Difference between revisions of "Sato2020"
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{{BibEntry | {{BibEntry | ||
| − | |BibType= | + | |BibType=INCOLLECTION |
|Author(s)=Kei Sato; Masaki Onishi; Ikushi Yoda; Kotaro Uchida; Satomi Kuroshima; Michie Kawashima | |Author(s)=Kei Sato; Masaki Onishi; Ikushi Yoda; Kotaro Uchida; Satomi Kuroshima; Michie Kawashima | ||
|Title=Quantitative Evaluation of Emergency Medicine Resident’s Non-technical Skills Based on Trajectory and Conversation Analysis | |Title=Quantitative Evaluation of Emergency Medicine Resident’s Non-technical Skills Based on Trajectory and Conversation Analysis | ||
| − | |Editor(s)= | + | |Editor(s)=Arash Shaban-Nejad; Martin Michalowski; David L. Buckeridge |
| − | |Tag(s)=EMCA | + | |Tag(s)=EMCA; Non-technical skills evaluation; Emergency medicine; Resident education; Trajectory analysis |
|Key=Sato2020 | |Key=Sato2020 | ||
| + | |Publisher=Springer | ||
|Year=2020 | |Year=2020 | ||
|Language=English | |Language=English | ||
| + | |Address=Cham | ||
|Booktitle=Explainable AI in Healthcare and Medicine | |Booktitle=Explainable AI in Healthcare and Medicine | ||
| − | |Pages= | + | |Pages=211–220 |
|URL=https://link.springer.com/chapter/10.1007/978-3-030-53352-6_19 | |URL=https://link.springer.com/chapter/10.1007/978-3-030-53352-6_19 | ||
| − | |DOI= | + | |DOI=10.1007/978-3-030-53352-6_19 |
|Abstract=In this paper, we propose a quantitative method for evaluating non-technical skills (e.g., leadership skills, communication skills, and decision-making skills) of Emergency Medicine Residents (EMRs) who are participating in a simulation-based training. This method creates a workflow event database based on the trajectories of and conversations among the medical personnel and scores an EMR’s non-technical skills based on that database. We installed a data acquisition system in the emergency room of Tokyo Medical University Hospital to obtain trajectories and conversations. Our experimental results show that the method can create a workflow event database for cardiac arrest. In addition, we evaluated EMRs who are beginners, intermediates, and experts to show that our method can correctly represent the differences in their skill levels. | |Abstract=In this paper, we propose a quantitative method for evaluating non-technical skills (e.g., leadership skills, communication skills, and decision-making skills) of Emergency Medicine Residents (EMRs) who are participating in a simulation-based training. This method creates a workflow event database based on the trajectories of and conversations among the medical personnel and scores an EMR’s non-technical skills based on that database. We installed a data acquisition system in the emergency room of Tokyo Medical University Hospital to obtain trajectories and conversations. Our experimental results show that the method can create a workflow event database for cardiac arrest. In addition, we evaluated EMRs who are beginners, intermediates, and experts to show that our method can correctly represent the differences in their skill levels. | ||
}} | }} | ||
Latest revision as of 01:30, 27 December 2020
| Sato2020 | |
|---|---|
| BibType | INCOLLECTION |
| Key | Sato2020 |
| Author(s) | Kei Sato, Masaki Onishi, Ikushi Yoda, Kotaro Uchida, Satomi Kuroshima, Michie Kawashima |
| Title | Quantitative Evaluation of Emergency Medicine Resident’s Non-technical Skills Based on Trajectory and Conversation Analysis |
| Editor(s) | Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge |
| Tag(s) | EMCA, Non-technical skills evaluation, Emergency medicine, Resident education, Trajectory analysis |
| Publisher | Springer |
| Year | 2020 |
| Language | English |
| City | Cham |
| Month | |
| Journal | |
| Volume | |
| Number | |
| Pages | 211–220 |
| URL | Link |
| DOI | 10.1007/978-3-030-53352-6_19 |
| ISBN | |
| Organization | |
| Institution | |
| School | |
| Type | |
| Edition | |
| Series | |
| Howpublished | |
| Book title | Explainable AI in Healthcare and Medicine |
| Chapter | |
Abstract
In this paper, we propose a quantitative method for evaluating non-technical skills (e.g., leadership skills, communication skills, and decision-making skills) of Emergency Medicine Residents (EMRs) who are participating in a simulation-based training. This method creates a workflow event database based on the trajectories of and conversations among the medical personnel and scores an EMR’s non-technical skills based on that database. We installed a data acquisition system in the emergency room of Tokyo Medical University Hospital to obtain trajectories and conversations. Our experimental results show that the method can create a workflow event database for cardiac arrest. In addition, we evaluated EMRs who are beginners, intermediates, and experts to show that our method can correctly represent the differences in their skill levels.
Notes
