ArticleJournal of the American Medical Informatics Association : JAMIA2025
Secure messaging telehealth billing in the digital age: moving beyond time-based metrics.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveWe 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
methodsWe 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.
resultsAccuracy 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.
conclusionOur 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.
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Registered trials
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