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Peer-reviewedCC BY-NC-ND 4.0NPJ Digital Medicine

Conversational artificial intelligence for pre-procedural patient preparation: implementation, validation and patient satisfaction

  • Annapoorna Kini1
  • Andriy Vengrenyuk1
  • Derek Pineda NP1
  • Yuliya Vengrenyuk1
  • Beth Oliver DNP1
  • Grace Poon1
  • Yolanda Balce1
  • Jeffrey Bander1
  • Amanda Rhee1
  • Bruce Darrow1
  • Samin Sharma1
  • Nicholas Gavin1
  1. 1Mount Sinai Health System, New York, NY USA
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Abstract

Preparing patients for cardiac catheterization requires critical but repetitive tasks. We conducted a prospective evaluation of the AI voice assistant, Sofiya, for pre-procedural calls in our center. A customized Large Language Model was trained on medical knowledge and deployed on an agentic AI framework with conversational AI. Sofiya called patients to provide instructions, collect clinical data, and answer questions or redirect the patient to a nurse. The study consisted of a 90-day stabilization period with collaboration between clinicians and developers (Phase I), followed by a 90-day real-world application led by nurses (Phase II). The primary outcome was the overall rate of successfully completed calls reaching the end of the script with all patients answering all clinical questions. From January 16, 2025, to July 17, 2025, 1431 patients received 1606 calls from Sofiya. Phase I had 806 calls whose completion rate gradually improved, reaching an average of 86.4%. High completed call rate (87.9%) was maintained in Phase II with an additional 800 calls. AI system errors were observed in 48 (6.0%) calls during Phase I and 24 (2.6%) in Phase II. A voice-based AI assistant can successfully augment routine tasks of pre-procedural patient preparation, allowing nurses to focus on clinical aspects of patient care.

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Cite

BibTeXRIS
BibTeX
@article{kini2026conversational,
  title = {{Conversational artificial intelligence for pre-procedural patient preparation: implementation, validation and patient satisfaction}},
  author = {Annapoorna Kini and Andriy Vengrenyuk and Derek Pineda NP and Yuliya Vengrenyuk and Beth Oliver DNP and Grace Poon and Yolanda Balce and Jeffrey Bander and Amanda Rhee and Bruce Darrow and Samin Sharma and Nicholas Gavin},
  journal = {NPJ Digital Medicine},
  year = {2026},
  month = jul,
  doi = {10.1038/s41746-026-02959-x},
  url = {https://doi.org/10.1038/s41746-026-02959-x},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Conversational artificial intelligence for pre-procedural patient preparation: implementation, validation and patient satisfaction
AU  - Annapoorna Kini
AU  - Andriy Vengrenyuk
AU  - Derek Pineda NP
AU  - Yuliya Vengrenyuk
AU  - Beth Oliver DNP
AU  - Grace Poon
AU  - Yolanda Balce
AU  - Jeffrey Bander
AU  - Amanda Rhee
AU  - Bruce Darrow
AU  - Samin Sharma
AU  - Nicholas Gavin
PY  - 2026
DA  - 2026/07/01
JO  - NPJ Digital Medicine
DO  - 10.1038/s41746-026-02959-x
UR  - https://doi.org/10.1038/s41746-026-02959-x
AB  - Preparing patients for cardiac catheterization requires critical but repetitive tasks. We conducted a prospective evaluation of the AI voice assistant, Sofiya, for pre-procedural calls in our center. A customized Large Language Model was trained on medical knowledge and deployed on an agentic AI framework with conversational AI. Sofiya called patients to provide instructions, collect clinical data, and answer questions or redirect the patient to a nurse. The study consisted of a 90-day stabilization period with collaboration between clinicians and developers (Phase I), followed by a 90-day real-world application led by nurses (Phase II). The primary outcome was the overall rate of successfully completed calls reaching the end of the script with all patients answering all clinical questions. From January 16, 2025, to July 17, 2025, 1431 patients received 1606 calls from Sofiya. Phase I had 806 calls whose completion rate gradually improved, reaching an average of 86.4%. High completed call rate (87.9%) was maintained in Phase II with an additional 800 calls. AI system errors were observed in 48 (6.0%) calls during Phase I and 24 (2.6%) in Phase II. A voice-based AI assistant can successfully augment routine tasks of pre-procedural patient preparation, allowing nurses to focus on clinical aspects of patient care.
SN  - 2398-6352
ER  -