Evidence map›Paper›PMID 32410053›Full record

ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2020

[Digital data for more efficient prevention: ethical and legal considerations regarding potentials and risks].

Minou Friele, Peter Bröckerhoff, Wiebke Fröhlich, Indra Spiecker Genannt Döhmann, Christiane Woopen

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Minou FrieleCologne Center for Ethics, Rights, Economics, and Social Sciences of Health (ceres), Universität zu Köln, Köln, Deutschland. minou.friele@uk-koeln.de.
Peter BröckerhoffCologne Center for Ethics, Rights, Economics, and Social Sciences of Health (ceres), Universität zu Köln, Köln, Deutschland.
Wiebke FröhlichForschungsstelle Datenschutz, Institut für Europäische Gesundheitspolitik und Sozialrecht (ineges), Goethe-Universität Frankfurt a.M., Frankfurt a.M., Deutschland.
Indra Spiecker Genannt DöhmannForschungsstelle Datenschutz, Institut für Europäische Gesundheitspolitik und Sozialrecht (ineges), Goethe-Universität Frankfurt a.M., Frankfurt a.M., Deutschland.
Christiane WoopenCologne Center for Ethics, Rights, Economics, and Social Sciences of Health (ceres), Universität zu Köln, Köln, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digitization offers considerable potential for strengthening prevention in the healthcare system. Data from various clinical and nonclinical sources can be collected in a structured way and systematically processed using algorithms. Prevention needs can thus be identified more quickly and precisely, and interventions can be planned, implemented, and evaluated for specific target groups. At the same time, however, it is necessary that data processing not only meets high technical but also ethical standards and legal data protection regulations in order to avoid or minimize risks.This discussion article examines the potentials and risks of digital prevention first from a "data perspective," which deals with the use of health-related data for the purpose of prevention, and second from an "algorithm perspective," which focuses on the use of algorithmic systems, including artificial intelligence, for the assessment of needs and evaluation of preventive measures, from an ethical and legal point of view. Finally, recommendations are formulated for framework conditions that should be created to strengthen the further development of prevention in the healthcare system.

Indexed as

AlgorithmsArtificial IntelligenceMoralsBioethicsDatasets as TopicDelivery of Health CareElectronic Health RecordsGermanyHumansAlgorithmsArtificial intelligenceBig dataNeeds assessmentPrevention

Identifiers

PMID32410053

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.