Automated linkage of patient records from disparate sources

Li, Xiaochun; Xu, Huiping; Shen, Changyu; Grannis, Shaun J.

doi:10.1177/0962280215626180

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Prior research has demonstrated its impaired performance when conditional dependence exists, as well as the potential gain in matching accuracy when conditional dependence latent class models are used (Xu et al, 2019). However, the success of the conditional dependence models is heavily dependent on the use of correct conditional dependence structure (Li et al, 2018). Existing approaches for the identification of the conditional dependence structure, including the correlation residual plot, the log‐odds ratio check, and the bivariate residual approach, have been shown to have poor performance (Oberski et al, 2013; Subtil et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Conditional dependence among matching fields may exist in one or both latent classes. When it does, the FS model provides an inadequate fit to the data, yields biassed parameter estimates, and produces impaired matching accuracy (Li et al, 2018; Xu et al, 2019).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, this is true only with the correct conditional dependence structure. When the conditional dependence structure is misspecified, the model could yield a worse performance than the FS model (Li et al, 2018). These findings are consistent to those demonstrated by Albert and Dodd (2004) regarding the use of latent class models for the evaluation of sensitivity and specificity of diagnostic tests in the absence of a gold standard, where it was noted that the conditional dependence latent class models lack robustness with regard to the conditional dependence structure and misspecified structures can seriously bias the diagnostic accuracy estimation.…”

Section: Introductionmentioning

confidence: 99%

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Score Test for Assessing the Conditional Dependence in Latent Class Models and its Application to Record Linkage

Zhang

et al. 2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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The Fellegi-Sunter model has been widely used in probabilistic record linkage despite its often invalid conditional independence assumption. Prior research has demonstrated that conditional dependence latent class models yield improved match performance when using the correct conditional dependence structure. With a misspecified conditional dependence structure, these models can yield worse performance. It is, therefore, critically important to correctly identify the conditional dependence structure. Existing methods for identifying the conditional dependence structure include the correlation residual plot, the log-odds ratio check, and the bivariate residual, all of which have been shown to perform inadequately. Bootstrap bivariate residual approach and score test have also been proposed and found to have better performance, with the score test having greater power and lower computational burden.In this paper, we extend the score-test-based approach to account for different conditional dependence structures. Through a simulation study, we develop practical recommendations on the utilisation of the score test and assess the match performance with conditional dependence identified by the proposed method. Performance of the proposed method is further evaluated using a real-world record linkage example. Findings show that

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Score Test for Assessing the Conditional Dependence in Latent Class Models and its Application to Record Linkage

Zhang

et al. 2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…The INPC is routinely used for health services and public health research [ 16 ], including prior studies on STIs [ 17 – 20 ]. Probabilistic matching techniques, described previously [ 21 , 22 ], were used to match the mother’s social security number, last name, first name, date of birth, and gender from the birth certificate to her medical records.…”

Section: Methodsmentioning

confidence: 99%

Syphilis testing adherence among women with livebirth deliveries: Indianapolis 2014-2016

Ojo

Arno

Tao

et al. 2021

BMC Pregnancy Childbirth

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Background The number of congenital syphilis (CS) cases in the United States are increasing. Effective prevention of CS requires routine serologic testing and treatment of infected pregnant women. The Centers for Disease Control and Prevention (CDC) recommends testing all pregnant women at their first prenatal visit and subsequent testing at 28 weeks gestation and delivery for women at increased risk. Methods We conducted a cross-sectional cohort study of syphilis testing among pregnant women with a livebirth delivery from January 2014 to December 2016 in Marion County, Indiana. We extracted and linked maternal and infant data from the vital records in a local health department to electronic health records available in a regional health information exchange. We examined syphilis testing rates and factors associated with non-testing among women with livebirth delivery. We further examined these rates and factors among women who reside in syphilis prevalent areas. Results Among 21260 pregnancies that resulted in livebirths, syphilis testing in any trimester, including delivery, increased from 71.7% in 2014 to 86.6% in 2016. The number of maternal syphilis tests administered only at delivery decreased from 16.6% in 2014 to 4.04% in 2016. Among women living in areas with high syphilis rates, syphilis screening rates increased from 79.6% in 2014 to 94.2% in 2016. Conclusion Improvement in prenatal syphilis screening is apparent and encouraging, yet roughly 1-in-10 women do not receive syphilis screening during pregnancy. Adherence to recommendations set out by CDC improved over time. Given increasing congenital syphilis cases, the need for timely diagnoses and prevention of transmission from mother to fetus remains a priority for public health.

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“…Synthetic data is widely used in testing, validating, and evaluating different data linkage methods, frameworks, and algorithms either as the primary dataset or comparison dataset [30, [54][55][56][57]. A research project compared the performance of different algorithms in terms of linkage accuracy and speed using nine million synthetic records [58].…”

Section: Plos Digital Healthmentioning

confidence: 99%

Synthetic data in health care: A narrative review

2023

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Data are central to research, public health, and in developing health information technology (IT) systems. Nevertheless, access to most data in health care is tightly controlled, which may limit innovation, development, and efficient implementation of new research, products, services, or systems. Using synthetic data is one of the many innovative ways that can allow organizations to share datasets with broader users. However, only a limited set of literature is available that explores its potentials and applications in health care. In this review paper, we examined existing literature to bridge the gap and highlight the utility of synthetic data in health care. We searched PubMed, Scopus, and Google Scholar to identify peer-reviewed articles, conference papers, reports, and thesis/dissertations articles related to the generation and use of synthetic datasets in health care. The review identified seven use cases of synthetic data in health care: a) simulation and prediction research, b) hypothesis, methods, and algorithm testing, c) epidemiology/public health research, d) health IT development, e) education and training, f) public release of datasets, and g) linking data. The review also identified readily and publicly accessible health care datasets, databases, and sandboxes containing synthetic data with varying degrees of utility for research, education, and software development. The review provided evidence that synthetic data are helpful in different aspects of health care and research. While the original real data remains the preferred choice, synthetic data hold possibilities in bridging data access gaps in research and evidence-based policymaking.

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Automated linkage of patient records from disparate sources

Cited by 5 publications

References 23 publications

Score Test for Assessing the Conditional Dependence in Latent Class Models and its Application to Record Linkage

Score Test for Assessing the Conditional Dependence in Latent Class Models and its Application to Record Linkage

Syphilis testing adherence among women with livebirth deliveries: Indianapolis 2014-2016

Synthetic data in health care: A narrative review

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