8E500251 Data Literacy in Medicine

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School Common Graduate Courses (Master’s Course)

Lecture Code 8E500251 Subject Classification Common Graduate Courses

Subject Name 医療情報リテラシー

Subject Name
（Katakana）イリョウジョウホウリテラシー

Subject Name in
English Data Literacy in Medicine

Instructor AKITA TOMOYUKI,YOSHINAGA SHINJI,ABE SHINICHI,MIKI DAIKI,KUBO TATSUHIKO,HINOI TAKAO,TANAKA TAKESHI,SUGIYAMA AYA

Instructor
(Katakana) アキタ　トモユキ,ヨシナガ　シンジ,アベ　シンイチ,ミキ　ダイキ,クボ　タツヒコ,ヒノイ　タカオ,タナカ　タケシ,スギヤマ　アヤ

Campus Kasumi Semester/Term 1st-Year,  Second Semester,  3Term

Days, Periods, and Classrooms (3T) Thur13-14：Online

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

(1st to 7th lesson)Data Literacy in Medicine will be held as an Online lecture.
There are lectures delivered on-demand and live stream.
For on-demand lectures, please take the lecture within one week from the　scheduled lecture date.
In the case of live stream lecture, please be sure to attend during the　scheduled lecture time.
Only if you cannot take the live lecture on time, you can receive the recorded lecture.

Final exam: Face-to-face

Credits 1.0 Class Hours/Week 2 Language of Instruction B : Japanese／English

Course Level 7 : Graduate Special Studies

Course Area（Area） 27 : Health Sciences

Course Area（Discipline） 01 : Medical Sciences

Eligible Students Master's course

Keywords Epidemiology, Big data, Genome information, Medical research, Clinical research, Medical Information

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline In medical research, the importance of fields dealing with medical information is increasing. This includes the development of new cancer treatments using cancer genome information, clinical research aimed at establishing effective therapies, and epidemiological studies utilizing medical records, which have become accessible as big data due to the widespread use of electronic health records.
Therefore, to excel in future healthcare-related fields, it is essential to learn how to handle various types of medical information, including ethical considerations such as personal data protection.
This course, based on the participants' fundamental knowledge of medicine and healthcare, provides basic explanations and practical exercises on the knowledge and techniques necessary for the appropriate handling, processing, and analysis of medical information.

Class Schedule lesson1　（10/2）Guidance, Acquisition and Utilization of Medical Information in Epidemiological Research
<on-demand>
lesson2　（10/9）Use of data from vital statistics and National Cancer Registry and their use in epidemiological studies
<on-demand>
lesson3　（10/23）Classifications of genomic information,  merits, demerits and usefulness for research using genomic information
<on-demand>
lesson4　（10/30）Types of medical data, system issues, and usefulness of information in the medical field
<live stream>
lesson5　（11/6）Overview of cancer genome information and challenges
<on-demand>
lesson6　（11/13）How to establish standard clinical data set during emergencies
<on-demand>
lesson7　（11/20）Digital technology in Healthcare Field: Application, future prospect and challenges
<on-demand>
lesson8　（11/27）Final exam <face-to-face>

The final exam will generally be conducted in person. However, if you are unable to attend the in-person exam due to unavoidable reasons, such as residing in a remote area, an online final exam will be held at the same time as the in-person exam.

If you are unable to take the in-person exam due to unavoidable reasons, such as residing in a remote area, an online final exam will be conducted at the same time as the in-person exam.
For the online final exam, a file containing the answer sheet (without the exam questions) will be distributed in advance. You must print it out or prepare it in advance. The exam questions will be provided at the scheduled start time.
During the exam, you must keep your camera on in Microsoft Teams while taking the test. Answers must be handwritten (typing is not allowed). After the exam, you must submit your answers as photo or scanned data via the designated upload system or email within 30 minutes.
If you cannot set up this environment, you will not be able to take the online exam.

After each lecture, students are required to submit a report and practical work results. Reports should be submitted using Microsoft Forms (or the Dropbox file upload system if necessary). The URLs for Microsoft Forms and other submission platforms will be posted on Momiji and Moodle.

Text/Reference
Books,etc. Due to the broad scope of the lecture content, no specific textbook will be designated. There are numerous books available on topics covered in the lectures, such as epidemiology, medical informatics, and genomic medicine. Students are encouraged to refer to those that best suit their needs.

PC or AV used in
Class,etc. Handouts, Microsoft Teams, Microsoft Stream, Microsoft Forms, moodle

(More Details) Handout・Picture（Video/PC/Other）

Learning techniques to be incorporated Discussions, Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report

Suggestions on
Preparation and
Review With the advancement of information technology and data analysis techniques, as well as revisions to ethical guidelines and personal information protection laws, the concept of appropriate handling of medical information is evolving each year. This course presents several case studies in the medical field and encourages students to consider the appropriate utilization of medical information in their respective areas based on the lecture content.

Requirements ・Basic medical knowledge is assumed. (Although non-medical graduate students may take this course, no special consideration will be given. If you feel that your knowledge is insufficient, you are expected to take an active learning approach by asking questions or conducting independent research.)
・This course includes exercises and practical sessions, which may require additional time beyond video lectures.
・This course is designed to accommodate international students, and each instructor provides English support to meet the required standards.
・The course includes both on-demand and live lectures, each with designated viewing periods and assignment submission deadlines. Be sure to check and adhere to these deadlines.
・Any academic misconduct, such as plagiarism in assignments or disruptive behavior, will result in the immediate denial of course credit.
・Unless otherwise specified, do not use generative AI for report writing. If more than 70% of the content is suspected to be copied or generated by AI, as determined by a detection tool, the report may not be accepted after careful review.
By enrolling in this course, you acknowledge and agree to these conditions.

Grading Method Report submission after each lecture (submitted via Microsoft Forms) (60%)/Final exam (40%)
＊This serves as an evaluation of lecture attendance and engagement. Significant point deductions will be applied if the report contains extremely little content or if it is unclear from the content whether the lecture video was actually attended.
Each report will be graded on a 10-point scale, and the six highest scores will be used for evaluation.
The final exam will consist of written-response questions. It is planned to be in a format where students can select and answer questions from multiple question sets, including topics related to the lecture content of each session and the utilization of data in medical practice and medical research.

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it A head of EMNES will give a lecture of big data and AI technology in health care field.

Message This course is designed for international students, and the language of instruction is set to "English-supported." Depending on the instructor, different approaches may be used, such as alternating explanations in Japanese and English, conducting the lecture entirely in English, or providing slides with both Japanese and English text. Please take this into consideration before enrolling.

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	Common Graduate Courses (Master’s Course)
Lecture Code	8E500251	Subject Classification	Common Graduate Courses
Subject Name	医療情報リテラシー
Subject Name （Katakana）	イリョウジョウホウリテラシー
Subject Name in English	Data Literacy in Medicine
Instructor	AKITA TOMOYUKI,YOSHINAGA SHINJI,ABE SHINICHI,MIKI DAIKI,KUBO TATSUHIKO,HINOI TAKAO,TANAKA TAKESHI,SUGIYAMA AYA
Instructor (Katakana)	アキタ　トモユキ,ヨシナガ　シンジ,アベ　シンイチ,ミキ　ダイキ,クボ　タツヒコ,ヒノイ　タカオ,タナカ　タケシ,スギヤマ　アヤ
Campus	Kasumi	Semester/Term	1st-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Thur13-14：Online
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
(1st to 7th lesson)Data Literacy in Medicine will be held as an Online lecture. There are lectures delivered on-demand and live stream. For on-demand lectures, please take the lecture within one week from the　scheduled lecture date. In the case of live stream lecture, please be sure to attend during the　scheduled lecture time. Only if you cannot take the live lecture on time, you can receive the recorded lecture. Final exam: Face-to-face
Credits	1.0	Class Hours/Week	2	Language of Instruction	B : Japanese／English
Course Level	7 : Graduate Special Studies
Course Area（Area）	27 : Health Sciences
Course Area（Discipline）	01 : Medical Sciences
Eligible Students	Master's course
Keywords	Epidemiology, Big data, Genome information, Medical research, Clinical research, Medical Information
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	In medical research, the importance of fields dealing with medical information is increasing. This includes the development of new cancer treatments using cancer genome information, clinical research aimed at establishing effective therapies, and epidemiological studies utilizing medical records, which have become accessible as big data due to the widespread use of electronic health records. Therefore, to excel in future healthcare-related fields, it is essential to learn how to handle various types of medical information, including ethical considerations such as personal data protection. This course, based on the participants' fundamental knowledge of medicine and healthcare, provides basic explanations and practical exercises on the knowledge and techniques necessary for the appropriate handling, processing, and analysis of medical information.
Class Schedule	lesson1　（10/2）Guidance, Acquisition and Utilization of Medical Information in Epidemiological Research <on-demand> lesson2　（10/9）Use of data from vital statistics and National Cancer Registry and their use in epidemiological studies <on-demand> lesson3　（10/23）Classifications of genomic information, merits, demerits and usefulness for research using genomic information <on-demand> lesson4　（10/30）Types of medical data, system issues, and usefulness of information in the medical field <live stream> lesson5　（11/6）Overview of cancer genome information and challenges <on-demand> lesson6　（11/13）How to establish standard clinical data set during emergencies <on-demand> lesson7　（11/20）Digital technology in Healthcare Field: Application, future prospect and challenges <on-demand> lesson8　（11/27）Final exam <face-to-face> The final exam will generally be conducted in person. However, if you are unable to attend the in-person exam due to unavoidable reasons, such as residing in a remote area, an online final exam will be held at the same time as the in-person exam. If you are unable to take the in-person exam due to unavoidable reasons, such as residing in a remote area, an online final exam will be conducted at the same time as the in-person exam. For the online final exam, a file containing the answer sheet (without the exam questions) will be distributed in advance. You must print it out or prepare it in advance. The exam questions will be provided at the scheduled start time. During the exam, you must keep your camera on in Microsoft Teams while taking the test. Answers must be handwritten (typing is not allowed). After the exam, you must submit your answers as photo or scanned data via the designated upload system or email within 30 minutes. If you cannot set up this environment, you will not be able to take the online exam. After each lecture, students are required to submit a report and practical work results. Reports should be submitted using Microsoft Forms (or the Dropbox file upload system if necessary). The URLs for Microsoft Forms and other submission platforms will be posted on Momiji and Moodle.
Text/Reference Books,etc.	Due to the broad scope of the lecture content, no specific textbook will be designated. There are numerous books available on topics covered in the lectures, such as epidemiology, medical informatics, and genomic medicine. Students are encouraged to refer to those that best suit their needs.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, Microsoft Stream, Microsoft Forms, moodle
(More Details)	Handout・Picture（Video/PC/Other）
Learning techniques to be incorporated	Discussions, Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report
Suggestions on Preparation and Review	With the advancement of information technology and data analysis techniques, as well as revisions to ethical guidelines and personal information protection laws, the concept of appropriate handling of medical information is evolving each year. This course presents several case studies in the medical field and encourages students to consider the appropriate utilization of medical information in their respective areas based on the lecture content.
Requirements	・Basic medical knowledge is assumed. (Although non-medical graduate students may take this course, no special consideration will be given. If you feel that your knowledge is insufficient, you are expected to take an active learning approach by asking questions or conducting independent research.) ・This course includes exercises and practical sessions, which may require additional time beyond video lectures. ・This course is designed to accommodate international students, and each instructor provides English support to meet the required standards. ・The course includes both on-demand and live lectures, each with designated viewing periods and assignment submission deadlines. Be sure to check and adhere to these deadlines. ・Any academic misconduct, such as plagiarism in assignments or disruptive behavior, will result in the immediate denial of course credit. ・Unless otherwise specified, do not use generative AI for report writing. If more than 70% of the content is suspected to be copied or generated by AI, as determined by a detection tool, the report may not be accepted after careful review. By enrolling in this course, you acknowledge and agree to these conditions.
Grading Method	Report submission after each lecture (submitted via Microsoft Forms) (60%)/Final exam (40%) ＊This serves as an evaluation of lecture attendance and engagement. Significant point deductions will be applied if the report contains extremely little content or if it is unclear from the content whether the lecture video was actually attended. Each report will be graded on a 10-point scale, and the six highest scores will be used for evaluation. The final exam will consist of written-response questions. It is planned to be in a format where students can select and answer questions from multiple question sets, including topics related to the lecture content of each session and the utilization of data in medical practice and medical research.
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	A head of EMNES will give a lecture of big data and AI technology in health care field.
Message	This course is designed for international students, and the language of instruction is set to "English-supported." Depending on the instructor, different approaches may be used, such as alternating explanations in Japanese and English, conducting the lecture entirely in English, or providing slides with both Japanese and English text. Please take this into consideration before enrolling.
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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