How NLP Streamlines Patient Message Response Times and Reduces Staff Workload in Healthcare

Improving Clinical Response Times with NLP Message Labeling

Electronic Health Record (EHR) messaging portals have become a common channel for patients to communicate with their care teams. However, these messages often land in a central pool and require manual routing to the right clinical staff, causing delays and inefficiencies.

A recent study tested the use of a Natural Language Processing (NLP) system to automatically label and route incoming patient messages. This approach aimed to reduce the time clinical staff spend identifying message types and improve overall response times.

How the NLP Model Was Developed and Tested

The NLP model was trained on 40,000 patient messages from adult EHR records. Each message was classified into one of five categories: urgent, clinician-related, refill requests, scheduling, or form completion. These categories represent common themes that healthcare staff typically encounter.

After training, the system was deployed across four outpatient clinics. Incoming messages were either routed automatically by the NLP system (intervention group) or handled traditionally without routing (control group). Both groups included messages from the same time frame and locations to ensure fair comparison.

Key Metrics and Findings

• Initial response time: The NLP-routed messages reached healthcare staff roughly one hour faster (median difference of −1 hour; 95% CI, −1.42 to −0.5).
• Conversation completion time: Full message threads closed nearly a day earlier in the NLP group (median difference of −22.5 hours; 95% CI, −36.3 to −17.7).
• Staff messaging burden: The number of message interactions needed by healthcare staff decreased by a median of two interactions (95% CI, −2.9 to −1.4) in the NLP group.
• Labeling accuracy: The system achieved over 95% precision, recall, and accuracy across all five categories.

The results indicate that automating message categorization and routing can meaningfully cut down response times and reduce administrative workload for clinical teams.

Practical Implications for Healthcare Management

For healthcare leaders, integrating an NLP classifier into EHR messaging workflows can streamline communication pathways. Faster triage of patient messages means quicker clinical responses, which can improve patient satisfaction and operational efficiency.

Reducing the volume of manual message handling also frees staff time for higher-value tasks. This technology can be a practical step toward managing growing patient communication demands without adding to clinician burnout.

As the healthcare industry continues to adopt AI-driven tools, training and resources to understand and implement these systems will be essential. For those interested in expanding their knowledge on AI applications in healthcare, exploring targeted courses and certifications can be valuable. For example, Complete AI Training offers courses tailored to AI integration in clinical settings.