Artificial intelligence (AI) has revolutionized the way industries operate, and healthcare is no exception.
Since the introduction of generative AI tools like large language models (LLMs), AI has become a centerpiece in conversations about innovation. While for some organizations AI remains a buzzword, the possibilities it offers to enhance both clinical and administrative workflows have captured widespread attention.
Over the past year, early adopters have showcased these possibilities, prompting others to explore AI cautiously but with growing interest.
As we move into 2025, healthcare organizations are expected to adopt AI more widely, with a deliberate focus on tools that address specific business needs and provide measurable returns. Leaders are demonstrating greater risk tolerance for AI initiatives, but they’re also prioritizing solutions that yield clear ROI—whether through cost savings, improved patient care, or greater administrative efficiency.
One of the most impactful use cases we’ve seen is ambient listening technology. These AI-powered audio tools analyze patient-provider conversations in real time, extracting relevant details to create clinical notes that meet billing and coding requirements. By reducing the documentation burden on clinicians, this technology allows them to focus fully on patient care. Ambient listening also plays a critical role in addressing clinician burnout, a significant challenge in healthcare today. Many organizations view these tools as a low risk starting point, given their proven efficiency and clear ROI.
Another promising area is machine vision. By integrating cameras, sensors, and microphones into patient rooms, healthcare providers can gather actionable data to improve care. For instance, cameras can detect when a patient turns over in bed, reducing unnecessary manual interventions, or alert staff if a patient tries to get up, potentially preventing falls. As this technology evolves, it will increasingly complement tools like ambient listening, enhancing both patient care and clinical workflows.
Generative AI is also making strides, particularly through retrieval-augmented generation (RAG). This framework combines generative AI chatbots with traditional data storage systems, allowing organizations to provide more accurate and context-specific answers in Q&A applications. Such tools address common pitfalls in generative AI, like hallucinations, by relying on validated organizational data. Synthetic data, used for model testing and validation, is also gaining traction as a way to ensure AI tools deliver on their promises.
In the past, healthcare organizations often lacked the knowledge to evaluate AI models critically. Today, with more education and resources available, leaders are scrutinizing AI performance claims more thoroughly. Initiatives like those from the Coalition for Health AI are providing frameworks to ensure AI solutions meet performance and safety standards. This increased oversight benefits organizations by promoting trust and transparency.
However, the journey to effective AI adoption isn’t without challenges. Regulatory scrutiny is growing, and organizations must ensure compliance with standards like the ONC’s HTI-1 Final Rule on health data and interoperability. Balancing regulation with innovation will be key to fostering AI’s potential without stifling progress.
Successful implementation also hinges on robust IT infrastructure and data governance. Even out-of-the-box AI tools require clean, well-organized data to function effectively. Organizations must prepare their data environments, ensuring they understand their data assets and can integrate AI solutions seamlessly into existing workflows. Governance is equally important: leaders need clear definitions of AI’s purpose, along with strategies to manage risks, measure ROI, and ensure cultural readiness.
Without user buy-in and proper integration, even the most advanced AI tools can fall short. IT and clinical teams must collaborate closely to align AI with workflow needs, gaining trust and engagement from end users. Limited budgets also mean that organizations must prioritize solutions that solve specific problems and deliver tangible benefits.
Looking ahead, AI will increasingly become a part of healthcare’s cultural and operational fabric. To ensure success, organizations must not only adopt the right tools but also foster a culture of innovation and preparedness. Collaboration with experienced technology partners can further support these efforts, guiding organizations through strategy development, data preparation, and sustainable AI implementation.
AI has the potential to transform healthcare, improving both provider experiences and patient outcomes. With careful planning, thoughtful governance, and a commitment to addressing real-world challenges, healthcare organizations can harness AI to drive meaningful, lasting improvements.
The next few years will be pivotal in shaping how AI becomes a core part of healthcare’s evolution.
As discussed last week, payments to Medicare Advantage (MA) plans are adjusted to account for expected healthcare resource consumption by enrollees. Higher risk scores increase
EDITOR’S NOTE: This piece on RADV audits, written by Frank Cohen, was published on RACmonitor on Aug. 25, 2025. It’s being republished on ICD10monitor because
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