Francisca Rammsy

Mr. García-Valdés, Ms. Rammsy, Fuentes, Mr. Torres, Ms. Quilaleo, Muñoz, Mr. Coloanes, Ms. Diez de Medina, Chamorro-Giné, Mr. Moya-Gallardo, Prof. Damiani, and Mr. Villagrán are affiliated with Escuela Ciencias de la Salud, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.

4 publications 2023 – 2026

Research Overview

Francisca Rammsy studies how health professions students learn clinical and procedural skills, with a focus on remote and simulation-based training. Her work has shown that asynchronous online programs with structured feedback can reliably develop skills like airway suctioning and neurodynamic testing without in-person instruction. She also explores how artificial intelligence can assess feedback quality and how co-created tools can improve the feedback culture in clinical placements.

Publications

Remote Simulation as a Training Method for Artificial Airway Suctioning Technique.

2026

Respiratory care

García-Valdés P, Rammsy F, Fuentes J, Torres G, Quilaleo C +7 more

Plain English
Researchers tested whether physiotherapy students could learn a complex airway suctioning technique through a four-week remote simulation program using a 3D airway model and personalized video feedback. Students' procedural performance improved significantly across training sessions and reached a level comparable to expert performance. The findings show that remote, asynchronous simulation is a practical alternative to in-person training for high-risk clinical procedures.

View on PubMed

Co-creation of a fit-for-purpose Feedback Toolkit for clinical clerkships.

2026

Medical teacher

Fuentes-Cimma J, Sluijsmans D, Rammsy F, Villagran I, Isbej L +2 more

Plain English
This study used an eight-session co-creation process with faculty, students, and clinical teachers to design a feedback toolkit for physiotherapy clinical clerkships. The resulting toolkit included podcasts, infographics, feedback prompts, and a structured assessment tool built around three principles: building trust, planning learning opportunities, and using feedback deliberately. A pilot test confirmed the toolkit was usable and helped structure feedback conversations, though limited time for full implementation was a challenge.

View on PubMed

Quality matters: Artificial intelligence-based assessment of feedback quality predicts technical skill improvement.

2025

Surgery

Kewalramani D, Roman DS, Lagos SA, Rammsy F, Villagran I +7 more

Plain English
An AI tool called Teach1 was used to score the quality of instructor feedback given to medical students during simulated paracentesis training, and those quality scores were compared to how much students improved. Higher feedback quality scores were strongly linked to better skill gains, with each 10-point increase in quality corresponding to a measurable improvement in performance. The study shows that AI can reliably assess feedback quality at scale and that better feedback directly drives better skill development.

View on PubMed

Remote, asynchronous training and feedback enables development of neurodynamic skills in physiotherapy students.

2023

BMC medical education

Villagrán I, Rammsy F, Del Valle J, Gregorio de Las Heras S, Pozo L +6 more

Plain English
This study examined whether physiotherapy students could learn upper limb neurodynamic techniques through a fully remote, asynchronous training program during the COVID-19 pandemic. Students practiced independently, submitted performance videos, and received checklist-based feedback from instructors over four sequential sessions. Performance reached the target standard by the second session, confirming that remote asynchronous training is an effective way to build hands-on clinical skills when in-person instruction is unavailable.

View on PubMed

Publication data sourced from PubMed . Plain-English summaries generated by AI. Not medical advice.