Using deterministic record linkage to link ambulance and emergency department data: is it possible without patient identifiers?

Clark, Sophie; Halter, Mary; Porter, Alison; Smith, Hazel; Brand, Marek; Fothergill, Rachael; Lindridge, Jaqualine; McTigue, Martin; Snooks, Helen

doi:10.23889/ijpds.v4i1.1104

Cited by 5 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The match rate in our study is comparable to other similar projects conducted in Australia and the United Kingdom which achieved linkage rates of between 83% and 94%. 2,3,[5][6][7]11 Nevertheless, in order to further increase the linkage rate, the algorithm for future linkages of this nature in Victoria could be optimised. For instance, a similar linkage conducted in New South Wales, Australia used two linkage passes to achieve a match rate of 92.4% between ambulance and administrative datasets.…”

Section: Discussionmentioning

confidence: 99%

“…However, routine linkage of this data with emergency department (ED) presentation and hospital admission datasets is rare and has historically been performed on a project-by-project basis. [2][3][4][5][6][7] Consequently, the influence of prehospital triage and care on patient outcomes is not systematically evaluated by ambulance services internationally.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linking Ambulance Records with Hospital and Death Index Data to Evaluate Patient Outcomes

Andrew

Cox

2022

IJGM

View full text Add to dashboard Cite

Linkage of electronic administrative datasets is becoming increasingly common, offering a powerful resource for research and analysis. However, routine linkage of prehospital data with emergency department (ED) presentation and hospital admission datasets is rare. We describe a methodology used to link ambulance data with hospital ED presentations, admissions, and death records, and examine potential biases between matched and unmatched patients. Methods: Iterative deterministic linkage methodologies were employed to link clinical, operational, and secondary triage ambulance data to ED presentations, hospital admissions, and death records in Victoria, Australia. Descriptive analyses and standardised differences were used to examine potential biases between matched and unmatched patients. Results: A total of 2,813,913 ambulance records were available for linkage. Of the patients that were transported to a public ED (n=1,753,268), 83.3% matched with an ED record. Only small differences were observed between matched and unmatched patients for sex, year, time of day and attending crew type. The data elements with the largest standardised differences were patient age (0.25) and paramedic diagnosis (0.25). Matched patients were older (mean ± standard deviation: 55.6±25.7 vs 49.0±26.0 years) and more likely to have a paramedicsuspected cardiac, respiratory, neurological, or gastrointestinal/genitourinary condition, suspected infection/sepsis, or pain. Conclusion:This linked dataset will facilitate a large body of research into prehospital care and patient outcomes. Although future analysis of matched patients should acknowledge the linkage error rate, our findings suggest that results are likely to be generalisable to the broader ambulance population.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linking Ambulance Records with Hospital and Death Index Data to Evaluate Patient Outcomes

Andrew

Cox

2022

IJGM

View full text Add to dashboard Cite

show abstract

“…To study the effect of the removal of these records (n = 463,541), we have generated Figure S5, Supplemental Digital Content, http://links.lww.com/MD/I861 and Figure S6, Supplemental Digital Content, http://links.lww.com/MD/I862 , that can be compared with Figure S3, Supplemental Digital Content, http://links.lww.com/MD/I859 and Figure S4, Supplemental Digital Content, http://links.lww.com/MD/I860 where such exclusion has not occurred. The binding method (deterministic binding [ 15 ] uses matching standards of the following variables: patient name, sex, date of birth, wife/husband name, father and mother names, legal identification number, address). Only records with a perfect match were selected.…”

Section: Methodsmentioning

confidence: 99%

“…com/MD/I859 and Figure S4, Supplemental Digital Content, http://links.lww.com/MD/I860 where such exclusion has not occurred. The binding method (deterministic binding [15] uses matching standards of the following variables: patient name, sex, date of birth, wife/husband name, father and mother names, legal identification number, address). Only records with a perfect match were selected.…”

Section: Population Recordsmentioning

confidence: 99%

See 1 more Smart Citation

Mortality after discharge from a public tertiary cardiovascular referral hospital

et al. 2023

View full text Add to dashboard Cite

It is critically important for stakeholders with distinct foci of attention on healthcare to understand patient evolution in the presence of an established diagnosis or with a suspected diagnosis of various diseases, specially considering death as an outcome. To study the long-term mortality of patients at a cardiovascular referral hospital. Deterministic binding (selection of pairs of registers from the hospital electronic health records and the mortality records of São Paulo state) from 2002 to 2017 was performed. Studied variables were: age, sex, hospital treatment unit where the first visit occurred (Emergency Unit, Outpatient Unit, Hospital Admissions, Diagnostics Services), treatment type, elapsed time between the first visit and death, diagnosis at first and last visits and variables related to death. Statistical Methods: descriptive, survival (with Kaplan–Meier method), correspondence and competitive risks analyses; in case of nonoccurrence of death until the end of 2017, the patients were considered alive. Statistical significance was set at values of P < .05. Median age at the first visit to the Hospital was 51.9 years. Birth locations included 4496 cities, 17.33% in São Paulo, 0.41% in Rio de Janeiro, 0.40% in Osasco, 24.04% in other cities. Sex included females (46.7%), males (44.2%), not defined (6.3%), and other (2.8%). We observed an association between diseases in ICD-10 Chapter 16 (certain conditions originating in the perinatal period) and Chapter 17 (congenital malformations, deformations, and chromosomal abnormalities), both as diagnoses and underlying causes of death, as well as between neoplasms as diagnoses and as the underlying cause of death. In this sample, there was an association between admission diagnoses and underlying causes of death, such as neoplasms, cardiovascular diseases, and congenital heart malformations. Additionally, patients who underwent a cardiac intervention had a smaller less mortality rate than those who were not operated on. There were also differences in cardiovascular mortality between distinct treatment units of the hospital ((Emergency Unit, Outpatient Unit, Hospital Admissions, Diagnostic Services).

show abstract

Improving Probabilistic Record Linkage Using Statistical Prediction Models

Moretti

Shlomo

2022

Int Statistical Rev

View full text Add to dashboard Cite

SummaryRecord linkage brings together information from records in two or more data sources that are believed to belong to the same statistical unit based on a common set of matching variables. Matching variables, however, can appear with errors and variations and the challenge is to link statistical units that are subject to error. We provide an overview of record linkage techniques and specifically investigate the classic Fellegi and Sunter probabilistic record linkage framework to assess whether the decision rule for classifying pairs into sets of matches and non‐matches can be improved by incorporating a statistical prediction model. We also study whether the enhanced linkage rule can provide better results in terms of preserving associations between variables in the linked data file that are not used in the matching procedure. A simulation study and an application based on real data are used to evaluate the methods.

show abstract

Using deterministic record linkage to link ambulance and emergency department data: is it possible without patient identifiers?

Cited by 5 publications

References 26 publications

Linking Ambulance Records with Hospital and Death Index Data to Evaluate Patient Outcomes

Linking Ambulance Records with Hospital and Death Index Data to Evaluate Patient Outcomes

Mortality after discharge from a public tertiary cardiovascular referral hospital

Improving Probabilistic Record Linkage Using Statistical Prediction Models

Contact Info

Product

Resources

About