Objective To acquire the flow law of outpatient and emergency visits in a large general hospital.
Methods By sampling monthly amount of outpatient and emergency from January 2005 to December 2013 of a large general hospital in Guangzhou, the trend of the time series was analyzed and calculated the seasonal index of the amount of hospital outpatient and emergency visits with the use of long-term trends method.
Result The flow law of patients in the hospital outpatient and emergency was significantly affected by seasonal factors, and different month had its own variation characters. The seasonal indexes were the highest in March, July, August, November and December (seasonal index >105%), while the lowest in January, February, October (seasonal index <95%).
Conclusion Based on analysis of the outpatient and emergency visits and causes with hospitals, decision makers and hospitals should make reasonable allocation of medical resources and provide evidence for the scientific decisions of hospital management. Thus, ensure the safety of patients.
Citation:
CHENGShu-yuan, HEZhi-min, ZENGXun, PENGDan-xin. Dynamic Analysis of Outpatient and Emergency Visits in a Large Tertiary Hospital in Guangzhou from 2005 to 2013. Chinese Journal of Evidence-Based Medicine, 2015, 15(4): 389-392. doi: 10.7507/1672-2531.20150066
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Copyright © the editorial department of Chinese Journal of Evidence-Based Medicine of West China Medical Publisher. All rights reserved
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- 3. 孙婕. 我院门诊量季节变动趋势分析. 中国卫生统计, 2009, 27(2):177-178.
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- 5. Huang F, Zhao A, Chen RJ. Ambient temperature and outpatient visits for acute exacerbation of chronic bronchitis in shanghai:a time series analysis. Biomed Environ Sci, 2015, 28(1):76-79.
- 6. 任月红, 赵晓娟, 张雅琼. 门诊诊疗人次季节变化动态分析. 中国病案, 2008, 8(1):28-29.
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- 9. Deloitte (2015) Healthcare and life sciences predictions 2020. Available at:http://www2.deloitte.com/cn/zh/pages/life-sciences-and-healthcare/articles/healthcare-and-life-sciences-predictions-2020.html.
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- 13. Cheng CH, Wang JW, Li CH, et al. Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix. Expert Systems with Applications, 2008, 34(4):2568-2575.