Objective To discuss issues related to telemedicine in the context of the“Internet plus”and the prevention of novel coronavirus in early 2020,so as to provide some reference for the rapid development of Internet plu...Objective To discuss issues related to telemedicine in the context of the“Internet plus”and the prevention of novel coronavirus in early 2020,so as to provide some reference for the rapid development of Internet plus telemedicine.Methods Literature analysis method was used to summarize the current status of telemedicine at home and abroad.Descriptive statistical analysis and comparative analysis were also conducted to analyze the data of population and health in the“China Health Statistical Yearbook”and“China Statistical Yearbook”from 2009 to 2018.Results and Conclusion The distribution of medical demand and medical resources is uneven in 31 provinces,municipalities and autonomous regions,such problems are more serious between urban and rural areas in different regions.The population’s demand for medical care and the allocation of medical resources have the characteristics of positive correlation,large urban-rural differences and regional imbalance.Confronted with the situation that the uneven distribution of medical resources provides potential development opportunities for telemedicine and the difficulties in the further development of telemedicine,the government should formulate policies to improve the publicity of telemedicine,setting up a full coverage of telemedicine service system.Besides,hospitals should ensure the information security monitoring.展开更多
Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation...Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.展开更多
基金Source of the project:the Social Science Planning Fund of Liaoning Province(L19BGL034).
文摘Objective To discuss issues related to telemedicine in the context of the“Internet plus”and the prevention of novel coronavirus in early 2020,so as to provide some reference for the rapid development of Internet plus telemedicine.Methods Literature analysis method was used to summarize the current status of telemedicine at home and abroad.Descriptive statistical analysis and comparative analysis were also conducted to analyze the data of population and health in the“China Health Statistical Yearbook”and“China Statistical Yearbook”from 2009 to 2018.Results and Conclusion The distribution of medical demand and medical resources is uneven in 31 provinces,municipalities and autonomous regions,such problems are more serious between urban and rural areas in different regions.The population’s demand for medical care and the allocation of medical resources have the characteristics of positive correlation,large urban-rural differences and regional imbalance.Confronted with the situation that the uneven distribution of medical resources provides potential development opportunities for telemedicine and the difficulties in the further development of telemedicine,the government should formulate policies to improve the publicity of telemedicine,setting up a full coverage of telemedicine service system.Besides,hospitals should ensure the information security monitoring.
文摘Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.
