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.

Figure 1.

Association between the Patient Portal Secure Messages and Audit Log. The 2 datasets are related through PATID, ENCID, and MSGID. The Patient Portal Secure Messages example shows message exchanges between a patient and provider. The Audit Log example shows the provider conducting clinical activities for 2 patient messages—message identifiers 300 on September 1, 2024 and 370 on September 3, 2024. ENCID, message encounter unique identifier; INFOVIEW, clinical information access; KD, knowledge domain; MSGID, message unique identifier; MSGNOTE, message note; MSGSUBJ, message subject; PATID, patient unique identifier; PROVID, provider unique identifier; TIME, timestamp of clinical activity.

Figure 2.

Mixed effect modeling visuals. These bar plots visually represent the impact of each KD on the dependent variable (SM billing), with error bars indicating the SEs. The vertical dashed line at zero helps identifies whether the coefficients are positive or negative, which is important for understanding their effect. KD, knowledge domain; SM, secure messaging.