Secure messaging telehealth billing in the digital age: moving beyond time-based metrics

Abstract

Objective: We proposed adopting billing models for secure messaging (SM) telehealth services that move beyond time-based metrics, focusing on the complexity and clinical expertise involved in patient care.

Materials and methods: We trained 8 classification machine learning (ML) models using providers' electronic health record (EHR) audit log data for patient-initiated non-urgent messages. Mixed effect modeling (MEM) analyzed significance.

Results: Accuracy and area under the receiver operating characteristics curve scores generally exceeded 0.85, demonstrating robust performance. MEM showed that knowledge domains significantly influenced SM billing, explaining nearly 40% of the variance.

Discussion: This study demonstrates that ML models using EHR audit log data can improve and predict billing in SM telehealth services, supporting billing models that reflect clinical complexity and expertise rather than time-based metrics.

Conclusion: Our research highlights the need for SM billing models beyond time-based metrics, using EHR audit log data to capture the true value of clinical work.

Keywords: electronic health record; machine learning; mixed effect modeling; secure messaging; telehealth billing.