{"title":"数字时代的安全信息远程医疗计费:超越基于时间的指标。","authors":"Dong-Gil Ko, Umberto Tachinardi, Eric J Warm","doi":"10.1093/jamia/ocae250","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Secure messaging telehealth billing in the digital age: moving beyond time-based metrics.\",\"authors\":\"Dong-Gil Ko, Umberto Tachinardi, Eric J Warm\",\"doi\":\"10.1093/jamia/ocae250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":50016,\"journal\":{\"name\":\"Journal of the American Medical Informatics Association\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Medical Informatics Association\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1093/jamia/ocae250\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocae250","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
目的:我们建议对安全信息传送(SM)远程医疗服务采用计费模式:我们建议对安全信息(SM)远程医疗服务采用计费模式,这种模式超越了基于时间的衡量标准,侧重于患者护理所涉及的复杂性和临床专业知识:我们使用医疗服务提供者的电子健康记录(EHR)审计日志数据,针对患者发起的非紧急信息训练了 8 个分类机器学习(ML)模型。混合效应建模(MEM)分析了显著性:结果:准确率和接收者工作特征曲线下面积得分普遍超过 0.85,显示出强大的性能。混合效应模型显示,知识域对 SM 计费有显著影响,解释了近 40% 的方差:本研究表明,使用 EHR 审计日志数据的 ML 模型可以改进和预测 SM 远程医疗服务的计费,支持反映临床复杂性和专业知识而非基于时间指标的计费模型:我们的研究强调,除了基于时间的指标外,还需要使用电子病历审计日志数据来捕捉临床工作的真正价值,从而建立 SM 计费模型。
Secure messaging telehealth billing in the digital age: moving beyond time-based metrics.
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.
期刊介绍:
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.