one organization to another or provider knowledge gaps.
Fortunately, actionable AI might be able to catch some of these hiccups before they impact the patient. The precision and agility provided by this AI system may alert healthcare providers of diagnostic errors, prescribing decisions and poorly created orders for transfer of care. In fact, it may take it a step further and automatically fix such issues, which could result in improved patient outcomes.
3. Increased productivity
If actionable AI analyzes data in real-time and provides relevant and feasible suggestions, it should free your staff of mundane tasks. For example, instead of employees sitting on the phone trying to get prior authorizations for medications, maybe an actionable AI system could automatically find an alternative medication that’s covered by a patient's insurance.
Let’s consider another scenario. One study found that nurses spend only 21% of their time on direct patient care, while over 60% is dedicated to administrative tasks and various indirect care activities. Integrating actionable AI could relieve nurses of this administrative burden, which may reduce burnout and boost patient satisfaction.
4. Significant cost-savings
Healthcare companies using actionable AI may uncover new opportunities, decrease errors and reduce risks, which could significantly improve their bottom line. In short, it has the potential to increase their competitive advantage.
However, according to CompTIA, only 22% of businesses aggressively try to implement AI into their offerings and business processes. Uncertainty and initial costs largely contribute to this hesitation, but the characteristics of actionable AI may provide a worthy return on investment for those who take the leap.
What’s next for actionable AI?
As with any shiny new technology, it’s going to take more studies and research before we see any wide adoption. There are also ethical issues that need to be discussed and considered with the use of autonomous technology within patient care. Essentially, it’s still very early in the game for actionable AI, but as we’ve seen from its helpful predecessor the LLM, it's smart to keep an eye on its progression.
Actionable AI: Another innovative tool for competitive businesses
Just as we were getting familiar with large language models, actionable AI has come along as a potential next step in the evolution of AI.
While there is still much more to learn about this tool, the future of healthcare AI looks promising. Speaking of the future of healthcare, we’d like to introduce ourselves!
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