ReviewHeredity2020
Big data in digital healthcare: lessons learnt and recommendations for general practice.
Review in Heredity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled 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.
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
59 citing papers in PubMed, 1 synthesis or guideline pooled it, 213 citations in OpenAlex.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.Journal of pain and symptom management · 2022Trial
- Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses.Nature biomedical engineering · 2026Article
- Article
- Unlocking insights: general practice staff perspectives on sharing general practice data for research - a scoping review.BMC primary care · 2025Article
- Global challenges in diabetes research and care: which way forward? An appraisal from the EASD Global Council.Diabetologia · 2025Review
- How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review.JMIR rehabilitation and assistive technologies · 2025Review
- Navigating the Transformative Impact of Artificial Intelligence in Health Services Research.Health science reports · 2025Article
- Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.BMC medical informatics and decision making · 2025Article
- Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure.Insights into imaging · 2025Article
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Mini-review of clinical data service platforms in the era of artificial intelligence: A case study of the iHi data platform.BioMedicine · 2025Review
- Synthetic data generation methods in healthcare: A review on open-source tools and methods.Computational and structural biotechnology journal · 2024Review
- Advancing governance for digital transformation in health: insights from Georgia's experience.BMJ global health · 2024Article
- PheWJAMIA open · 2024Article
- Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.Journal of medical Internet research · 2024Article
- Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis.JMIRx med · 2024Article
- Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.BMJ health & care informatics · 2024Article
- An open source knowledge graph ecosystem for the life sciences.Scientific data · 2024Article
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 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
2 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Big Data will be an integral part of the next generation of technological developments-allowing us to gain new insights from the vast quantities of data being produced by modern life. There is significant potential for the application of Big Data to healthcare, but there are still some impediments to overcome, such as fragmentation, high costs, and questions around data ownership. Envisioning a future role for Big Data within the digital healthcare context means balancing the benefits of improving patient outcomes with the potential pitfalls of increasing physician burnout due to poor implementation leading to added complexity. Oncology, the field where Big Data collection and utilization got a heard start with programs like TCGA and the Cancer Moon Shot, provides an instructive example as we see different perspectives provided by the United States (US), the United Kingdom (UK) and other nations in the implementation of Big Data in patient care with regards to their centralization and regulatory approach to data. By drawing upon global approaches, we propose recommendations for guidelines and regulations of data use in healthcare centering on the creation of a unique global patient ID that can integrate data from a variety of healthcare providers. In addition, we expand upon the topic by discussing potential pitfalls to Big Data such as the lack of diversity in Big Data research, and the security and transparency risks posed by machine learning algorithms.
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.