2022
DOI: 10.3389/fdgth.2022.841853
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Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project

Abstract: IntroductionElectronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research.ObjectivesThis study aims to present the main elements of the MODELHealth project implementation and the evaluati… Show more

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Cited by 3 publications
(2 citation statements)
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“…This leads to potential challenges related to data completeness in the context of the use of CDM-converted health care data. Moreover, the relational anonymization of CDM-converted data by using the k-anonymity or l -diversity privacy models might build an interesting lever to allow patient privacy–preserved sharing of harmonized health care data as shown by Almeida et al [ 6 ] and in a recent study by Pitoglou et al [ 27 ]. Nonetheless, the anonymization of health care data can disproportionally affect the quality of resulting anonymous data sets due to information loss, and hence their suitability for medical research, as investigated by Langarizadeh et al [ 28 ] and Ferrão et al [ 29 ].…”
Section: Introductionmentioning
confidence: 99%
“…This leads to potential challenges related to data completeness in the context of the use of CDM-converted health care data. Moreover, the relational anonymization of CDM-converted data by using the k-anonymity or l -diversity privacy models might build an interesting lever to allow patient privacy–preserved sharing of harmonized health care data as shown by Almeida et al [ 6 ] and in a recent study by Pitoglou et al [ 27 ]. Nonetheless, the anonymization of health care data can disproportionally affect the quality of resulting anonymous data sets due to information loss, and hence their suitability for medical research, as investigated by Langarizadeh et al [ 28 ] and Ferrão et al [ 29 ].…”
Section: Introductionmentioning
confidence: 99%
“…HIE is a valuable tool for disease monitoring due to its extensive regional and demographic spread [ 7 , 11 ]. However, merging data from various sources into an HIE could result in possible quality problems, such as dropout, or aggravate the problems present in each EHR system, such as low data integrity [ 11 , 12 , 13 , 14 ].…”
Section: Introductionmentioning
confidence: 99%