What Is the Impact of AI on Clinical Workflow Efficiency?

By Rasit Dinc

In the ever-evolving landscape of healthcare, the quest for greater efficiency is a constant pursuit. Clinicians are often bogged down by a mountain of administrative tasks, which can detract from their primary focus: patient care. The electronic health record (EHR), while a powerful tool, has paradoxically contributed to this burden, with studies showing that physicians spend nearly two hours on EHR tasks for every hour spent with patients [1]. This is where Artificial Intelligence (AI) emerges as a transformative force, promising to revolutionize clinical workflows and restore the human touch to medicine.

One of the most significant impacts of AI on clinical workflow efficiency is in the realm of clinical documentation. Ambient listening technology, a form of AI, is being used to create “ambient medical scribes” that can listen to and transcribe patient-physician conversations in real-time. These AI-powered tools then summarize the conversation, organize it into a structured format, and integrate it into the EHR. This automation of clinical documentation significantly reduces the time clinicians spend on note-taking, freeing them up to engage more meaningfully with their patients. A physiatrist at the Mayo Clinic reported that using an ambient AI tool has “significantly decreased my cognitive burden,” leaving him “fresher for the rest of the day” [2]. By shouldering the documentation load, AI not only improves efficiency but also combats clinician burnout.

Beyond documentation, AI is streamlining a multitude of other clinical processes. AI-powered tools can automate repetitive administrative tasks such as scheduling, billing, and data entry, which have traditionally consumed a significant portion of a clinician's day. In the critical area of medication safety, AI algorithms can rapidly analyze a patient's medical history, allergies, and comorbidities to identify potential drug interactions and recommend the most appropriate medications. This not only saves time but also enhances patient safety. Furthermore, in fields like radiology, AI is being used to prioritize urgent cases, reduce diagnostic errors, and improve the overall efficiency of the imaging workflow [3].

However, the successful integration of AI into clinical workflows is not a simple plug-and-play process. It requires a thoughtful and strategic approach. A clear roadmap for implementation is essential, starting with the identification of a specific clinical problem that AI can address. This is followed by the acquisition of high-quality data, the development and rigorous validation of the AI model, and its seamless integration into the existing clinical workflow. Crucially, the process does not end with deployment. Continuous monitoring and surveillance of the AI model's performance are necessary to ensure its ongoing accuracy and effectiveness, and to mitigate any potential for bias or performance degradation over time [4].

In conclusion, AI is not a futuristic concept in healthcare; it is a present-day reality that is already making a tangible impact on clinical workflow efficiency. From automating clinical documentation and administrative tasks to enhancing diagnostic accuracy and medication safety, AI is empowering clinicians to work smarter, not harder. By reducing the administrative burden, AI is enabling healthcare professionals to redirect their time and energy to what matters most: providing compassionate, patient-centered care. As AI technology continues to mature, its role in healthcare is set to expand, and its responsible and strategic implementation will be key to unlocking its full potential to create a more efficient, effective, and humane healthcare system.

References

[1] Redesigning Clinical Workflows with Generative AI - NEJM AI. (2025). Retrieved from https://ai.nejm.org/doi/full/10.1056/AI-S2501083

[2] Impact of artificial intelligence-based clinical documentation tools on clinical workflow - Mayo Clinic. (2025). Retrieved from https://www.mayoclinic.org/medical-professionals/physical-medicine-rehabilitation/news/impact-of-artificial-intelligence-based-clinical-documentation-tools-on-clinical-workflow/mqc-20590250

[3] The power of artificial intelligence in clinical workflows - Merative. (2025). Retrieved from https://www.merative.com/blog/artificial-intelligence-for-clinical-workflows

[4] Implementing AI Models in Clinical Workflows: A Roadmap - PMC. (2025). Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC11666800/