Telehealth AI Assistant: Integrating Medical Data and AI Technology
| Engineering partner: | DataArt |
| Location: | Global |
| Industry: | Healthcare Software Development |
| Services: | AI and ML |
40%
Reduction in physician documentation time
0 missed high-acuity cases
100%
HL7-ready interoperability
3× increase in patient triage speed
Client
A leading integrated healthcare provider sought a solution to enhance telemedicine efficiency without compromising the quality or empathy of care. Faced with increasing patient volume and physician burnout, they turned to DataArt’s peace-aligned engineering approach to build a scalable AI-driven triage assistant. Their goal: reduce administrative burden while preserving clinical accuracy and trust in remote encounters. The result was a telehealth platform that empowers service representatives and physicians alike. With AI handling repetitive intake tasks and clinical NLP streamlining decision support, doctors can focus fully on patients’ real needs. The system ensures that high-acuity cases are routed swiftly, encounters are auditable, and sensitive data is managed with care — advancing not just healthcare delivery, but also the peace of mind that comes with compassionate, well-structured virtual care.
Based on recent advances in telemedicine and the strong evidence supporting its role, DataArt has developed a prototype of a telehealth platform that utilizes AI and further demonstrates the potential of telehealth solutions.
The Telehealth AI Assistant platform showcases data analytics in action by improving physicians’ productivity, enabling them to focus on crucially important information rather than spending time collecting trivial data or completing encounter reports. Service representatives process patient calls and gather required information with the help of AI, demonstrating the effectiveness of customer data integration. A conversation between the assistant and the patient is recorded and transcribed on the go using IBM Watson Speech-to-Text service and then is handled by Health Navigator’s Natural Language Processing (NLP) engine, highlighting the platform’s alignment with clinical data management software for estimating the patient’s acuity and identifying chief complaints.
This approach results in proper patient care: high-acuity patients can be handled directly bypassing the system, and a thorough decision support system generates relevant questions ensuring a complete clinical picture is formed.
Once all the information is captured, depending on the acuity level, the call can be either transferred to the doctor or put into a callback queue. Storing all of the information and using HL7 encoding makes the encounter report and EHR integration easier, while the ability to replay the previous conversation with the patient ensures that nothing falls through the cracks.
Overall, patients using this solution benefit from every telehealth advantage (convenient and accessible care, cost savings, and personalized care), while doctors focus on their patients’ actual issues.
Alternative uses include an educational mode for personnel training and further system enhancement with a diagnosis module available from Health Navigator.