ArticleFuture healthcare journal2024
Real-world learnings for digital health industry
Article in Future healthcare journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial intelligence in the NHS: Moving from ideation to implementation.Future healthcare journal · 2024Article
- Artificial intelligence: friend or foe?Future healthcare journal · 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several publications have indicated potential benefit from collaboration with industry regarding wider use of anonymised routine NHS healthcare data. However, there is limited guidance regarding exactly how such collaborations between NHS hospitals and industry partners should best be carried out, and specific issues that need to be addressed at an individual project or collaboration level to achieve desired benefit. Specifically, routine health data are complex, not collected in a format optimised for secondary use, and often require interpretation based on clinical understanding of the medical conditions or patients. In order to address these issues, a formal partnership collaboration was established between an NHS organisation (Great Ormond Street Hospital for Children) and a pharmaceutical company (Roche Products Limited), to jointly understand the problems that require solving in order to maximise such use of NHS data to support improved patient outcomes and other patient/NHS benefit in a more sustainable way. We present the learnings from the first 2 years of the 5-year collaboration addressing aspects such as complexities of NHS Electronic Patient Record (EPR), data engineering and use of modern technology to optimise such data. Plus, the development of appropriate technology and data infrastructure within the NHS to support interoperability and prepare the NHS for wider application of artificial intelligence. We also highlight the staff skills and training needed to support such systems in the NHS, governance structures and processes needed to ensure appropriate use of tools and data and how best to co-design with patients, their families, and clinical teams. It is hoped that this review may provide useful information for both healthcare organisations and industry partners working towards the future of optimal use of data and technology for healthcare benefit.
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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.