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Decision Support in Medical Care

How to access knowledge for clinical decision support

University Medical Center Göttingen 

This module is part of the certificate ”Digitalisation of Clinical Research and Care

Teaching Language: English and German

Workload: 0h presence / 56h online / 124h self-study = 180h total1 (6 ECTS) 

Target Group: Students and medical personnel need to be prepared to deal with such decision scenarios by using adequate strategies. A part of these strategies can be the usage of clinical decision support systems (CDSS). Therefore, they should know the options, strengths, and weaknesses of digital decision support systems in medical care and use of such systems in training environment. 

The course primarily aimed at students of medicine and medical informatics as well as physicians in postgraduate training or continuing medical education. The course is offered in close cooperation with the German Medical Association and the industrial partner Ada Health GmbH.

Pretty young student studying at home sitting at her dining table with a large binder of notes checking something on the screen of her laptop computer
top view of Medicine doctor hand working with modern computer and smart phone on wooden desk as medical concept-1
Focused classmates studying together and using laptop in library

Consultation & Registration:
If you have any questions, please do not hesitate to contact us:

Form of teaching: Etivities, Consultations, Educational videos, Quiz, Ada App

Clinical decisions are substantially complicated by the increasing diagnostic differentiation of personalized medicine, highly specific strategies in the treatment of patients, a growing disease spectrum due to increasing migration of pathogens, and increasingly complex comorbidities in elderly patients. This results in considerable possibilities for incorrect treatment. Additionally, this complexity hampers the communication between the physicians involved in decision-making, as well as the patients’ participation in these processes.

Competence Level & Competence Type |
Competence Description

1. Level: Knowing & Understanding / Professional competence

After successful completion oft he module, learners will be able to:

  • describe the evolution of data science aspects of precision medicine and explain approaches to clinical data management.
  • name new data sources and describe their technical background.

2. Level: Apply & Analyze / Professional competence 

After successful completion of the module, learners will be able to:

  • distinguish traditional types of clinical and research data collection including metadata from new forms and needs
  • describe the types of decision support systems, and name the corresponding mathematical support options
  • assess and outline which clinical decision-making processes can be supported by modern data science 

3. Level: Evaluate & synthesize / Professional competence 

After successful completion of the module, learners will be able to:

    • explain the strengths and weaknesses of different types of data collection with regard to different decision-making situations and evaluate them using examples
    • describe, plan, and evaluate the use of data in making a clinical decision using self-selected practice examples
    • evaluate in a sophisticated manner the effects of past and anticipated development of decision support on work processes using self-selected examples
    • explain and demonstrate the developed spectrum of decision support using the Ada Health software as an example - both oriented towards physician support and patient participation



  • Berner E.S., La Lande T.J. (2016) Overview of Clinical Decision Support Systems. In: Berner E. (eds) Clinical Decision Support Systems. Health Informatics. Springer, Cham.
  • Campbell, Robert James. "The Five Rights of Clinical Decision Support: CDS Tools Helpful for Meeting Meaningful Use" Journal of AHIMA84, no.10 (October 2013): 42-47 (web version updated February 2016).
  • Wright A, Sittig DF. A four-phase model of the evolution of clinical decision support architectures. Int J Med Inform. 2008;77(10):641-649. doi:10.1016/j.ijmedinf.2008.01.004
  • Boersch I, Heinsohn J, Socher R. Wissensverarbeitung – Eine Einführung in die Künstliche Intelligenz für Informatiker und Ingenieure 2nd Ed. Spektrum Akademischer Verlag ELSEVIER, 2007, pp 8-10
  • Richter J, Vogel S. Illustration of Clinical Decision Support System Development Complexity. Stud Health Technol Inform. 2020 Jun 26; 272:261-264. doi: 10.3233/SHTI200544. PMID: 32604651.


1The times serve as rough orientation. The real times may differ.