SPMM21004U Evidence Based Clinical Application of Personalised Medicine

Volume 2024/2025
Education

This course is offered as part of the Master in Personalised Medicine.

The master's program is continuing education for health professionals.

The Master of Personal Medicine has been developed in close collaboration between the four faculties of health sciences at University of Copenhagen, Aarhus University, Aalborg University and the University of Southern Denmark as well as the Technical University of Denmark. In this way, we ensure that you are taught by national experts from internationally recognized research environments in Denmark.

Read more about the programme on the website: www.personligmedicin.ku.dk

Content

Implementation of personalised medicine represents the potential for a paradigm shift in disease prevention and treatment, based on optimizing individualised intervention. gain knowledge on how to ensure that that the usefulness of this evidence is maintained over time, after its clinical application is introduced into everyday clinical settings across the health care system.

You will be introduced to the varying degrees of evidence-building, and how the scientific findings can be internally and externally validated.

Innovative approaches to obtaining evidence are discussed, with a special focus on the unique challenges that a personal medicine approach has, as opposed to traditional evidence-building, by the study of groups of patients.

Approaches to quantifying the beneficial effect of these interventions, and any unintended consequences, are elucidated.

You will gain insight into the required investments and the existing legal challenges pertaining to the implementation and maintenance of such interventions in the operational part of the healthcare system.

 

The course consists of 5 main themes:

1. Why do all patients with an illness not benefit from the same effect of a certain medicine?

  • Evidence creation
  • Phenotypes: syndrome vs. disease vs. biomarker vs. individual health status
  • Effect and side effect at group level vs. the individual level
  • The causal reasons for illness
  • Experience with well-meaning, but erroneous conclusions about using interventions that actually did not help


2. Creation of evidence for new medicines

  • RCTs: Phases before and after regulatory approval
  • Surrogate marker for effect
  • Stratification of risk
  • Effectiveness: relative vs. absolute
  • Investigator vs. industry initiated studies
  • Rare illness and evidence creation
  • N=1 studies
  • Multi-arm-multi-stage and other flexible designs
  • Observational studies: correlation and causality


3. Strategic questions as opposed to A vs. B

  • Early vs. late intervention
  • More vs. less intervention
  • New vs. established intervention


4. Draw from electronic knowledge in a specific clinical situation

  • Patient-like-me
  • Operation (drift?) in data quality can affect knowledge - and how is it counteracted
  • Authorities and hospital governance
  • Who is responsible if electronic knowledge is misleading?


5. Cost-effective treatment

  • The basic concepts and how they are calculated
  • Prioritization: Gains for the individual and for society
  • Prioritization: Who decides what is best? The doctor or the economist?
  • Ethics: Do the best for the individual vs. what society can afford
Learning Outcome

Knowledge

  • Be able to describe approaches to the development of evidence for medical interventions in patient care
  • Be able to discuss the unique challenges in obtaining this evidence in connection with personalised medicine, rather than the traditional evidence approach of groups of patients, as well as explain innovative approaches to dealing with these challenges
  • Be able to explain, in connection with the implementation of new interventions based on automated computer science, how to separate interventions where evidence of usefulness already exists (and where the automation therefore aims to optimize workflows) from interventions where the evidence base is not sufficiently substantiated
  • Be able to describe approaches and pitfalls in the introduction and maintenance of the clinical quality of new interventions based on automated computer-generated information. Both in terms of clinical value as well as legal division of responsibilities
  • Be able to account for the quantification of the clinically beneficial conditions of new interventions in relation to the necessary public investments


Competencies

  • Be able to identify the opportunities and challenges in one's own department / own subject area that are important for the development of personalised medicine
  • Be able to plan research, development, validation and implementation of clinical evidence for interventions based on personalised medicine
  • Be able to seek out and apply the latest scientific literature on these matters


Skills

  • Be able to analyze and critically approach the construction of clinical evidence by using personalised medicine in the health care system
  • Be able to communicate knowledge about these relationships to patients, colleagues and other professional groups
Literature

Articles and selected readings.

Reading list can be found on Absalon.

Read more about application requirements on the programme homepage. To find more information, please go to 'Sign Up' below.
- On-location attendance
- Teaching and group work on project

Expect significant preparation time for all parts of the course. Between the three attendance periods there will be self-study and group work.
  • Category
  • Hours
  • Lectures
  • 6
  • Class Instruction
  • 12
  • Preparation
  • 135
  • Project work
  • 35
  • Exam
  • 18
  • Total
  • 206
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Written assignment
Oral examination
Type of assessment details
The exam consists of a written course assignment followed by an oral examination. The assignment is written in continuation of the course teaching and can be prepared individually or in groups.

The scope of the exam paper is: 8 pages, if you write alone; 12 pages, when two to three, who write together; 16 pages if you are four who write together. One page is defined as 2400 number of characters (including spacing). That means, if one person writes the assignment it will be 19.200 characters; 28.800 characters if two-three people write together; 38.400 characters if four people write together.

At the oral exam, the exam paper is presented followed by verbal examination of its content.

Time for oral examination:
1 person: 20 minutes
Group of 2 people: 30 minutes
Group of 3 people: 40 minutes
Group of 4 people: 50 minutes
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
More than one internal examiner
Exam period


See information about exam time in the  exam plan. The exam plan is published on this website:  https://sund.ku.dk/uddannelse/studieinformation/eksamensplaner/

Re-exam

The exam form for the reexam is the same as the ordinary exam. 
See information about re-exam time in the  exam plan. The exam plan is published on this website:  https://sund.ku.dk/uddannelse/studieinformation/eksamensplaner/

Criteria for exam assesment

To acheive the grade 12, the student must:

Knowledge

  • Be able to describe approaches to the development of evidence for medical interventions in patient care
  • Be able to discuss the unique challenges in obtaining this evidence in connection with personalised medicine, rather than the traditional evidence approach of groups of patients, as well as explain innovative approaches to dealing with these challenges
  • Be able to explain, in connection with the implementation of new interventions based on automated computer science, how to separate interventions where evidence of usefulness already exists (and where the automation therefore aims to optimize workflows) from interventions where the evidence base is not sufficiently substantiated
  • Be able to describe approaches and pitfalls in the introduction and maintenance of the clinical quality of new interventions based on automated computer-generated information. Both in terms of clinical value as well as legal division of responsibilities
  • Be able to account for the quantification of the clinically beneficial conditions of new interventions in relation to the necessary public investments

 

Competencies

  • Be able to identify the opportunities and challenges in one's own department / own subject area that are important for the development of personalised medicine
  • Be able to plan research, development, validation and implementation of clinical evidence for interventions based on personalised medicine
  • Be able to seek out and apply the latest scientific literature on these matters

 

Skills

  • Be able to analyze and critically approach the construction of clinical evidence by using personalised medicine in the health care system
  • Be able to communicate knowledge about these relationships to patients, colleagues and other professional groups