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The construction of smart hospital is an important part of modern hospital management system,and it is also the key way to build the new system of high-quality hospital development.In terms of building smart hospitals,multi-campus hos-pitals face more difficulties and challenges than single campus hospitals,such as the lack of top-level design,the difficulty of in-tegrated management,the uneven development of hospitals and the widespread phenomenon of information islands.This study summarizes and analyzes the difficulties encountered in the construction and application of smart hospitals in multi-hospital areas.Guided by problems,it puts forward countermeasures and suggestions for the construction of refined and high-quality smart hospi-tals in multi-campus hospitals,including strengthening overall and forward-looking awareness,integrating management according to hospital conditions,characteristic development under demand guidance,establishing a data integration center for smart hospi-tals,scientific planning of talent reserve and discipline layout,etc.
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Objective To systematically review the worldwide simulation model studies on the natural history of breast cancer and to summarize related parameters.Methods A structured literature search was conducted in PubMed and the Cochrane Library to identify articles during 1980-2015.Articles were screened independently by two researchers.Health states in the natural history and relevant parameters were extracted.Results A total of 36 studies were included for analysis,within the earliest one was published in 1990.Most studies were from Europe and America countries,and 2 studies from China.Markov model was mostly applied to evaluating breast cancer screening programs (n=32).Reported health status included “healthy” (n=36),ductal carcinoma in situ (DCIS,n=17),invasive breast cancer (IBC,n=36),and death (n=27).There were two definite classifications for IBC,tumor size (n=9) and TNM staging (n=9,3 studies reported transition rates).The median (range) of annual transition rates from DCIS to stage-I IBC,I to Ⅱ,Ⅱ to Ⅲ,Ⅲ to Ⅳ were 0.279 (0.259-0.299),0.150 (0.069-0.430),0.100 (0.060-0.128) and 0.210 (0.010-0.625),respectively.A total of 15 studies reported the mean duration from predinical to clinical stage for IBC was 1.95-4.70 years,which gradually increased with age,and 7 studies reported that for DCIS.Conclusions Despite closer attention was paid to breast cancer natural history models,in recent years atypical hyperplasia has been neglected.Data on the mean duration of DCIS requires reasonable conversion.Various classifications for IBC exist whereas transition rates are limited.Current findings would be valuable references but challenging for the Chinese-population specific natural history model,development.
ABSTRACT
Objective To systematically review the worldwide simulation model studies on the natural history of breast cancer and to summarize related parameters.Methods A structured literature search was conducted in PubMed and the Cochrane Library to identify articles during 1980-2015.Articles were screened independently by two researchers.Health states in the natural history and relevant parameters were extracted.Results A total of 36 studies were included for analysis,within the earliest one was published in 1990.Most studies were from Europe and America countries,and 2 studies from China.Markov model was mostly applied to evaluating breast cancer screening programs (n=32).Reported health status included “healthy” (n=36),ductal carcinoma in situ (DCIS,n=17),invasive breast cancer (IBC,n=36),and death (n=27).There were two definite classifications for IBC,tumor size (n=9) and TNM staging (n=9,3 studies reported transition rates).The median (range) of annual transition rates from DCIS to stage-I IBC,I to Ⅱ,Ⅱ to Ⅲ,Ⅲ to Ⅳ were 0.279 (0.259-0.299),0.150 (0.069-0.430),0.100 (0.060-0.128) and 0.210 (0.010-0.625),respectively.A total of 15 studies reported the mean duration from predinical to clinical stage for IBC was 1.95-4.70 years,which gradually increased with age,and 7 studies reported that for DCIS.Conclusions Despite closer attention was paid to breast cancer natural history models,in recent years atypical hyperplasia has been neglected.Data on the mean duration of DCIS requires reasonable conversion.Various classifications for IBC exist whereas transition rates are limited.Current findings would be valuable references but challenging for the Chinese-population specific natural history model,development.
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Objective@#To parameterize the 1-year transition probabilities between different health status of the natural history of breast cancer based on the data of randomized controlled trial of X-ray mammography screening worldwide.@*Methods@#Based on the breast cancer screening randomized controlled trials defined by a mammography screening review from the Cochrane 2013 and the International Agency for Research on Cancer, a systematic review was initiated in PubMed by searching names of the key investigators of the trials, combined with the diseases, screening intervention and outcome indicators. If applicable, all the original cumulative incidence rates were converted into one-year transition rate, using the life-table approach considering time length of follow-up.@*Results@#A total of 23 reports from 9 RCTs were included. The data on transition rate between the healthy status to precancerous lesions was absent. The 1-year transition rate from health to carcinoma in situ was 17.78 to 50.21 per 100 000 persons in the intervention group and 9.16 to 26.84 per 100 000 persons in the control group. Correspondingly, the 1-year transition rate from health to breast cancer (including carcinoma in situ and invasive cancer) were estimated as 143.75 to 316.97 per 100 000 persons in the intervention group, and 141.45 to 288.84 per 100 000 persons in the control group. Furthermore, the transition rate from the healthy status to invasive breast cancer was 159.79 to 264.60 per 100 000 persons in intervention group and 170.12 to 255.33 per 100 000 persons in control group. The transition rate from carcinoma in situ to invasive breast cancer varied among different pathological types.@*Conclusions@#The most common natural history states of reported by the included trials involved the full healthy status, carcinoma in situ and invasive breast cancer. The findings of transition rates between different health statuses will be informative for future model development of natural history studies of breast cancer. Information in relation to breast precancerous lesions still limited and needs to be further addressed.
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Objective To analyze the optimal scales of secondary public hospitals so as to optimize the expansion of public hospitals.Methods Forty-six secondary public general hospitals in Beijing were selected as the sample,with input and output indicators pinpointed,for analysis of the status of economic return to scale of such hospitals from 1996 to 2012,and identification of inflexion points of the returns to scale.These efforts will help find an optimal scale of such hospitals.Resalts The period from 1996 to 2012 found the general effectiveness of such hospitals in a decline.In 2012,only 4 of the 46 hospitals were in DEA effectiveness status,and the other 42 hospitals were not; Forty-three inflexion points were identified.This study found that the strict control standards for secondary public general hospitals in Beijing were 298 beds and 585 staffs; the flexible control standards were 421 beds and 807 staffs.Conclclsion The optimal scales for secondary public hospitals were drown from the analysis,for references of other regions in China.The hospitals should prioritize resources efficiency instead of scale expansion.