Implementation of Artificial Intelligence–Powered Patient Simulation for Communication Training in a Telehealth Transitional Care Program

Introduction: Effective communication during care transitions is essential for patient safety and quality outcomes. Telehealth-based transitional care programs decrease 30-day readmissions and improve patient satisfaction, yet staff frequently report the need for structured communication training. Artificial intelligence (AI)–supported simulation may offer a scalable training method for health care settings with limited simulation resources. This study evaluates whether AI-powered simulation training improves communication performance during real telehealth patient encounters. Methods: This quality improvement initiative was conducted within a hospital-based transitions of care program (June 2025-December 2025). Sequential implementation included rubric validation (Lawshe Content Validity Index), AI platform testing, online modules, and individualized AI simulation across 3 patient pathways (inpatient, postoperative, and emergency department). Pathway-specific rubrics demonstrated excellent interrater reliability (Intraclass Correlation Coefficient = 0.91 to 0.97). Weekly communication performance was tracked using run charts and statistical process control; balancing measures included call duration and calls per hour. Results: Fifteen of 17 eligible staff completed role-specific simulation. Communication performance was assessed across 468 recorded patient calls. Total rubric scores improved: inpatient by 24% ( P < 0.001), postoperative by 19% ( P < 0.001), and emergency department by 37% ( P < 0.001). Statistical process control analysis demonstrated special cause variation with sustained median shifts across all pathways. Conclusions: AI-powered simulation training was associated with meaningful and sustained improvements in patient-provider communication during real-world telehealth encounters. Findings support AI simulation as a scalable, practical training method for telehealth programs.

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Publication Details

Journal
Simulation in Healthcare The Journal of the Society for Simulation in Healthcare
Published
2026-09-15
DOI
https://doi.org/10.1097/sih.0000000000000971
Primary Topic
Simulation-Based Education in Healthcare
Type
article
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article

Implementation of Artificial Intelligence–Powered Patient Simulation for Communication Training in a Telehealth Transitional Care Program

Farrukh N. Jafri, Roger Edwards, Michelle Elsener, Suzie Kardong-Edgren et al.
Simulation in Healthcare The Journal of the Society for Simulation in Healthcare
Simulation-Based Education in Healthcare
article

Implementation of Artificial Intelligence–Powered Patient Simulation for Communication Training in a Telehealth Transitional Care Program

Farrukh N. Jafri, Roger Edwards, Michelle Elsener, Suzie Kardong-Edgren, Kristelle Pulido, Anshul Kumar
article en

Abstract

Introduction: Effective communication during care transitions is essential for patient safety and quality outcomes. Telehealth-based transitional care programs decrease 30-day readmissions and improve patient satisfaction, yet staff frequently report the need for structured communication training. Artificial intelligence (AI)–supported simulation may offer a scalable training method for health care settings with limited simulation resources. This study evaluates whether AI-powered simulation training improves communication performance during real telehealth patient encounters. Methods: This quality improvement initiative was conducted within a hospital-based transitions of care program (June 2025-December 2025). Sequential implementation included rubric validation (Lawshe Content Validity Index), AI platform testing, online modules, and individualized AI simulation across 3 patient pathways (inpatient, postoperative, and emergency department). Pathway-specific rubrics demonstrated excellent interrater reliability (Intraclass Correlation Coefficient = 0.91 to 0.97). Weekly communication performance was tracked using run charts and statistical process control; balancing measures included call duration and calls per hour. Results: Fifteen of 17 eligible staff completed role-specific simulation. Communication performance was assessed across 468 recorded patient calls. Total rubric scores improved: inpatient by 24% ( P < 0.001), postoperative by 19% ( P < 0.001), and emergency department by 37% ( P < 0.001). Statistical process control analysis demonstrated special cause variation with sustained median shifts across all pathways. Conclusions: AI-powered simulation training was associated with meaningful and sustained improvements in patient-provider communication during real-world telehealth encounters. Findings support AI simulation as a scalable, practical training method for telehealth programs.

Simulation in Healthcare The Journal of the Society for Simulation in Healthcare
Openalex Percentile: Top 11%
Simulation-Based Education in Healthcare
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