Evidence map›Paper›PMID 42080934›Full record

ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2026

[Evidence generation and methodological consultation by the project "EVAluation research based on data from routine clinical care 4 the Medical Informatics Initiative" (EVA4MII)].

Michelle Pfaffenlehner, Miriam Kesselmeier, Kai Günther, Kathrin Ungethüm, Viktoria Rücker, Flavia Remo, Harald Binder, André Scherag, Peter Heuschmann, Nadine Binder and 1 more

Abstract readEnglish AbstractReview
In one paragraph

Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Michelle PfaffenlehnerInstitut für Medizinische Biometrie und Statistik, Universitätsklinikum Freiburg, Medizinische Fakultät, Universität Freiburg, Stefan-Meier-Straße 26, 79104, Freiburg im Breisgau, Deutschland. michelle.pfaffenlehner@uniklinik-freiburg.de.
Miriam KesselmeierInstitut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena & Friedrich-Schiller-Universität Jena, Jena, Deutschland.
Kai GüntherInstitut für medizinische Datenwissenschaften (ImDS), Universitätsklinikum Würzburg, Würzburg, Deutschland.
Kathrin UngethümInstitut für medizinische Datenwissenschaften (ImDS), Universitätsklinikum Würzburg, Würzburg, Deutschland.
Viktoria RückerInstitut für Klinische Epidemiologie und Biometrie, Julius-Maximilians-Universität Würzburg, Würzburg, Deutschland.
Flavia RemoInstitut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena & Friedrich-Schiller-Universität Jena, Jena, Deutschland.
Harald BinderInstitut für Medizinische Biometrie und Statistik, Universitätsklinikum Freiburg, Medizinische Fakultät, Universität Freiburg, Stefan-Meier-Straße 26, 79104, Freiburg im Breisgau, Deutschland.
André ScheragInstitut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena & Friedrich-Schiller-Universität Jena, Jena, Deutschland.
Peter HeuschmannInstitut für medizinische Datenwissenschaften (ImDS), Universitätsklinikum Würzburg, Würzburg, Deutschland.
Nadine BinderFreiburger Zentrum für Datenanalyse, Modellbildung und AI, Universität Freiburg, Freiburg im Breisgau, Deutschland.
EVA4MII-Projekt

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Routine data from healthcare are gaining importance for evidence generation. In Germany, new structures have been established in recent years to support their use. As part of the Medical Informatics Initiative (MII), data integration centres (DIZ) have been set up at all university hospitals, where patient data are made available in a pseudonymized, standardized and operationalized form. Since routine data, unlike primary data, are collected for healthcare purposes, aspects of the original data collection must be considered when formulating the research question, planning and analysing the study, and interpreting the results.The EVAluation research based on data from routine clinical care 4 the MII (EVA4MII) project supports researchers in analysing nationwide clinical routine data, for example through training programs and a central advisory service. This support is provided by an interdisciplinary team with methodological-statistical, data-technical, and clinical-epidemiological expertise in close coordination with data-providing institutions. The service covers the entire research process, from study planning and formal requirements to implementation, analysis, evaluation, and publication, and is available to projects within the MII and beyond.The aim of this article is to highlight the importance of methodological support in the analysis of clinical routine data and to identify key stages in the research process where such support is particularly relevant. Finally, an outlook on future advisory needs is provided, including the potential role of artificial intelligence as a supportive tool.

Indexed as

Evidence-Based MedicineHealth Services ResearchMedical InformaticsGermanyHumansConsultingElectronic health recordsEvaluation researchResearch cycleRoutine data

Identifiers

PMID42080934
PMCPMC13212690

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.