Read about and report a Health Data Science Project

Here you can learn more about Health Data Science (SDV) projects. Health data science research projects are projects that solely involve research in so-called "dry data". Health data science research projects (SDV projects) concern research in sensitive bioinformatic data, i.e., there is an absence of intervention and data is retrospective.

What are health data science projects?

Health data science research projects cover the same research areas and purposes as health science research projects, but the object of research is solely the dry bioinformatic data, meaning there is an absence of intervention.

A health data science research project therefore involves:

  • Et planlagt og veltilrettelagt projekt, som anvender sensitive bioinformatiske data og som har til formål systematisk at erhverve viden om sygdoms opståen eller behandling, diagnostik, forebyggelse, rehabilitering af mennesker samt menneskets biologiske, fysiologiske eller psykologiske processer og arveanlæg.

A health data science research project must have a precisely defined purpose and be designed to achieve the described objectives.

Relevant methods are used, which can make this possible, and which can be assumed to be repeatable and reach the same conclusion. The project and its conclusions should also be assumed to provide generalizable knowledge that extends beyond the specific conditions being investigated and is of significant, relevant importance for the area that is the subject of the project.

Health data science research projects with image data have their explicit and primary focus on image data as the specific subject field of the research project. In addition to the image medium being the primary focus of the research project, data from the patient's medical records or health registries may also be included. If research also involves biological material, the project is considered a health science research project.

See examples of studies with imaging diagnostic data that fall respectively within and outside the concept of health data science research projects.

Health data science research must, in the same way as health science research projects, be distinguished from patient treatment and quality control.

SDV (Health Data Science) projects involve:

  • Research projects without biological material using existing genomic data from extensive mapping of human genetics, where the genomic data either comes from a previous research project or from patient diagnosis/treatment.
  • Research projects without biological material using existing imaging diagnostic data from a previous research project or from treatment using imaging diagnostics. The project must have its explicit and primary focus on image data as the specific subject field of the research project. In addition to the image medium being the primary focus, data from the patient's medical records or health registries may also be included.

The focus is thus on the "reuse" of already created data in the field of genomics or imaging diagnostics, either in a research context or in a treatment context. The research purpose will therefore be new. This type of project is particularly characterized by involving extensive and sensitive information about individuals, which may entail significant health findings about the individual, which may need to be reported back to the trial subject.

The project and its conclusions should also be assumed to provide generalizable knowledge that extends beyond the specific conditions being investigated and is of significant, relevant importance for the area that is the subject of the project.

Note: Research projects in the above sensitive bioinformatic data, which also involve humans or human biological material, must still be reported to the regional ethical committee and reported as a health science research project.

What is not Research, but Instead Quality Control and Quality Development?

Quality control and quality development do not need to be reported to the ethical committee.

Guidance No. 11052 of July 2, 1999, regarding the introduction of new treatments in the healthcare system, states about quality control: "Quality control does not aim to gain new knowledge about the value of treatment but tests the clinical unit's function."

Quality development is the development of new methods or new indicators within already established areas. It generates new knowledge locally within the organization, but this knowledge is not generalizable outside of it.

Quality control or quality development typically involves activities that are part of the healthcare system's operations regarding, for example, a hospital department's achieved treatment results for a specific patient group. This could involve comparing a current treatment with the adopted treatment instruction, evaluating the effectiveness and costs of different treatment principles. These are often retrospective or observational prospective studies where there is no intervention in the treatment, etc.

Document Requirements for Your Application

Are you reporting a research project that does not use biological material but uses existing data from extensive mapping of the genome or from imaging diagnostics? Guidance can be found here (the studies involve data from both diagnostics and previous research projects):

Document Requirements

Requirements for Protocol

Requirements for Protocol Summary

Also see:

Regulation on Notifiable Health Science and Health Data Science Research Projects

Last updated 01-02-2024

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