The future is here, and artificial intelligence (AI) has become ingrained in clinical workflows, from documentation to decision support. But because healthcare is so personal, you can’t bring AI into the picture without keeping safety, accuracy, and security in mind.
Thinking about how to plan AI’s role in your healthcare organization? Understand how the latest technology fits into your operations, balancing best practices with sound policies to support the continuum of care.
AI has become just as common across healthcare as stethoscopes and lab coats. This advanced technology is making the most headway with clinicians, with AI being predominantly used in two areas:
Like those in many industries, healthcare professionals were reluctant to integrate AI due to perceived risks to their job security. But as it’s grown, clinicians have embraced this technology, with hospitals actively seeking vendors who offer AI features to move them into the future. Healthcare recognizes the significant potential to free up time, reduce documentation, and improve patient interaction.
Just a few years ago, AI was full of possibilities, but it was unproven. Now that we have a better understanding of what's safe and what isn’t, integrating the technology poses less risk than a few years ago. If you think back to clinical decision support, AI insights from reputable products are safe and reliable as long as they’re backed by logical policies, procedures, and human oversight for final decisions.
Your biggest risks are when your organization isn’t in control.
One tool gives way to many more. If clinicians use AI that’s outside the organization's control, they’re operating without adequate safeguards, visibility, or oversight, and it could impact security. Your best bet is to provide your team with training and standard operating procedures, such as prohibiting the use of those outside tools, to manage AI risks.
As users, clinicians and their healthcare organizations must trust that AI tools follow the rules. Vendors must ensure AI tools stay within regulatory boundaries, respecting HIPAA and not compromising personal health information (PHI). These types of platforms must pass Office of the National Coordinator (ONC) certification and comply with data security regulations and frameworks (e.g., FedRAMP, HIPAA) to maintain patient safety, privacy, and avoid fines.
The changes to healthcare operations stemming from AI are understandably intimidating. And while many concerns have subsided, healthcare leaders continue to raise a few red flags, worrying that:
AI tools seemed to burst onto the scene overnight, and healthcare organizations and vendors were understandably concerned about security and compliance issues. But rapid advancements have reduced these risks to a comparable level to any other software that’s built into a secure environment.
AI tools make plenty of mistakes—such as misunderstanding data and throwing out documentation— that healthcare can’t afford. Clinicians must review AI-generated outputs critically to ensure accuracy, reduce liability risks, and make final decisions.
AI may have relevant knowledge, but it hasn’t treated patients. Therefore, it can’t replace clinical expertise and will remain a support tool for clinicians to focus on care delivery.
The rules that have been established surrounding AI in healthcare so far have been tremendously effective, but there are opportunities for expansion. Formal entities, individual healthcare organizations, and cybersecurity each have roles to play.
Healthcare AI tools already have some degree of formal oversight, with HIPAA and ONC maintaining the top regulations for any tool that touches PHI. While each is effective at governing healthcare AI, ONC standards are expected to evolve to address AI interoperability and integration more directly.
The remaining gap? AI use still needs clear liability guidelines. Current state is these remain informal and usually fall to clinicians. This could get murkier as healthcare organizations introduce more AI because future regulations may require patient consent or notifications when AI is used for documentation.
How is your facility managing AI? Hospitals need their own AI governance policies too. Most facilities that adopt AI quickly establish safeguards around the technology, eager to drive adoption and enjoy its benefits uninterrupted.
However, without guidance from above, liability standards often remain missing at an organizational level, and it’s a key gap to fill because the tools themselves can’t assume legal responsibility. On top of this, we must also rethink transparency, implementing policies to inform patients when AI influences their care.
Phishing, ransomware, hacking—the biggest overarching risk of healthcare AI is data security, and bad actors have countless ways to obtain it. Any breach could expose patients’ PHI, especially if AI is built on open models or unsupported third-party tools.
Maximize your protection with a few best practices:
Fortunately, most healthcare software, such as electronic health records (EHRs), is built with cybersecurity—from network security to endpoint monitoring—in mind. So, if you integrate AI, it will often inherit this protection.
Integrating AI in healthcare requires thoughtful planning, including what you want to do and how to adapt your workflows. Outline your goals, infrastructure needs, and regulatory considerations, and put the pieces together.
Have use cases in mind? Identify pain points before investing in software tools, such as note-taking or clinical decision support, to ensure your AI solution is purpose-driven.
Select a product designed for your goals. Get buy-in from clinicians to better understand a given tool’s functionality and data use so you know it solves your needs.
Lean on your IT and cybersecurity teams to ensure secure interoperability and understand how AI will integrate into your infrastructure. They’ll verify that the technology can be safely deployed from a security and data architecture standpoint.
Get your team up to speed on what your new tool does. Build clinicians’ knowledge and confidence to interpret AI outputs and use them alongside medical expertise.
Collect data on how you use AI and its results across the continuum of care. How much time are you saving? Have you reduced medical errors? Enjoying more patient interactions? Justify your investment and inform where the team could improve.
Name someone who is qualified to drive your AI initiatives and maximize the benefit to your organization. Whether it’s your nurse lead or a larger governing board, an AI champion maintains ownership of software advancements, compliance, and training.
Healthcare organizations using AI tools already see the benefits for care delivery, but it still needs thoughtful implementation. Everyone from IT team members to clinicians must understand how to use the technology and the risks of misuse, with leadership providing support and guardrails for the effort.
Talk to one of Juno Health's healthcare experts to see how the advanced thinking behind Juno EHR can move your organization forward, and prepare you for the future of digital health.
Embrace innovation, growing your AI toolset across the continuum of care, from care apps to EHRs. Just remember to stay on your toes as the technology grows to protect your organization and its patients.