Evidence map›Paper›PMID 34095533›Full record

ArticleInternational journal of population data science2019

Using deterministic record linkage to link ambulance and emergency department data: is it possible without patient identifiers? A case study from the UK.

S J Clark, M Halter, A Porter, H C Smith, M Brand, R Fothergill, S J Lindridge, M McTigue, H Snooks

Open access · goldAbstract read
In one paragraph

Article in International journal of population data science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 8 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

9 authors at 4 institutions in 1 country.

S J ClarkMedical Directorate, London Ambulance Service, London. SE1 8SD.
M HalterKingston University and St George's, University of London, Centre for Health and Social Care Research, London SW17 0RE.
A PorterSwansea University Medical School, Singleton Park, Swansea SA2 8PP.
H C SmithDepartment of Primary Care and Population Health, University College London, London, UK; Formally Nuffield Trust, 59 New Cavendish Street, London, UK.
M BrandStrategy Directorate, London Ambulance Service, London. SE1 8SD.
R FothergillClinical Audit and Research Unit, London Ambulance Service, London. SE1 0BW.
S J Lindridge27 Devonshire Way, Croydon, CR0 8BU. Emergency Care Intensive Support Team, NHS Improvement, London, SE1 8UG; Formerly Medical Directorate, London Ambulance Service NHS Trust, London, SE1 8SD.
M McTigueOperations West, London Ambulance Service, London. SE1 8SD.
H SnooksSwansea University Medical School, Singleton Park, Swansea SA2 8PP.
London Ambulance Service NHS Trust · GBSwansea University · GBNuffield Trust · GBSt George's, University of London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRoutine linkage of emergency ambulance records with those from the emergency department is uncommon in the UK. Our study, known as the Pre-Hospital Emergency Department Data Linking Project (PHED Data), aimed to link records of all patients conveyed by a single emergency ambulance service to thirteen emergency departments in the UK from 2012-2016.

objectivesWe aimed to examine the feasibility and resource requirements of collecting de-identified emergency department patient record data and, using a deterministic matching algorithm, linking it to ambulance service data.

methodsWe used a learning log to record contacts and activities undertaken by the research team to achieve data linkage. We also conducted semi-structured interviews with information management/governance staff involved in the process.

resultsWe found that five steps were required for successful data linkage for each hospital trust. The total time taken to achieve linkage was a mean of 65 weeks. A total of 958,057 emergency department records were obtained and, of these, 81% were linked to a corresponding ambulance record. The match rate varied between hospital trusts (50%-94%). Staff expressed strong enthusiasm for data linkage. Barriers to successful linkage were mainly due to inconsistencies between and within acute trusts in the recording of two ambulance event identifiers (CAD and call sign). Further data cleaning was required on emergency department fields before full analysis could be conducted. Ensuring the data was not re-identifiable limited validation of the matching method.

conclusionWe conclude that deterministic record linkage based on the combination of two event identifiers (CAD and call sign) is possible. There is an appetite for data linkage in healthcare organisations but it is a slow process. Developments in standardising the recording of emergency department data are likely to improve the quality of the resultant linked dataset. This would further increase its value for providing evidence to support improvements in health care delivery. HIGHLIGHTS: Ambulance records are rarely linked to other datasets; this study looks at the feasibility and resource requirement to use deterministic matching to link ambulance and emergency department data for patients conveyed by ambulance to the emergency department.It is possible to link these data, with an average match rate of 81% across 13 emergency departments and one large ambulance trust.All trusts approached provided match-able data and there was an appetite for data linkage; however, it was a long process taking an average of 65 weeks.We conclude that deterministic matching using no patient identifiers can be used in this setting.

Identifiers

PMID34095533
PMCPMC8142959
OpenAlexW2966089379

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Registered trials

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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.