UTHealth creators have utilized artificial intelligence (AI) to create a software tool that is integrated with existing electronic health records (EHR) systems to automate medical fax management, resulting in a 3.6x reduction in fax processing time or 60k hours compared to legacy systems. Efficiency in time and money on this task helped conserve resources so billing providers could see more patients. iDFax has been launched at UTHealth clinics, integrated with EHR, and has processed over 1.5 million faxes to date.
Current Challenges with Fax Processing
Despite advances in interoperability and EHR usage, healthcare organizations still rely on fax to share critical medical information such as referrals, prescriptions, and authorizations. The existing fax processing workflow is highly manual, with data entry, indexing, and search all requiring direct action from clinical staff. There is no commercially available, automated and secure solution on the market that can alleviate workforce strain and maximize efficiency and outcomes.
Introducing: iDFax
UTHealth creators have developed iDFax, a cloud-based, HIPAA-compliant AI solution that helps healthcare enterprises save time, reduce workforce strain, and improve patient care, through instant and automatic fax document processing. iDFax reduces human processing time by 3.6x leveraging AI-powered automation of manual tasks, supporting expedited referrals and improving patient care. iDFax is built to support healthcare enterprises, with HIPAA-compliant technology, a user-friendly software, and secure integration with existing IT infrastructure. iDFax automatically extracts physician name, patient name, and patient date of birth while identifying document type. iDFax also automatically retrieves and links patient records from EHR if they exist, only requiring verification of important details.
Technology Differentiators
Key Outcomes
iDFax reduces fax processing time by at least 3.6x or 60k hours of time saving on 1.5 million faxes at currently deployed sites.
Intellectual Property Status
Available for licensing
About the Creators
Martin J. Citardi, M.D.
Chair and Professor of Department of Otorhinolaryngology at UTHealth Houston
Xiaoqian Jiang, Ph.D.
Associate VP for Medical AI, Chair of Department of Health Data Science and Artificial Intelligence, Professor in Biomedical Informatics and Bioengineering at UTHealth Houston
Jiajie Zhang, Ph.D.
Dean, Professor, Chair of McWilliams School of Biomedical Informatics at UTHealth Houston
UTHealth Ref. No: 2025-0024