Requirements for doctoral courses in quality assurance of clinical/medical research
Are you planning to design a course in quality assurance of clinical/medical* research? This page describes the purpose, as well as the basic requirements for such a course in terms of content and learning objectives.
*For terminology, see the information box below.
Course requirements according to the general syllabus
KI doctoral students admitted to third-cycle education in accordance with the general syllabus applicable from 1 January 2018 or later must take a course in quality assurance of clinical/medical research (minimum of 1 credit) if their research project involves:
- participation in interventional studies involving physical and/or psychological impact, or
- handling sensitive personal data that are directly identifiable or traceable to a living individual (pseudonymised), for example through a code list. The location of the code list and whether or not the doctoral student has access to it are irrelevant.
For doctoral students conducting research solely on biological material from humans, this course is recommended but not obligatory.
Why we use the term clinical/medical research here
The term “clinical research”, as used in the general syllabus, is sometimes perceived as unclear, particularly by doctoral students in epidemiology, public health or laboratory-based research, who may not regard their work as clinical. On this webpage, we therefore also use the term “medical”, to clarify the wide range of the target group.
Target group
This type of course is aimed at a wide range of doctoral students, including those working with clinical trials, register-based research, human biological samples or other types of human research.
Purpose
The primary purpose of a course in quality assurance of clinical/medical research for doctoral students is to ensure that the doctoral students achieve the knowledge and skills necessary to responsibly conduct research involving personal data across diverse types of studies.
The purpose is also to enable doctoral students to:
- interpret and follow ethical and legal principles
- manage data responsibly
- follow the principles of Good Clinical Practice (GCP)
- critically evaluate research protocols in order to maintain high-quality and ethically sound research.
Intended learning outcomes
A course in quality assurance of clinical research for doctoral students should as a minimum, contain the following ILOs:
Upon completion of the course, students should be able to:
- interpret and apply ethical and legal frameworks – interpret and apply ethical principles, laws and regulations, including relevant data protection legislation such as the GDPR, when planning, conducting and managing research involving personal data.
- manage research data responsibly – collect, document, store, quality assure and share research data while balancing data protection requirements with open science practices (e.g. the FAIR principles)
- follow the principles of Good Clinical Practice (GCP) – explain and follow the core GCP principles in relevant research contexts
- identify and manage risks – recognise and address ethical, legal and methodological risks, including data protection issues, conflicts of interest, research misconduct (e.g. fabrication, falsification, plagiarism), as well as role-specific responsibilities to ensure research integrity
- critically assess research protocols – evaluate research protocols with regard to weaknesses, non-compliance or potential ethical, legal or methodological risks, and propose strategies to minimise these.
Course content
Minimum course content requirements
The course must, as a minimum, include:
- Ethical and legal frameworks: Key ethical principles and legal requirements (e.g. GDPR, the Declaration of Helsinki) for research planning, data handling (including anonymisation/pseudonymisation) and sharing; processes for ethical approval; compliance with national and international guidelines.
- Responsible data management: Methods for secure and responsible data collection, documentation, storage, archiving and sharing; ensuring data integrity and quality; balancing regulatory compliance with the principles of open science (e.g. the FAIR principles).
- Good Clinical Practice (GCP) and roles within research: Core GCP principles for both clinical and non-clinical research; responsibilities of sponsors, principal investigators and research teams; application in protocol development, documentation, compliance monitoring and handling of deviation.
- Research planning, risk assessment and protocol review: Key elements of study design and protocol evaluation, focusing on ethical, legal and methodological considerations, including risk identification and risk mitigation strategies.
Supplementary course content
In addition, the course may also include:
- Knowledge of regulations and procedures: Legislation relating to biobanks, registration of clinical trials, data transfer agreements, European regulations, and an introduction to Good Laboratory Practice (GLP).
- Scientific publishing and documentation: The Vancouver recommendations, responsible reporting, and use of KI’s electronic laboratory notebook (ELN) for traceable and reproducible research documentation.
- The role of AI in research: Appropriate use of AI in data analysis and reporting; requirements for transparency, reproducibility and documentation; alignment with local and international AI guidelines.
- Professional roles and societal impact: Awareness of responsibilities within the research team and critical reflection on the societal impact of research.
- Application to individual research: Applying the course principles to the doctoral students’ own projects, including the development of data management plans and receiving peer feedback to strengthen ethical, legal and methodological rigour.
Assessment
The assessment should be closely linked to the doctoral student’s own research project and guided by clear and transparent criteria.
Suggested formative and summative assignments include evaluating and/or developing research protocols and data management plans, drafting applications for ethical approval, preparing information materials for participants, and incorporating peer feedback.
Relationship to other courses
Compulsory introductory course for doctoral students: Although both courses cover GDPR, research documentation, and data management, the quality assurance course places emphasis on practical application in real research contexts, where these principles are integrated into daily research workflows.
Courses in research ethics: The research ethics courses focus on theoretical understanding, reasoning, and regulatory requirements, while the quality assurance courses apply principles of research ethics to real research processes, including risk management and context-specific challenges.
The obligatory doctoral introduction course should be completed prior to taking a course in quality assurance of clinical/medical research, whereas a course in research ethics may be taken either before or after the quality assurance course.