ReviewMethodist DeBakey cardiovascular journal
The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers.
Review in Methodist DeBakey cardiovascular journal. 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.
- Visualizing the Maternal Health Journey for Learning Health Systems: Mixed Methods Combined Experience Approach.Journal of participatory medicine · 2026Article
- Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record.The American journal of cardiology · 2023Review
- The need for increased pragmatism in cardiovascular clinical trials.Nature reviews. Cardiology · 2022Review
- 12th Korea Healthcare Congress 2021; 김치국부터 마시지 말라; The Time for Digital Health is Almost Here.Yonsei medical journal · 2022Article
- Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm.Journal of the American Heart Association · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.
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.