Citywide quality of health information system through text mining of electronic health records
Abstract A system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-07-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41109-021-00395-2 |
| _version_ | 1819154850545401856 |
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| author | Anastasia A. Funkner Michil P. Egorov Sergey A. Fokin Gennady M. Orlov Sergey V. Kovalchuk |
| author_facet | Anastasia A. Funkner Michil P. Egorov Sergey A. Fokin Gennady M. Orlov Sergey V. Kovalchuk |
| author_sort | Anastasia A. Funkner |
| collection | DOAJ |
| description | Abstract A system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, and individual behavior of physicians. The unstructured (textual) data of EHR is rarely used to assess the citywide quality of healthcare. Within the study, we analyze EHR data, particularly textual unstructured data, as a reflection of the complex multi-agent system of healthcare in the city of Saint Petersburg, Russia. Through analyzing the data collected by the Medical Information and Analytical Center, a method was proposed and evaluated for identifying a common structure, understanding the diversity, and assessing information quality in EHR data through the application of natural language processing techniques. |
| first_indexed | 2024-12-22T15:27:38Z |
| format | Article |
| id | doaj.art-4320243ed1bc4a7085eea45cac394f8c |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-12-22T15:27:38Z |
| publishDate | 2021-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-4320243ed1bc4a7085eea45cac394f8c2022-12-21T18:21:27ZengSpringerOpenApplied Network Science2364-82282021-07-016112110.1007/s41109-021-00395-2Citywide quality of health information system through text mining of electronic health recordsAnastasia A. Funkner0Michil P. Egorov1Sergey A. Fokin2Gennady M. Orlov3Sergey V. Kovalchuk4ITMO UniversityITMO UniversityMedical Information and Analytical CenterITMO UniversityITMO UniversityAbstract A system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, and individual behavior of physicians. The unstructured (textual) data of EHR is rarely used to assess the citywide quality of healthcare. Within the study, we analyze EHR data, particularly textual unstructured data, as a reflection of the complex multi-agent system of healthcare in the city of Saint Petersburg, Russia. Through analyzing the data collected by the Medical Information and Analytical Center, a method was proposed and evaluated for identifying a common structure, understanding the diversity, and assessing information quality in EHR data through the application of natural language processing techniques.https://doi.org/10.1007/s41109-021-00395-2Health information systemElectronic health recordUnstructured dataNatural language processingData completenessMachine learning |
| spellingShingle | Anastasia A. Funkner Michil P. Egorov Sergey A. Fokin Gennady M. Orlov Sergey V. Kovalchuk Citywide quality of health information system through text mining of electronic health records Applied Network Science Health information system Electronic health record Unstructured data Natural language processing Data completeness Machine learning |
| title | Citywide quality of health information system through text mining of electronic health records |
| title_full | Citywide quality of health information system through text mining of electronic health records |
| title_fullStr | Citywide quality of health information system through text mining of electronic health records |
| title_full_unstemmed | Citywide quality of health information system through text mining of electronic health records |
| title_short | Citywide quality of health information system through text mining of electronic health records |
| title_sort | citywide quality of health information system through text mining of electronic health records |
| topic | Health information system Electronic health record Unstructured data Natural language processing Data completeness Machine learning |
| url | https://doi.org/10.1007/s41109-021-00395-2 |
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