Abstract
Background: At present, passive alarm system from culture reports and announced from groups outbreak events make cases investigate delayed. Is there any predict factor can be used to suggest active high capture sensitivity surveillance and alarm outbreak early? Objectives: Is there any predict factor can be used to get active high capture sensitivity surveillance of hospital-acquired infections (HAI) in acute hospitals. Can it give alarm of outbreak early? Can it decrease the number for direct patient examination or chart review. Methods : We performed three months retrospective study to identify predictors(urine routine, device as catheter or cystoscope, antibiotics, culture, etc.) about major HAI (urinary tract infections), as defined by the Centers for Disease Control and Prevention (CDC) criteria in a medical center (733 beds). We compared patients list of predictor(s) positive collected from electronic medical record by medical information department members with confirmed nosocomial UTI cases list given from infection control department. Results: 5533 admission patients were screened. The overall prevalence of HAI was 2.5% (141/5533); 1.4% (77/5533) of patients was nosocomial UTI. At presence of urine routine examination and devices guarantees 100% capture sensitivity in detecting nosocomial UTI but requires an assessment of 2763 patients (49.9%) of the population. At presence of antibiotics and devices guarantees 98.7% capture sensitivity and requires an assessment of 1921 patients (34.7%) of the population, whereas presence of antibiotics and urine routine examination has 98.7% capture sensitivity but requires an assessment of 3019 patients (54.7%) of the population. Conclusion: A capture system based on daily list of newly order about antibiotics, devices, urine routine examination, urine culture, blood culture, infection control department of hospital can decide which high predict value criteria suggesting checklist from medical information department for infection control department member to perform active patient examination and decrease the number of direct patient examination and chart review, but still keep high capture sensitivity.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 |
| Pages | 443-448 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 - Taichung, Taiwan Duration: Jun 22 2009 → Jun 24 2009 |
Other
| Other | 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 |
|---|---|
| Country | Taiwan |
| City | Taichung |
| Period | 6/22/09 → 6/24/09 |
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Keywords
- HAI
- Hospital-acquired infection
- Nosocomial infection
- Predictor
- Surveillance
- UTI
ASJC Scopus subject areas
- Information Systems
- Biomedical Engineering
- Health Informatics
Cite this
Faster and active surveillance of hospital-acquired infections : A model for settings with high sensitivity predictors. / Chung, Yaowen; Lo, Yu Sheng; Lee, Wen Sen; Hsu, Min-Huei; Liu, Chien Tsai.
Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009. 2009. p. 443-448 5211224.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Faster and active surveillance of hospital-acquired infections
T2 - A model for settings with high sensitivity predictors
AU - Chung, Yaowen
AU - Lo, Yu Sheng
AU - Lee, Wen Sen
AU - Hsu, Min-Huei
AU - Liu, Chien Tsai
PY - 2009
Y1 - 2009
N2 - Background: At present, passive alarm system from culture reports and announced from groups outbreak events make cases investigate delayed. Is there any predict factor can be used to suggest active high capture sensitivity surveillance and alarm outbreak early? Objectives: Is there any predict factor can be used to get active high capture sensitivity surveillance of hospital-acquired infections (HAI) in acute hospitals. Can it give alarm of outbreak early? Can it decrease the number for direct patient examination or chart review. Methods : We performed three months retrospective study to identify predictors(urine routine, device as catheter or cystoscope, antibiotics, culture, etc.) about major HAI (urinary tract infections), as defined by the Centers for Disease Control and Prevention (CDC) criteria in a medical center (733 beds). We compared patients list of predictor(s) positive collected from electronic medical record by medical information department members with confirmed nosocomial UTI cases list given from infection control department. Results: 5533 admission patients were screened. The overall prevalence of HAI was 2.5% (141/5533); 1.4% (77/5533) of patients was nosocomial UTI. At presence of urine routine examination and devices guarantees 100% capture sensitivity in detecting nosocomial UTI but requires an assessment of 2763 patients (49.9%) of the population. At presence of antibiotics and devices guarantees 98.7% capture sensitivity and requires an assessment of 1921 patients (34.7%) of the population, whereas presence of antibiotics and urine routine examination has 98.7% capture sensitivity but requires an assessment of 3019 patients (54.7%) of the population. Conclusion: A capture system based on daily list of newly order about antibiotics, devices, urine routine examination, urine culture, blood culture, infection control department of hospital can decide which high predict value criteria suggesting checklist from medical information department for infection control department member to perform active patient examination and decrease the number of direct patient examination and chart review, but still keep high capture sensitivity.
AB - Background: At present, passive alarm system from culture reports and announced from groups outbreak events make cases investigate delayed. Is there any predict factor can be used to suggest active high capture sensitivity surveillance and alarm outbreak early? Objectives: Is there any predict factor can be used to get active high capture sensitivity surveillance of hospital-acquired infections (HAI) in acute hospitals. Can it give alarm of outbreak early? Can it decrease the number for direct patient examination or chart review. Methods : We performed three months retrospective study to identify predictors(urine routine, device as catheter or cystoscope, antibiotics, culture, etc.) about major HAI (urinary tract infections), as defined by the Centers for Disease Control and Prevention (CDC) criteria in a medical center (733 beds). We compared patients list of predictor(s) positive collected from electronic medical record by medical information department members with confirmed nosocomial UTI cases list given from infection control department. Results: 5533 admission patients were screened. The overall prevalence of HAI was 2.5% (141/5533); 1.4% (77/5533) of patients was nosocomial UTI. At presence of urine routine examination and devices guarantees 100% capture sensitivity in detecting nosocomial UTI but requires an assessment of 2763 patients (49.9%) of the population. At presence of antibiotics and devices guarantees 98.7% capture sensitivity and requires an assessment of 1921 patients (34.7%) of the population, whereas presence of antibiotics and urine routine examination has 98.7% capture sensitivity but requires an assessment of 3019 patients (54.7%) of the population. Conclusion: A capture system based on daily list of newly order about antibiotics, devices, urine routine examination, urine culture, blood culture, infection control department of hospital can decide which high predict value criteria suggesting checklist from medical information department for infection control department member to perform active patient examination and decrease the number of direct patient examination and chart review, but still keep high capture sensitivity.
KW - HAI
KW - Hospital-acquired infection
KW - Nosocomial infection
KW - Predictor
KW - Surveillance
KW - UTI
UR - http://www.scopus.com/inward/record.url?scp=70449440571&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449440571&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2009.39
DO - 10.1109/BIBE.2009.39
M3 - Conference contribution
SN - 9780769536569
SP - 443
EP - 448
BT - Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
ER -