Which AI Solutions Will Healthcare Organizations Adopt in 2025?
Healthcare leaders are interested in AI tools that provide clear value, whether that’s a better clinician experience, reduced costs, increased administrative efficiencies or improved patient care. Here are some examples.
Ambient Listening Reduces Clinical Documentation
Many organizations are already going down the AI path with ambient listening, which are machine learning-powered audio solutions. Used by physicians initially before being expanded to nurses, the voice-recognition technology listens to and analyzes patient-provider conversations in real time, then extracts the relevant information for use in clinical notes, meeting billing and coding requirements. This enables clinicians to focus on the patient rather than having to multitask to complete documentation.
A major reason that organizations are choosing ambient listening as a first step into AI is that they’ve evaluated and found clear ROI around these solutions when it comes to clinical efficiency and mitigating burnout.
Another reason is that ambient listening isn’t considered as risky as it was in the past. It now falls into a category of low-hanging fruit in the healthcare AI space, along with chart summarization.
Pushing for Increased Accuracy and Transparency in Generative AI
Some healthcare organizations are beginning to experiment with retrieval-augmented generation. RAG is an AI framework that combines traditional vector database capabilities with LLMs. In other words, users get the benefits of a generative AI-powered chatbot that can access an organization’s more accurate and recent data.
RAG allows the organization’s chatbot to produce better answers for staff in Q&A applications. This reduces some of the pitfalls of using generative AI tools.
The use of synthetic data in AI development and testing is another area that will see heightened interest, because having decent data to validate models is a challenge. This is part of a larger trend in model testing and model assurance capabilities. Performance claims for these models will face increasing scrutiny by healthcare organizations.
In the past, organizations didn’t know what questions to ask, but now there’s a lot more education available. Healthcare leaders want to ensure that the models do what they promise. Groups such as the Coalition for Health AI are creating frameworks to do this very thing.
DIVE DEEPER: Demystify artificial intelligence adoption for your organization.
Machine Vision Improves Patient Care
Adding cameras, sensors and microphones to patient rooms enables healthcare organizations to collect more data that can be analyzed by AI platforms to improve care. For example, cameras can detect when a patient has turned over in bed, and the platform can alert care team members that they don’t need to turn the patient manually. Some cameras can also detect when a patient is getting up and alert staff so they can prevent a fall.
As these tools advance and more Internet of Medical Things sensors and cameras are added to patient rooms, more solutions will combine the AI capabilities of machine vision and ambient listening to improve proactive patient care as well as clinical workflows.
Will AI Regulation Increase in 2025?
There’s already been an uptick in AI regulation, and we expect that to increase due to the nature of AI and fears of the unknown. Whether it comes from the government or from regulatory agencies and organizations, there will likely be new regulation to ensure AI isn’t used inappropriately. However, it’s important to achieve a balance between regulation and innovation.
Healthcare organizations will also become more interested in learning how to comply with existing regulations such as the Office of the National Coordinator for Health Information Technology’s HTI-1 Final Rule regarding health data, technology and interoperability.
Preparing for AI Adoption in Healthcare
AI provides healthcare organizations with exciting possibilities for provider experience and patient care, but being able to use AI tools effectively comes down to IT infrastructure. Have an organization’s speeds and feeds been upgraded to handle these solutions?
Organizations looking to implement AI solutions should also work on getting their data shop in order. Even out-of-the-box, consumable AI solutions require good data governance. The more an organization understands its own data, the easier it is for the IT team to know how a solution will work in the organization’s environment. If the data isn’t in order, then AI implementation is going to be more of a challenge.
AI governance is another important factor in achieving implementation success. An organization should have a good definition of what AI is and be able to ensure that the right people exist within the organization to discuss potential risks, ROI and cultural readiness. It’s worth having those discussions early and often to corral the different AI interests inside the organization.
CONSIDER: When is the cloud right for healthcare organizations deploying AI?
IT leaders also need to consider how to integrate AI solutions into workflows effectively while gaining user buy-in. The solution may be great, but if it’s implemented incorrectly, then you might as well have done nothing at all.
Most healthcare organizations have limited budgets; therefore, some AI tools will make the cut while others won’t. Tools that don’t solve an existing problem or provide some form of return on the money being spent will be a lower priority for an organization, which may choose to do what it’s always done instead.
Once healthcare organizations are ready to move to the next step of AI implementation, it can be helpful to collaborate with a technology partner that has experience in the realm. CDW offers data workshops to help healthcare organizations prepare their data for AI. We also offer strategy engagements for modern data platforms and deploying generative AI.
We can also help executive teams level-set on what AI is and isn’t, and how to approach management. Ultimately, we help guide healthcare organizations to ensure that their AI initiative is sustainable and becomes part of their culture.
This article is part of HealthTech’s MonITor blog series.
