Literature DB >> 35574663

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

Suzanne Mason1, Tony Stone1, Richard Jacques1, Jennifer Lewis1, Rebecca Simpson1, Maxine Kuczawski1, Matthew Franklin1.   

Abstract

BACKGROUND: This 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.

Entities:  

Keywords:  data linkage; emergency care; health data; research-ready data; routine data; routine data analysis; urgent care

Mesh:

Year:  2022        PMID: 35574663      PMCID: PMC9583284          DOI: 10.1177/0272989X221098699

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.749


  6 in total

1.  When to conduct probabilistic linkage vs. deterministic linkage? A simulation study.

Authors:  Ying Zhu; Yutaka Matsuyama; Yasuo Ohashi; Soko Setoguchi
Journal:  J Biomed Inform       Date:  2015-05-22       Impact factor: 6.317

2.  Frequent attendance at the emergency department shows typical features of complex systems: analysis of multicentre linked data.

Authors:  Christopher Burton; Tony Stone; Phillip Oliver; Jon M Dickson; Jen Lewis; Suzanne M Mason
Journal:  Emerg Med J       Date:  2021-05-26       Impact factor: 2.740

3.  Patient compliance with NHS 111 advice: Analysis of adult call and ED attendance data 2013-2017.

Authors:  Jen Lewis; Tony Stone; Rebecca Simpson; Richard Jacques; Colin O'Keeffe; Susan Croft; Suzanne Mason
Journal:  PLoS One       Date:  2021-05-10       Impact factor: 3.240

4.  Assessing data linkage quality in cohort studies.

Authors:  Katie Harron; James C Doidge; Harvey Goldstein
Journal:  Ann Hum Biol       Date:  2020-03       Impact factor: 1.533

5.  Non-urgent emergency department attendances in children: a retrospective observational analysis.

Authors:  Rebecca M Simpson; Colin O'Keeffe; Richard M Jacques; Tony Stone; Abu Hassan; Suzanne M Mason
Journal:  Emerg Med J       Date:  2021-10-28       Impact factor: 2.740

6.  Characterising non-urgent users of the emergency department (ED): A retrospective analysis of routine ED data.

Authors:  Colin O'Keeffe; Suzanne Mason; Richard Jacques; Jon Nicholl
Journal:  PLoS One       Date:  2018-02-23       Impact factor: 3.240

  6 in total

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