Using generative AI to fix overwhelming inboxes

Academic physicians can receive thousands of emails annually, ranging from clinical communications to mass-distributed invites and announcements that often contribute little to their immediate work. A larger number of emails leads to decreased workplace efficiency, increased stress, and distraction. In a 5-week JAMA Network Open study, 162 clinicians across primary care and gastroenterology tested an electronic health record–integrated large language model that drafted responses to patient portal messages. The results were: “The mean draft utilization rate was 20%, there were statistically significant reductions in burden and burnout score derivatives, and there was no change in time.” 

The findings suggest more than just a technical success. As the study authors put it: “The use of large language models in clinical workflows was spontaneously adopted, usable, and associated with improvement in clinician well-being.” AI doesn’t have to overhaul workflows to make an impact. Simply drafting responses on behalf of clinicians can lighten their cognitive load.

Generative AI leverages advanced natural language processing to understand, categorize, and generate human-like text. The technology acts like a virtual assistant, doing the initial heavy lifting of composing responses that clinicians can then review and personalize quickly. 

The scope of email overload in healthcare

A Neurology Clinical Practice study noted that an academic physician received 2,035 mass distribution emails: 74% from the medical center, 22% from the department, and 4% from the university. Mass distribution emails include “medical center, university, and departmental communications, invitations to contribute to open access journals and speak at conferences, sales information from medical/laboratory vendors, society membership updates, publication digests, and electronic tables of contents,” all flooding inboxes and eating into valuable time. 

The administrative flood contributes to burnout, fatigue, and reduced job satisfaction, issues that the healthcare system struggles to combat. 

The pressure isn’t merely inconvenient; the constant barrage heightens the risk of overlooked emails that could disrupt patient care or delay collaboration. As one commentary explains, “mass distribution physician emails result in wasted time and cost, with an estimated institutional cost of $1,029,419 to $3,088,257 annually.” Healthcare professionals are forced to act like gatekeepers, triaging messages for urgency and relevance in a workflow already stretched thin.

What is generative AI?

Generative AI uses advanced machine learning models that are trained on huge datasets to understand patterns, language structures, and context across vast amounts of information. This is enabled by technologies developed over the past decade, like generative adversarial networks (GANs). GANs work by placing two neural networks against each other, one creates content, while the other judges it, improving the quality of the generated output until it becomes impressively realistic.

It’s becoming a companion to healthcare workers by handling repetitive communication and data tasks, freeing them to focus more on patient care and less on paperwork. The market is growing rapidly, reflecting this huge potential. The study ‘Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency’ notes that from an $800 million valuation in 2022, healthcare generative AI tools are projected to skyrocket to over $17 billion by 2032, spurred by applications that support diagnostics, treatment planning, and workflow optimization.

How generative AI can address inbox overload 

Generative AI systems, powered by advanced large language models (LLMs), offer a nuanced and adaptive solution by automating email triage, composing reply drafts, detecting threats. As one Implementation Science article explains, “Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options,” and those same capabilities can extend to administrative domains like email management. 

Unlike filtering systems that rely on static rules or keyword matching, generative AI “analyses extensive datasets and generates valuable insights,” dynamically interpreting messages using training on linguistic patterns and healthcare-specific communication. This allows the AI to prioritize patient questions or clinician messages while deprioritizing routine notifications, effectively triaging the inbox and drawing attention to what truly matters. 

Learning from organizational workflows and user feedback, AI continuously improves its ability to discern which communications require immediate human attention and which can be automated or deferred. AI models analyze the content and behavioral patterns and metadata to detect phishing attempts, business email compromise (BEC), and social engineering tactics with far higher accuracy than traditional antivirus or spam filters. 

Identifying subtle deviations in sender behavior or linguistic cues that hint at malicious intent prevents dangerous emails from reaching end users’ inboxes, reducing both security risks and the resultant inbox clutter from suspicious or quarantined messages.

See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)

FAQs

How does generative AI maintain patient data privacy while scanning emails?

Generative AI systems designed for healthcare adhere to strict data governance and privacy laws such as HIPAA. They typically use encrypted data processing, avoid unnecessary storage of sensitive personal health information, and implement access controls and audit trails.

Does generative AI replace human judgment in email security?

No, generative AI acts as a powerful assistant but not a replacement. Human cybersecurity teams play a role in validating AI detections, fine-tuning threat response strategies, and reviewing AI-generated email drafts.

How do organizations implement generative AI-based inbound email security?

Implementation involves adopting AI-powered platforms, like Paubox, with its new inbound email security that complements its HIPAA compliant features.