Remote Monitoring Systems in Digital Medicine
Description: The discipline is devoted to the study of the main biomedical signals used to assess the current state of human health in remote monitoring systems; a basic set of sensors for recording biomedical signals; modern trends and approaches to instrumentation of remote monitoring systems in digital health.
Amount of credits: 5
Пререквизиты:
- Fundamentals of Informational-Instrumentation Technology
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 15 |
| Practical works | |
| Laboratory works | 30 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
| SAW (Student autonomous work) | 75 |
| Form of final control | Exam |
| Final assessment method | oral exam |
Component: Component by selection
Cycle: Base disciplines
Goal
- Formation of knowledge, skills and competencies in the field of development, implementation and analysis of remote patient monitoring systems using digital technologies, IoT and artificial intelligence. Mastering modern methods of collecting, transmitting and processing medical data for timely diagnostics, predicting patient conditions and supporting decision-making in healthcare.
Objective
- Studying the principles and architecture of remote monitoring systems, understanding how the system works to collect and analyze patient health data remotely.
- Studying sensors, wearables, and other technologies for collecting information about patient physiological parameters (heart rate, blood sugar, blood pressure, etc.).
- Mastering platforms and applications for storing, processing, and visualizing data, such as electronic medical records, cloud services, and systems for doctors and patients.
- Understanding the principles of using data to predict health conditions and identify potential risks.
- Studying the interaction of remote monitoring with traditional medical services and their effective integration into healthcare practice.
Learning outcome: knowledge and understanding
- Describe the architecture and components of remote health monitoring systems, principles of operation of software for analysis and visualization of medical data.
- Select modern technologies for collecting data on patient condition (sensors, wearable devices, mobile applications, etc.), algorithms and models for data analysis and health risk prediction.
Learning outcome: applying knowledge and understanding
- Apply remote monitoring methods and tools for collecting and analyzing health data.
- Apply predictive models and algorithms for monitoring patient health.
Learning outcome: formation of judgments
- Interpret the received data, identify risks and make informed decisions based on the analysis.
Learning outcome: communicative abilities
- Communicate in accordance with ethical standards, which is especially important in matters of confidentiality and protection of patient data.
Learning outcome: learning skills or learning abilities
- Develop and configure remote monitoring systems, including integration with electronic medical records and other digital systems.
Teaching methods
Modular learning technology
Technologies of educational and research activities
Information and Communication Technologies
Assessment of the student's knowledge
Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.
| Period | Type of task | Total |
|---|---|---|
| 1 rating | Laboratory work "Registration and transmission of biomedical signals" | 0-100 |
| Laboratory work "Integration of data from sensors into cloud systems" | ||
| Laboratory work "Cybersecurity and protection of personal data" | ||
| Boundary control 1 | ||
| 2 rating | Laboratory work "Analysis of medical data and risk prediction" | 0-100 |
| Laboratory work "Automated decision support systems" | ||
| Laboratory work "Development and testing of a monitoring system" | ||
| Boundary control 2 | ||
| Total control | Exam | 0-100 |
The evaluating policy of learning outcomes by work type
| Type of task | 90-100 | 70-89 | 50-69 | 0-49 |
|---|---|---|---|---|
| Excellent | Good | Satisfactory | Unsatisfactory | |
| laboratory work | Demonstrated excellent theoretical preparation. The necessary skills and abilities have been fully mastered. The result of the laboratory work fully corresponds to its goals. | Demonstrated good theoretical preparation. The necessary skills and abilities have been largely mastered. The result of the laboratory work generally corresponds to its objectives. | Demonstrated satisfactory theoretical preparation. Partially required skills and abilities mastered. The result of laboratory work is partially suits her goals. | Demonstrated excellent theoretical preparation. The necessary skills and abilities have been fully mastered. The result of the laboratory work fully corresponds to its goals. |
| Oral interview (written answer) on test questions | The answer qualitatively discloses the content of the topic. The answer is well structured. The conceptual apparatus is perfectly mastered. A high level of understanding of the material is demonstrated. Excellent ability to formulate your thoughts, discuss controversial positions. | The main issues of the topic are covered. The structure of the answer is generally adequate to the topic. The conceptual apparatus is mastered well. A good level of understanding of the material is demonstrated. Good ability to formulate your thoughts, discuss controversial positions. | The topic is partially covered. The answer is poorly structured. The conceptual apparatus is partially mastered. Understanding of individual points from the material on the topic. Satisfactory ability to formulate your thoughts, discuss controversial points. | The answer qualitatively discloses the content of the topic. The answer is well structured. The conceptual apparatus is perfectly mastered. A high level of understanding of the material is demonstrated. Excellent ability to formulate your thoughts, discuss controversial positions. |
Evaluation form
The student's final grade in the course is calculated on a 100 point grading scale, it includes:
- 40% of the examination result;
- 60% of current control result.
The final grade is calculated by the formula:
| FG = 0,6 | MT1+MT2 | +0,4E |
| 2 |
Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;
E is a digital equivalent of the exam grade.
Final alphabetical grade and its equivalent in points:
The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:
| Alphabetical grade | Numerical value | Points (%) | Traditional grade |
|---|---|---|---|
| A | 4.0 | 95-100 | Excellent |
| A- | 3.67 | 90-94 | |
| B+ | 3.33 | 85-89 | Good |
| B | 3.0 | 80-84 | |
| B- | 2.67 | 75-79 | |
| C+ | 2.33 | 70-74 | |
| C | 2.0 | 65-69 | Satisfactory |
| C- | 1.67 | 60-64 | |
| D+ | 1.33 | 55-59 | |
| D | 1.0 | 50-54 | |
| FX | 0.5 | 25-49 | Unsatisfactory |
| F | 0 | 0-24 |
Topics of lectures
- Concept and purpose of remote monitoring systems in medicine
- Architecture of remote monitoring systems
- Methods of biomedical data collection in remote monitoring systems
- Data transfer protocol in remote monitoring systems
- Cybersecurity and personal data protection
- Storage and processing of medical data in cloud systems
- Telemedicine and integration of remote monitoring systems with medical services
- Automated decision support systems in remote monitoring
- IoT and Wearable Technologies in Digital Medicine
- Ethics and legal aspects of remote monitoring
- Development and testing of remote monitoring systems: from concept to implementation
- Personalized monitoring systems and digital twins
- The Future of Remote Monitoring Technologies: Artificial Intelligence, Big Data and 6G
- Implantable and bioengineered sensors for remote monitoring
- Future challenges and directions of development of digital medicine
Key reading
- Telemedicinskie tekhnologii : uchebnoe posobie / M.S. Blagodareva, A.A. Kosova, N.S. Brynza, Yu.S. Reshetnikova ; [pod obshch. red. A. A. Kosovoj].— Ekaterinburg: UGMU, 2023.
- Korenevskij N.A. Proektirovanie biotekhnicheskih sistem medicinskogo naznacheniya. Obshchie voprosy proektirovaniya: ucheb. po napravleniyu podgot. ”Fotonika, priborostroenie, optich. i biotekhn. sistemy i tekhnologii”, po discipline ”Proektirovanie biotekhn. sistem med. naznacheniya”, po napravleniyu podgot. ”Biotekhn. sistemy i tekhnologii” / N. A. Korenevskij, Z.M. Yuldashev, 2018. – -308 s.
Further reading
- Korenevskij N.A. Pribory, apparaty, sistemy i kompleksy medicinskogo naznacheniya. Sredstva registracii neelektricheskih harakteristik bioob"ektov: ucheb. po napravleniyu podgot. ”Fotonika, priborostroenie, opt. i biotekhn. sistemy i tekhnologii”, ”Biotekhn. sistemy i tekhnologii” / N. A. Korenevskij, Z. M. Yuldashev, 2019. – 266 s.
- Obzor sistemy udalennogo monitoringa pacientov [Elektronnyj resurs]. – Rezhim dostupa: https://evercare.ru/sites/default/files/2021-03/Otchet_udalyonnyj_monitoring.pdf
- Krutikov V.K., Yakunina M.V. Formirovanie cifrovoj mediciny. Uchebno-metodicheskoe posobie. Kaluga: IP Strel'cov I.A. ( Izd-vo «Ejdos»). – 2022. –166 s.
