Development of a knowledge-based healthcare-associated infections surveillance system in China
Abstract Background In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02297-y |
| _version_ | 1797559238309969920 |
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| author | Yu Cao Yaojun Niu Xuetao Tian DeZhong Peng Li Lu Haojun Zhang |
| author_facet | Yu Cao Yaojun Niu Xuetao Tian DeZhong Peng Li Lu Haojun Zhang |
| author_sort | Yu Cao |
| collection | DOAJ |
| description | Abstract Background In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations. Methods We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts. Results We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability. Conclusions Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery. |
| first_indexed | 2024-03-10T17:42:35Z |
| format | Article |
| id | doaj.art-3301928f4686452e9602785db9530865 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-10T17:42:35Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-3301928f4686452e9602785db95308652023-11-20T09:38:30ZengBMCBMC Medical Informatics and Decision Making1472-69472023-10-0123111410.1186/s12911-023-02297-yDevelopment of a knowledge-based healthcare-associated infections surveillance system in ChinaYu Cao0Yaojun Niu1Xuetao Tian2DeZhong Peng3Li Lu4Haojun Zhang5 College of Computer Science, Sichuan UniversityLiLian Information Technology CompanyLiLian Information Technology Company College of Computer Science, Sichuan University College of Computer Science, Sichuan UniversityThe dean’s office, Second Provincial People’s Hospital of GansuAbstract Background In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations. Methods We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts. Results We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability. Conclusions Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.https://doi.org/10.1186/s12911-023-02297-yHealthcare-associated infectionsSurveillance systemKnowledges rulesInfection clinical guidelines |
| spellingShingle | Yu Cao Yaojun Niu Xuetao Tian DeZhong Peng Li Lu Haojun Zhang Development of a knowledge-based healthcare-associated infections surveillance system in China BMC Medical Informatics and Decision Making Healthcare-associated infections Surveillance system Knowledges rules Infection clinical guidelines |
| title | Development of a knowledge-based healthcare-associated infections surveillance system in China |
| title_full | Development of a knowledge-based healthcare-associated infections surveillance system in China |
| title_fullStr | Development of a knowledge-based healthcare-associated infections surveillance system in China |
| title_full_unstemmed | Development of a knowledge-based healthcare-associated infections surveillance system in China |
| title_short | Development of a knowledge-based healthcare-associated infections surveillance system in China |
| title_sort | development of a knowledge based healthcare associated infections surveillance system in china |
| topic | Healthcare-associated infections Surveillance system Knowledges rules Infection clinical guidelines |
| url | https://doi.org/10.1186/s12911-023-02297-y |
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