ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2024
[Establishment of the new "Health Data Lab" to provide data for science].
Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- A stakeholder-inclusive conceptual framework for modeling routinely collected health data for therapeutic decision-making illustrated by means of multistate models.BMC medical research methodology · 2026Article
- Multi-stakeholder use of patient experience data (PED): current state and future opportunities for the application of PED to inform decision-making.Journal of patient-reported outcomes · 2026Review
- [Improving record linkage for health research-how to overcome deficiencies?]Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Article
- Utilization of health-related data in the regional context for health service planning in the Federal State of Brandenburg, Germany-a qualitative study.Research in health services & regions · 2024Article
- Health economic evaluation of an electronic mindfulness-based intervention (eMBI) to improve maternal mental health during pregnancy - a randomized controlled trial (RCT).Health economics review · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of real-world data (RWD) has become increasingly important in health research in recent years. With the BfArM Health Data Lab (HDL), which is currently being set up, researchers will in future be able to gain access to routine data from the statutory health insurance of around 74 million people in Germany. Data from electronic patient records can also be made available for research prospectively. In doing so, the Health Data Lab guarantees the highest data protection and IT security standards. The digital application process, the provision of data in secure processing environments as well as the features supporting the analyses such as catalogues of coding systems, a point-and-click analysis tool and predefined standard analyses increase user-friendliness for researchers. The use of the extensive health data accessible at HDL will open a wide range of future possibilities for improving the health system and the quality of care. This article begins by highlighting the advantages of the HDL and outlining the opportunities that the RWD offers for research in healthcare and for the population. The structure and central aspects of the HDL are explained afterwards. An outlook on the opportunities of linking different data is given. What the application and data usage processes at the HDL will look like is illustrated using the example of fictitious possibilities for analysing long COVID based on the routine data available at the HDL in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.