{"title":"一个Excel预测模型,以帮助决策,影响医院资源/床位的利用-医院接收急诊室病人的能力","authors":"W. Stout, B. Tawney","doi":"10.1109/SIEDS.2005.193261","DOIUrl":null,"url":null,"abstract":"In complex systems it is difficult to discern the effects of interactions among component parts. Analysts can use Microsoft Excel for a preliminary assessment of internal systems dynamics. Often, in the hospital environment early data identification surfaces as rate estimates - admits per hour versus discharges per hour. For example, management may wish to assess capability to accommodate patients discharged from the emergency department for subsequent admission to the department of medicine (DOM) as well as demands on DOM bed capacity from other sources. This paper explores conceptual development and practical application of the spreadsheet model. Particular features include: constructing lookup tables by hour of day containing estimates of minimum and maximum rates, using the randbetween function to randomly select model inputs from a uniform distribution, developing frequency distributions to assist in output interpretation, illustrating conditional formatting, output graphing, etc. One can observe multiple samples of hourly patient fluctuations based on unit open beds and midnight census. Number of patients waiting can be shown at varying levels of system utilization. As utilization approaches approximately eighty percent, patient waiting time increases disproportionately. The spreadsheet model is a dynamic, visual illustration of how variation in individual process times can affect total process capability. Its use is primarily intended as a teaching tool for those new to simulation modeling.","PeriodicalId":317634,"journal":{"name":"2005 IEEE Design Symposium, Systems and Information Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"An Excel forecasting model to aid in decision making that affects hospital resource/bed utilization - hospital capability to admit emergency room patients\",\"authors\":\"W. Stout, B. Tawney\",\"doi\":\"10.1109/SIEDS.2005.193261\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In complex systems it is difficult to discern the effects of interactions among component parts. Analysts can use Microsoft Excel for a preliminary assessment of internal systems dynamics. Often, in the hospital environment early data identification surfaces as rate estimates - admits per hour versus discharges per hour. For example, management may wish to assess capability to accommodate patients discharged from the emergency department for subsequent admission to the department of medicine (DOM) as well as demands on DOM bed capacity from other sources. This paper explores conceptual development and practical application of the spreadsheet model. Particular features include: constructing lookup tables by hour of day containing estimates of minimum and maximum rates, using the randbetween function to randomly select model inputs from a uniform distribution, developing frequency distributions to assist in output interpretation, illustrating conditional formatting, output graphing, etc. One can observe multiple samples of hourly patient fluctuations based on unit open beds and midnight census. Number of patients waiting can be shown at varying levels of system utilization. As utilization approaches approximately eighty percent, patient waiting time increases disproportionately. The spreadsheet model is a dynamic, visual illustration of how variation in individual process times can affect total process capability. Its use is primarily intended as a teaching tool for those new to simulation modeling.\",\"PeriodicalId\":317634,\"journal\":{\"name\":\"2005 IEEE Design Symposium, Systems and Information Engineering\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE Design Symposium, Systems and Information Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIEDS.2005.193261\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE Design Symposium, Systems and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS.2005.193261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Excel forecasting model to aid in decision making that affects hospital resource/bed utilization - hospital capability to admit emergency room patients
In complex systems it is difficult to discern the effects of interactions among component parts. Analysts can use Microsoft Excel for a preliminary assessment of internal systems dynamics. Often, in the hospital environment early data identification surfaces as rate estimates - admits per hour versus discharges per hour. For example, management may wish to assess capability to accommodate patients discharged from the emergency department for subsequent admission to the department of medicine (DOM) as well as demands on DOM bed capacity from other sources. This paper explores conceptual development and practical application of the spreadsheet model. Particular features include: constructing lookup tables by hour of day containing estimates of minimum and maximum rates, using the randbetween function to randomly select model inputs from a uniform distribution, developing frequency distributions to assist in output interpretation, illustrating conditional formatting, output graphing, etc. One can observe multiple samples of hourly patient fluctuations based on unit open beds and midnight census. Number of patients waiting can be shown at varying levels of system utilization. As utilization approaches approximately eighty percent, patient waiting time increases disproportionately. The spreadsheet model is a dynamic, visual illustration of how variation in individual process times can affect total process capability. Its use is primarily intended as a teaching tool for those new to simulation modeling.