Optimizing Patient Communication: Streamlining Patient Email Management with Medical Language Models"

Florian O. Stummer, Konstantin Moser, Felix Bauch, Thomas Frese

Keywords: Medical Language Model, Primary Care Efficiency, Patient Communication, Artificial Intelligence in Primary Care

Background:

Primary care centers face significant challenges in managing the high volume of patient emails. These emails often include inquiries about medical concerns, appointments, prescriptions, or general health information. The manual processing of these communications by medical staff is labor-intensive and resource-consuming. This project explores the application of a Medical Language Model (MLM) to automate and improve email management in primary care.

Research questions:

Can a MLM efficiently automate the email management process in primary care centers, thereby reducing staff workload while maintaining patient satisfaction and care quality?

Method:

Using a dataset of anonymized patient emails spanning 3–5 years, the MLM will be trained to understand and respond to typical inquiries based on medical guidelines and the services offered by the care center. The system will integrate with existing email platforms to provide real-time, automated responses, ensuring privacy compliance with GDPR standards. Evaluation metrics include response time, accuracy of answers, patient satisfaction, and staff workload reduction.

Results:

The automated system is anticipated to significantly enhance efficiency by minimizing manual email handling and enabling staff to focus on complex cases. Patients are expected to benefit from quicker response times and consistent, evidence-based information. Additionally, the system has potential for analyzing patient communication trends to inform resource allocation and healthcare service planning.

Conclusions:

This research project demonstrates the transformative potential of integrating advanced NLP technologies like Medical Language Models into primary care, paving the way for scalable and efficient patient communication solutions.

Points for discussion:

How can we ensure patient confidentiality and compliance with GDPR during the anonymization and processing of sensitive medical data?

Will automation affect the personal touch and trust in patient-provider interactions?

How adaptable is the model to other languages, medical systems, or regions with varying healthcare infrastructures?

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