{"title":"代表需求工程中的人为障碍:电子健康记录的案例","authors":"M. Levy, Michal Pauzner, I. Hadar","doi":"10.1109/RE51729.2021.00041","DOIUrl":null,"url":null,"abstract":"Human barriers were found to impede the acceptance, adoption, or effective use of technology, manifested as under-usage of systems, non-compliance with regulations, short-term use of behavioral change systems, and more. These barriers can be overcome only by meeting appropriate requirements. However, such requirements are frequently overlooked. Different visual models are offered for supporting the derivation of requirements from, e.g., goals and emotions; however, we found no visualization techniques that support the elicitation and specification of requirements derived from known human barriers. The recruitment of Service Design visualization methods for visually expressing such barriers may help bridge this gap. This paper presents this vision and a demonstration for the case of Electronic Health Records, utilizing the Causal Loop Diagram to represent clinicians’ barriers when using a system. The visualization enabled by the diagram allows, for example, identification of use cases in which the system output may be overloaded with information (inducing cognitive overload) and still lacking relevant information (e.g., patient information beyond their clinical data), ultimately leading to suboptimal clinical decisions. This demonstration indicates the promise of this approach for eliciting frequently missed requirements rooted in users’ cognitive barriers.","PeriodicalId":440285,"journal":{"name":"2021 IEEE 29th International Requirements Engineering Conference (RE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Representing Human Barriers in Requirements Engineering: The Case of Electronic Health Records\",\"authors\":\"M. Levy, Michal Pauzner, I. Hadar\",\"doi\":\"10.1109/RE51729.2021.00041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Human barriers were found to impede the acceptance, adoption, or effective use of technology, manifested as under-usage of systems, non-compliance with regulations, short-term use of behavioral change systems, and more. These barriers can be overcome only by meeting appropriate requirements. However, such requirements are frequently overlooked. Different visual models are offered for supporting the derivation of requirements from, e.g., goals and emotions; however, we found no visualization techniques that support the elicitation and specification of requirements derived from known human barriers. The recruitment of Service Design visualization methods for visually expressing such barriers may help bridge this gap. This paper presents this vision and a demonstration for the case of Electronic Health Records, utilizing the Causal Loop Diagram to represent clinicians’ barriers when using a system. The visualization enabled by the diagram allows, for example, identification of use cases in which the system output may be overloaded with information (inducing cognitive overload) and still lacking relevant information (e.g., patient information beyond their clinical data), ultimately leading to suboptimal clinical decisions. This demonstration indicates the promise of this approach for eliciting frequently missed requirements rooted in users’ cognitive barriers.\",\"PeriodicalId\":440285,\"journal\":{\"name\":\"2021 IEEE 29th International Requirements Engineering Conference (RE)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 29th International Requirements Engineering Conference (RE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RE51729.2021.00041\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 29th International Requirements Engineering Conference (RE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RE51729.2021.00041","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Representing Human Barriers in Requirements Engineering: The Case of Electronic Health Records
Human barriers were found to impede the acceptance, adoption, or effective use of technology, manifested as under-usage of systems, non-compliance with regulations, short-term use of behavioral change systems, and more. These barriers can be overcome only by meeting appropriate requirements. However, such requirements are frequently overlooked. Different visual models are offered for supporting the derivation of requirements from, e.g., goals and emotions; however, we found no visualization techniques that support the elicitation and specification of requirements derived from known human barriers. The recruitment of Service Design visualization methods for visually expressing such barriers may help bridge this gap. This paper presents this vision and a demonstration for the case of Electronic Health Records, utilizing the Causal Loop Diagram to represent clinicians’ barriers when using a system. The visualization enabled by the diagram allows, for example, identification of use cases in which the system output may be overloaded with information (inducing cognitive overload) and still lacking relevant information (e.g., patient information beyond their clinical data), ultimately leading to suboptimal clinical decisions. This demonstration indicates the promise of this approach for eliciting frequently missed requirements rooted in users’ cognitive barriers.