A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment
In recent decades, information technology in healthcare, such as Electronic Medical Record (EMR) system, is potential to improve service quality and cost efficiency of the hospital. The continuous use of EMR systems has generated a great amount of data. However, hospitals tend to use these data to r...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2017/9185686 |
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| _version_ | 1832562346938073088 |
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| author | Hongyi Mao Yang Sun |
| author_facet | Hongyi Mao Yang Sun |
| author_sort | Hongyi Mao |
| collection | DOAJ |
| description | In recent decades, information technology in healthcare, such as Electronic Medical Record (EMR) system, is potential to improve service quality and cost efficiency of the hospital. The continuous use of EMR systems has generated a great amount of data. However, hospitals tend to use these data to report their operational efficiency rather than to understand their patients. Base on a dataset of inpatients’ medical records from a Chinese general public hospital, this study applies a configuration analysis from a managerial perspective and explains inpatients management in a different way. Four inpatient configurations (valued patients, managed patients, normal patients, and potential patients) are identified by the measure of the length of stay and the total hospital cost. The implications of the finding are discussed. |
| format | Article |
| id | doaj-art-171747515add42b782b990304b057099 |
| institution | Kabale University |
| issn | 1687-6415 1687-6423 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-171747515add42b782b990304b0570992025-02-03T01:22:50ZengWileyInternational Journal of Telemedicine and Applications1687-64151687-64232017-01-01201710.1155/2017/91856869185686A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data EnvironmentHongyi Mao0Yang Sun1Economics and Management School, Jiujiang University, Jiujiang 332005, ChinaUnion Hospital, Tongji Medical School, Huazhong University of Science and Technology, Wuhan 430022, ChinaIn recent decades, information technology in healthcare, such as Electronic Medical Record (EMR) system, is potential to improve service quality and cost efficiency of the hospital. The continuous use of EMR systems has generated a great amount of data. However, hospitals tend to use these data to report their operational efficiency rather than to understand their patients. Base on a dataset of inpatients’ medical records from a Chinese general public hospital, this study applies a configuration analysis from a managerial perspective and explains inpatients management in a different way. Four inpatient configurations (valued patients, managed patients, normal patients, and potential patients) are identified by the measure of the length of stay and the total hospital cost. The implications of the finding are discussed.http://dx.doi.org/10.1155/2017/9185686 |
| spellingShingle | Hongyi Mao Yang Sun A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment International Journal of Telemedicine and Applications |
| title | A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment |
| title_full | A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment |
| title_fullStr | A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment |
| title_full_unstemmed | A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment |
| title_short | A Way to Understand Inpatients Based on the Electronic Medical Records in the Big Data Environment |
| title_sort | way to understand inpatients based on the electronic medical records in the big data environment |
| url | http://dx.doi.org/10.1155/2017/9185686 |
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