Sciensus shares CareTranscribe pilot results on ambient AI in homecare

By AI, Created 2:10 PM UTC, May 21, 2026, /AGP/ – Sciensus presented early findings from its CareTranscribe pilot at ISPOR 2026, saying AI-enabled ambient speech capture could fit into routine nurse-led home visits and generate real-world evidence. The 16-week study across England and Scotland found high patient consent, strong nurse usability scores and about 90% transcription accuracy.

Why it matters: - Sciensus is testing whether ambient AI can reduce documentation burden during complex homecare visits while capturing real-world evidence that clinic-based data can miss. - The pilot suggests the technology could support nurses, improve insight generation and help services for patients receiving long-term therapies.

What happened: - Sciensus announced results this week from CareTranscribe, a feasibility pilot presented as a poster at ISPOR 2026 in the United States. - The 16-week pilot covered 100 home visits by 10 nurses across England and Scotland. - Patients in the study were receiving biologic treatment for inflammatory bowel disease.

The details: - Sciensus reported a 95% patient consent rate. - Nurses gave the system a usability score of 8.5 out of 10. - The company said transcription accuracy was about 90%. - The pilot identified four possible real-world insight areas for outcomes research: adherence barriers, safety signals, device and self-administration challenges, and daily-life factors. - Sciensus said the pilot showed ambient AI can be integrated into routine home visits in a way that is feasible for nurses and acceptable to patients. - More information is available in the company’s announcement.

Between the lines: - Homecare visits may reveal treatment behavior and practical barriers that are harder to observe in clinics. - If Sciensus can prove the workflow is safe and scalable, ambient capture could become a data layer for both care delivery and evidence generation. - Alison Griffiths, director of nursing and clinical operations at Sciensus, said the technology could cut administrative work and let nurses focus more on care. - Noolie Gregory, head of evidence generation at Sciensus, said home-based ambient capture could surface information that has been invisible to traditional data sources.

What’s next: - Phase 2 will focus on deeper analysis of real-world insight signals. - Sciensus also plans to expand into additional disease areas. - The company will look at workflow automation to reduce parallel documentation burden.

The bottom line: - Sciensus is positioning ambient AI as both a nursing support tool and a source of real-world evidence for complex home-based therapies.