Evidence map›Paper›PMID 35574663›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022

Creating a Real-World Linked Research Platform for Analyzing the Urgent and Emergency Care System.

Suzanne Mason, Tony Stone, Richard Jacques, Jennifer Lewis, Rebecca Simpson, Maxine Kuczawski, Matthew Franklin

Open access · hybridAbstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Suzanne MasonSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.ORCID 0000-0002-1701-0577
Tony StoneSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.ORCID 0000-0002-0167-3800
Richard JacquesSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.ORCID 0000-0001-6710-5403
Jennifer LewisSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.
Rebecca SimpsonSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.ORCID 0000-0003-1677-5938
Maxine KuczawskiSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.
Matthew FranklinSchool of Health Related Research, University of Sheffield, Sheffield, England, UK.ORCID 0000-0002-2774-9439
University of Sheffield · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis article describes the development of a system-based data platform for research developed to provide a detailed picture of the characteristics of the Urgent and Emergency Care system in 1 region of the United Kingdom. DATA SET DEVELOPMENT: CUREd is an integrated research data platform that describes the urgent and emergency care system in 1 region of the United Kingdom on almost 30 million patient contacts within the system. We describe regulatory approvals required, data acquisition, cleaning, and linkage. DATA SET ANALYSES: The data platform covers 2011 to 2017 for 14 acute National Health Service (NHS) Hospital Trusts, 1 ambulance service, the national telephone advice service (NHS 111), and 19 emergency departments. We describe 3 analyses undertaken: 1) Analyzing triage patterns from the NHS 111 telephone helpline using routine data linked to other urgent care services, we found that the current triage algorithms have high rates of misclassifying calls. 2) Applying an algorithm to consistently identify avoidable attendances for pediatric patients, we identified 21% of pediatric attendances to the emergency department as avoidable. 3) Using complex systems analysis to examine patterns of frequent attendance in urgent care, we found that frequent attendance is stable over time but varies by individual patient. This implies that frequent attendance is more likely to be a function of the system overall. DISCUSSION: We describe the processes necessary to produce research-ready data that link care across the components of the urgent and emergency care system. Making the use of routine data commonplace will require partnership between the collectors, owners, and guardians of the data and researchers and technical teams. HIGHLIGHTS: This article describes the development of a system-level data platform for research using routine patient-level data from the urgent and emergency care system in 1 region of the United Kingdom.The article describes how the data were acquired, cleaned, and linked and the challenges faced when undertaking analysis with the data.The data set has been used to understand patient use of the system, journeys once in the system, and outcomes following its use, for example, patterns of frequent use within urgent care and accuracy of referral decisions within the system.

Indexed as

Emergency Medical ServicesState MedicineAmbulancesChildEmergency Service, HospitalHumansTriagedata linkageemergency carehealth dataresearch-ready dataroutine dataroutine data analysisurgent care

Identifiers

PMID35574663
PMCPMC9583284
OpenAlexW4280499396

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.