Secure messaging telehealth billing in the digital age: moving beyond time-based metrics (2025)

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Dong-Gil Ko, PhD

Department of Operations, Business Analytics & Information Systems, Carl H. Lindner College of Business, University of Cincinnati

, Cincinnati, OH 45221,

United States

Corresponding author: Dong-Gil Ko, PhD, Department of Operations, Business Analytics & Information Systems, Carl H. Lindner College of Business, University of Cincinnati, 2906 Woodside Drive, Cincinnati, OH 45221, United States (kodg@ucmail.uc.edu)

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,

Umberto Tachinardi, MD

UC Health Digital Health Systems, UC Health

, Cincinnati, OH 45229,

United States

Department of Biostatistics, Health Informatics & Data Sciences, College of Medicine, University of Cincinnati

, Cincinnati, OH 45267,

United States

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Eric J Warm, MD

Department of Internal Medicine, College of Medicine, University of Cincinnati

, Cincinnati, OH 45267,

United States

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Journal of the American Medical Informatics Association, ocae250, https://doi.org/10.1093/jamia/ocae250

Published:

26 September 2024

Article history

Received:

25 July 2024

Revision received:

04 September 2024

Editorial decision:

13 September 2024

Accepted:

16 September 2024

Published:

26 September 2024

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    Dong-Gil Ko, Umberto Tachinardi, Eric J Warm, Secure messaging telehealth billing in the digital age: moving beyond time-based metrics, Journal of the American Medical Informatics Association, 2024;, ocae250, https://doi.org/10.1093/jamia/ocae250

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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.

secure messaging, telehealth billing, electronic health record, machine learning, mixed effect modeling

© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)

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