Evidence map›Paper›PMID 32424555›Full record

ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2020

[Assessing the quality of digital health services: How can informed decisions be promoted?]

Felix G Rebitschek, Gerd Gigerenzer

Abstract readReview
PubMed Publisher
In one paragraph

Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Approaches to Improvement of Digital Health Literacy (eHL) in the Context of Person-Centered Care.International journal of environmental research and public health · 2022
    Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Felix G RebitschekMax-Planck-Institut für Bildungsforschung, Lentzeallee 94, 14195, Berlin, Deutschland. Rebitschek@mpib-berlin.mpg.de.
Gerd GigerenzerMax-Planck-Institut für Bildungsforschung, Lentzeallee 94, 14195, Berlin, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An important prerequisite for the success of the digitisation of the healthcare system are risk-literate users. Risk literacy means the ability to weigh potential benefits and harms of digital technologies and information, to use digital services critically, and to understand statistical evidence. How do people find reliable and comprehensible health information on the Internet? How can they better assess the quality of algorithmic decision systems? This narrative contribution describes two approaches that show how the competence to make informed decisions can be promoted.Evidence-based and reliable health information exists on the Internet but must be distinguished from a large amount of unreliable information. Various institutions in the German-speaking world have therefore provided guidance to help laypersons make informed decisions. The Harding Center for Risk Literacy in Potsdam, for example, has developed a decision tree ("fast-and-frugal tree"). When dealing with algorithms, natural frequency trees (NFTs) can help to assess the quality and fairness of an algorithmic decision system.Independent of reliable and comprehensible digital health services, further tools for laypersons to assess information and algorithms should be developed and provided. These tools can also be included in institutional training programmes for the promotion of digital literacy. This would be an important step towards the success of digitisation in prevention and health promotion.

Indexed as

Decision MakingHealth LiteracyTelemedicineDelivery of Health CareGermanyAlgorithmic decision systemsAlgorithm performanceDigital health informationInformed decision-makingRisk literacy

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.