Adequate reutilization of routinely generated clinical data is a key component of what has been called a learning healthcare system, a system that is able to generate enough data that can be then analyzed to generate new insights into what works and what doesn't. However, the reutilization of electronic clinical data is not trivial since the quality of such data is usually low or unknown. Several tools have been developed to extract structured data from electronic health records (EHRs)--such as natural language processing--but, to this day, most researchers and quality experts rely on manual data extraction from EHRs. Here we assess the accuracy of ClincalTime, a temporal abstraction and query system designed easily identify patient cohorts based on patterns of clinical time intervals.

References

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Rojas ECapurro D(2019)Characterization of Drug Use Patterns Using Process Mining and Temporal Abstraction Digital PhenotypingBusiness Process Management Workshops10.1007/978-3-030-11641-5_15(187-198)Online publication date: 29-Jan-2019
https://doi.org/10.1007/978-3-030-11641-5_15
Güzel Kalayci EXiao GRyzhikov VKalayci TCalvanese DCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Ontop-temporalProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3269230(1927-1930)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3269230

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BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

October 2016

675 pages

ISBN:9781450342254

DOI:10.1145/2975167

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Rojas ECapurro D(2019)Characterization of Drug Use Patterns Using Process Mining and Temporal Abstraction Digital PhenotypingBusiness Process Management Workshops10.1007/978-3-030-11641-5_15(187-198)Online publication date: 29-Jan-2019
https://doi.org/10.1007/978-3-030-11641-5_15
Güzel Kalayci EXiao GRyzhikov VKalayci TCalvanese DCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Ontop-temporalProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3269230(1927-1930)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3269230

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Using electronic health record systems in diabetes care: emerging practices

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning

Standardizing Care for Huntington’s Disease: The Development of an openEHR Template for Motor Symptoms

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