ReviewZeitschrift fur Rheumatologie2018
[Big data from clinical routine].
Review in Zeitschrift fur Rheumatologie, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- [Data integration centers-from a concept in the Medical Informatics Initiative to its local implementation in the Network of University Medicine].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Article
- A functional learning health system in Japan: Experience with processes and information infrastructure toward continuous health improvement.Learning health systems · 2021Article
- [Biomarkers and imaging for diagnosis and stratification of rheumatoid arthritis and spondylarthritis in the BMBF consortium ArthroMark].Zeitschrift fur Rheumatologie · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver the past 100 years, evidence-based medicine has undergone several fundamental changes. Through the field of physiology, medical doctors were introduced to the natural sciences. Since the late 1940s, randomized and epidemiological studies have come to provide the evidence for medical practice, which led to the emergence of clinical epidemiology as a new field in the medical sciences. Within the past few years, big data has become the driving force behind the vision for having a comprehensive set of health-related data which tracks individual healthcare histories and consequently that of large populations.
objectivesThe aim of this article is to discuss the implications of data-driven medicine, and to examine how it can find a place within clinical care. MATERIALS AND
methodsThe EU-wide discussion on the development of data-driven medicine is presented.
resultsThe following features and suggested actions were identified: harmonizing data formats, data processing and analysis, data exchange, related legal frameworks and ethical challenges. For the effective development of data-driven medicine, pilot projects need to be conducted to allow for open and transparent discussion on the advantages and challenges. The Federal Ministry of Education and Research ("Bundesministerium für Bildung und Forschung," BMBF) Arthromark project is an important example. Another example is the Medical Informatics Initiative of the BMBF. DISCUSSION AND
conclusionThe digital revolution affects clinic practice. Data can be generated and stored in quantities that are almost unimaginable. It is possible to take advantage of this for development of a learning healthcare system if the principles of medical evidence generation are integrated into innovative IT-infrastructures and processes.
Indexed as
Identifiers
29453548What 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.