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A novel simulator model and standardized assessment tools for fine needle aspiration cytology training.

Publication ,  Journal Article
Alcaraz-Mateos, E; Jiang, XS; Mohammed, AAR; Turic, I; Hernández-Sabater, L; Caballero-Alemán, F; Párraga-Ramírez, MJ; Poblet, E
Published in: Diagn Cytopathol
April 2019

OBJECTIVES: Fine needle aspiration (FNA) is an invaluable diagnostic procedure for evaluation of lesions; however, acquisition of diagnostic material is dependent on the skill of the practitioner. We report a novel patient simulator for teaching the FNA procedure and structured assessment tools for educators and learners. METHODS: We created a novel simulator model for FNA training, employed a standardized teaching module, and assessed procedure utility in medical students. Groups of students completed training using a commercial version of the model, and underwent structured evaluation using an Objective Structured Assessment of Technical Skills (OSATS) form, and the Debriefing Assessment for Simulation in Healthcare (DASH) tool. RESULTS: In the initial phase, 178 students rated the training workshop between valuable and essential (4.2 on a 5-point Likert scale). In the second phase, for students evaluated with the OSATS form, the mean overall score was 33 out of 50 (range 26-43). The areas of weakness for the participants were: (a) compression after the FNA procedure, (b) completion of the informed consent, and (c) correct explanation of the procedure to the patient. For the group of students that completed the DASH questionnaire, the results were: 6.2 (assessment by students) and 6.7 (assessment by instructor) out of a maximum of 7. CONCLUSION: A realistic simulation model, in combination with a standardized training program with formal assessment methods is a valuable tool to teach FNA. We here describe a process for teaching the FNA procedure to interested educators and learners.

Duke Scholars

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Published In

Diagn Cytopathol

DOI

EISSN

1097-0339

Publication Date

April 2019

Volume

47

Issue

4

Start / End Page

297 / 301

Location

United States

Related Subject Headings

  • Simulation Training
  • Pathology
  • Medical Oncology
  • Humans
  • Education, Medical, Graduate
  • Biopsy, Fine-Needle
  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

Citation

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Alcaraz-Mateos, E., Jiang, X. S., Mohammed, A. A. R., Turic, I., Hernández-Sabater, L., Caballero-Alemán, F., … Poblet, E. (2019). A novel simulator model and standardized assessment tools for fine needle aspiration cytology training. Diagn Cytopathol, 47(4), 297–301. https://doi.org/10.1002/dc.24105
Alcaraz-Mateos, Eduardo, Xiaoyin Sara Jiang, Ahmed Abdelraheem Reda Mohammed, Iva Turic, Laura Hernández-Sabater, Fuensanta Caballero-Alemán, Manuel José Párraga-Ramírez, and Enrique Poblet. “A novel simulator model and standardized assessment tools for fine needle aspiration cytology training.Diagn Cytopathol 47, no. 4 (April 2019): 297–301. https://doi.org/10.1002/dc.24105.
Alcaraz-Mateos E, Jiang XS, Mohammed AAR, Turic I, Hernández-Sabater L, Caballero-Alemán F, et al. A novel simulator model and standardized assessment tools for fine needle aspiration cytology training. Diagn Cytopathol. 2019 Apr;47(4):297–301.
Alcaraz-Mateos, Eduardo, et al. “A novel simulator model and standardized assessment tools for fine needle aspiration cytology training.Diagn Cytopathol, vol. 47, no. 4, Apr. 2019, pp. 297–301. Pubmed, doi:10.1002/dc.24105.
Alcaraz-Mateos E, Jiang XS, Mohammed AAR, Turic I, Hernández-Sabater L, Caballero-Alemán F, Párraga-Ramírez MJ, Poblet E. A novel simulator model and standardized assessment tools for fine needle aspiration cytology training. Diagn Cytopathol. 2019 Apr;47(4):297–301.
Journal cover image

Published In

Diagn Cytopathol

DOI

EISSN

1097-0339

Publication Date

April 2019

Volume

47

Issue

4

Start / End Page

297 / 301

Location

United States

Related Subject Headings

  • Simulation Training
  • Pathology
  • Medical Oncology
  • Humans
  • Education, Medical, Graduate
  • Biopsy, Fine-Needle
  • 3202 Clinical sciences
  • 1103 Clinical Sciences