ArticleNPJ digital medicine2018
Guiding interoperable electronic health records through patient-sharing networks.
Article in NPJ digital medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Impact of Electronic Health Record Interoperability on Safety and Quality of Care in High-Income Countries: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Extent of Digital Health Fragmentation and Potential Implications for Antimicrobial Prescribing: Rapid Evidence Review.Journal of medical Internet research · 2026Review
- Reconstructing healthcare networks from patient transfer data: A systematic review.PLOS digital health · 2026Article
- AI in Psychiatry for Improving Continuity of Patient Care: Protocol for a Mixed Methods Systematic Review.JMIR research protocols · 2026Article
- When clinicians group together: a scoping review of clustering in patient-sharing networks.BMC health services research · 2026Article
- Exploring the Intensity and Continuity of Hospital Care for Patients With Long Covid: Evidence From an English Urban Healthcare System.Health expectations : an international journal of public participation in health care and health policy · 2026Observational
- Article
- Patient and caregiver perceptions of electronic health records interoperability in the NHS and its impact on care quality: a focus group study.BMC medical informatics and decision making · 2024Article
- A picture of health: determining the core population served by an urban NHS hospital trust and understanding the key health needs.BMC public health · 2022Article
- Article
- Identifying naturally occurring communities of primary care providers in the English National Health Service in London.BMJ open · 2020Observational
- Observational
- The impact of data breach on IT investment at neighboring hospitals: Evidence from California Hospitals.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective sharing of clinical information between care providers is a critical component of a safe, efficient health system. National data-sharing systems may be costly, politically contentious and do not reflect local patterns of care delivery. This study examines hospital attendances in England from 2013 to 2015 to identify instances of patient sharing between hospitals. Of 19.6 million patients receiving care from 155 hospital care providers, 130 million presentations were identified. On 14.7 million occasions (12%), patients attended a different hospital to the one they attended on their previous interaction. A network of hospitals was constructed based on the frequency of patient sharing between hospitals which was partitioned using the Louvain algorithm into ten distinct data-sharing communities, improving the continuity of data sharing in such instances from 0 to 65-95%. Locally implemented data-sharing communities of hospitals may achieve effective accessibility of clinical information without a large-scale national interoperable information system.
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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.