Evidence map›Paper›PMID 37940189›Full record

ArticleBMJ health & care informatics2023

Proof-of-concept solution to create an interoperable timeline of healthcare data.

Sapna Trivedi, Stephen Hall, Fiona Inglis, Afzal Chaudhry

Open access · goldAbstract read
In one paragraph

Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 74% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Sapna TrivediClinical Informatics (eHospital), Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK sapna.trivedi@nhs.net.ORCID http://orcid.org/0000-0003-4184-0164
Stephen HallClinical Informatics (eHospital), Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Fiona InglisClinical Informatics (eHospital), Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Afzal ChaudhryEpic Systems Corporation, (previously eHospital), Epic Systems, The Core, St Thomas St, Bristol, UK.
Cambridge University Hospitals NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo overcome the barriers of interoperability by sharing simulated patient data from different electronic health records systems and presenting them in an intuitive timeline of events.

methodsThe 'Patient Story' software comprising database and blockchain, PS Timeline Windows interface, PS Timeline Web interface and network relays on Azure cloud was customised for Epic and Lorenzo electonic patient record (EPR) systems used at different hospitals, using site-specific adapters.

resultsEach site could view their own clinical documents and view each other's site specific, fully coded test sets of (Care Connect) medications, conditions and allergies, in an aggregated single view. DISCUSSION: This work has shown that clinical data from different EPR systems can be successfully integrated and visualised on a single timeline, accessible by clinicians and patients.

conclusionThe Patient Story system combined the timeline visualisation with successful interoperability across healthcare settings, as well giving patients the ability to directly interact with their timeline.

Indexed as

Electronic Health RecordsSoftwareDelivery of Health CareHealth FacilitiesHospitalsHumansData VisualizationElectronic Health RecordsHealth Information InteroperabilityPatient Care

Identifiers

PMID37940189
PMCPMC10693683
OpenAlexW4388539887

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

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.