Pub Date : 2022-04-01DOI: 10.1109/syscon53536.2022.9773819
Georgia A Mandell, Matthew B Keating, Inas S Khayal
Healthcare delivery systems are widely accepted as socio-technical systems. Unlike other socio-technical systems, healthcare systems leave very little decision-making to technical automation and control. Instead, the healthcare delivery system relies on human healthcare resources for decision-making. Human decision-making is imperative to the clinical delivery of care to patients and to the operational processes that support care delivery, quality improvement, and other organizational management activities. For these clinical and operational activities, human resources make healthcare decisions using healthcare data typically housed in electronic medical records (EMRs). Unfortunately, EMR systems were first designed with the functional capability to store data, and, second to a lesser degree, to retrieve data. The literature recognizes the need to improve the retrieval of information from EMR systems. More specifically, there remains the need to directly view a patient's holistic health and healthcare trajectory. At this time, decision-makers are left to mentally build this holistic picture in their mind by sequentially clicking through many sections of the EMR. Therefore, in this paper, we develop a visualization tool to organize and present an individual's health and healthcare trajectory by describing a patient record holistically from a system architecture perspective. This approach is based on a previously developed system model for healthcare delivery and individual health outcomes.
{"title":"Development of a Visualization Tool for Healthcare Decision-Making using Electronic Medical Records: A Systems Approach to Viewing a Patient Record.","authors":"Georgia A Mandell, Matthew B Keating, Inas S Khayal","doi":"10.1109/syscon53536.2022.9773819","DOIUrl":"https://doi.org/10.1109/syscon53536.2022.9773819","url":null,"abstract":"<p><p>Healthcare delivery systems are widely accepted as socio-technical systems. Unlike other socio-technical systems, healthcare systems leave very little decision-making to technical automation and control. Instead, the healthcare delivery system relies on human healthcare resources for decision-making. Human decision-making is imperative to the clinical delivery of care to patients and to the operational processes that support care delivery, quality improvement, and other organizational management activities. For these clinical and operational activities, human resources make healthcare decisions using healthcare data typically housed in electronic medical records (EMRs). Unfortunately, EMR systems were first designed with the functional capability to store data, and, second to a lesser degree, to retrieve data. The literature recognizes the need to improve the retrieval of information from EMR systems. More specifically, there remains the need to directly view a patient's holistic health and healthcare trajectory. At this time, decision-makers are left to mentally build this holistic picture in their mind by sequentially clicking through many sections of the EMR. Therefore, in this paper, we develop a visualization tool to organize and present an individual's health and healthcare trajectory by describing a patient record holistically from a system architecture perspective. This approach is based on a previously developed system model for healthcare delivery and individual health outcomes.</p>","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483409/pdf/nihms-1928430.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1109/syscon53536.2022.9773793
Inas S Khayal
While the structure of healthcare systems evolved out of the need to address acute conditions, the function of healthcare systems evolved to primarily address chronic conditions. The healthcare delivery system organically developed to respond to "one-off" acute illness or injury. Subsequently, healthcare delivery systems grew into legacy systems that evolved into complex systems over time. Healthcare delivery for acute conditions tends to utilize a specific part or form of the healthcare delivery system. In contrast, healthcare delivery for chronic conditions forces patients to seek care over time between different places or healthcare entities. Because of the self-contained structural organization of these healthcare delivery systems, they were not designed to provide coordinated, integrated, and longitudinal care over time and place. Consequently, today's complex legacy healthcare delivery system requires significant improvement in the quality of care delivered to patients, especially those with chronic conditions. As a complex and legacy system, the most appropriate approach to improve the quality of delivered care is through a re-design quality improvement process, rather than a new system design process. In this paper, we describe the conceptual framework for quality improvement (QI) and the current micro and macro level approaches to quality improvement. We applied the current quality improvement approaches to the QI conceptual framework. We identified the limitations in current quality improvement processes in complex healthcare systems at the macro-level, pointing to the need for macro-systems approaches to healthcare quality improvement.
{"title":"Healthcare Quality Improvement: The Need for a Macro-Systems Approach.","authors":"Inas S Khayal","doi":"10.1109/syscon53536.2022.9773793","DOIUrl":"https://doi.org/10.1109/syscon53536.2022.9773793","url":null,"abstract":"<p><p>While the structure of healthcare systems evolved out of the need to address acute conditions, the function of healthcare systems evolved to primarily address chronic conditions. The healthcare delivery system organically developed to respond to \"one-off\" acute illness or injury. Subsequently, healthcare delivery systems grew into legacy systems that evolved into complex systems over time. Healthcare delivery for acute conditions tends to utilize a specific part or form of the healthcare delivery system. In contrast, healthcare delivery for chronic conditions forces patients to seek care over time between different places or healthcare entities. Because of the self-contained structural organization of these healthcare delivery systems, they were not designed to provide coordinated, integrated, and longitudinal care over time and place. Consequently, today's complex legacy healthcare delivery system requires significant improvement in the quality of care delivered to patients, especially those with chronic conditions. As a complex and legacy system, the most appropriate approach to improve the quality of delivered care is through a re-design quality improvement process, rather than a new system design process. In this paper, we describe the conceptual framework for quality improvement (QI) and the current micro and macro level approaches to quality improvement. We applied the current quality improvement approaches to the QI conceptual framework. We identified the limitations in current quality improvement processes in complex healthcare systems at the macro-level, pointing to the need for macro-systems approaches to healthcare quality improvement.</p>","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483430/pdf/nihms-1928428.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1109/SysCon53536.2022.9773861
Ulf Könemann, Daria Wilke, H. Anacker, R. Dumitrescu
{"title":"Identification of stakeholder-specific Systems Engineering competencies for industry","authors":"Ulf Könemann, Daria Wilke, H. Anacker, R. Dumitrescu","doi":"10.1109/SysCon53536.2022.9773861","DOIUrl":"https://doi.org/10.1109/SysCon53536.2022.9773861","url":null,"abstract":"","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73482806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-13DOI: 10.1109/SYSCON.2015.7116746
L. R. Rodrigues, I. Medeiros, C. Kern
Since maintenance planning directly affect the availability and the lifecycle cost of components and systems, it has become a topic of great interest among researchers and industry practitioners in recent years. Preventive maintenance techniques can be adopted in order to determine a convenient maintenance schedule, reducing the number of unexpected failure events. The implementation of a preventive maintenance approach may also provide other benefits such as increase in equipment availability, reduction in maintenance costs and increase in equipment lifetime. In this scenario, the application of PHM (Prognostics and Health Monitoring) techniques can be thought as a powerful tool to support the implementation of a CBM (Condition Based Maintenance) approach. The problem of CBM optimization can be formulated as finding the optimum maintenance schedule for a set of components so that the average maintenance cost per unit of time is minimized. In this paper, a maintenance cost optimization method for multiple components is presented. The proposed method uses information on the health condition of each component and takes into account the economic benefits of repairing multiple components at the same time instead of scheduling maintenance interventions for different components in different time instants, based on individual optimization recommendations. A numerical example is presented to illustrate the application of the proposed method.
{"title":"Maintenance cost optimization for multiple components using a condition based method","authors":"L. R. Rodrigues, I. Medeiros, C. Kern","doi":"10.1109/SYSCON.2015.7116746","DOIUrl":"https://doi.org/10.1109/SYSCON.2015.7116746","url":null,"abstract":"Since maintenance planning directly affect the availability and the lifecycle cost of components and systems, it has become a topic of great interest among researchers and industry practitioners in recent years. Preventive maintenance techniques can be adopted in order to determine a convenient maintenance schedule, reducing the number of unexpected failure events. The implementation of a preventive maintenance approach may also provide other benefits such as increase in equipment availability, reduction in maintenance costs and increase in equipment lifetime. In this scenario, the application of PHM (Prognostics and Health Monitoring) techniques can be thought as a powerful tool to support the implementation of a CBM (Condition Based Maintenance) approach. The problem of CBM optimization can be formulated as finding the optimum maintenance schedule for a set of components so that the average maintenance cost per unit of time is minimized. In this paper, a maintenance cost optimization method for multiple components is presented. The proposed method uses information on the health condition of each component and takes into account the economic benefits of repairing multiple components at the same time instead of scheduling maintenance interventions for different components in different time instants, based on individual optimization recommendations. A numerical example is presented to illustrate the application of the proposed method.","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76334733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-04-13DOI: 10.1109/SYSCON.2015.7116749
I. Medeiros, L. R. Rodrigues, C. Kern, Rafael D. C. Santos, E. H. Shiguemori
PHM (Prognostics and Health Monitoring) can be defined as the capability of assessing the health condition, forecasting impending failures and the expected RUL (Remaining Useful Life) of a component based on a set of measurements collected from systems. Additionally, an important concept that could stem from PHM is IVHM (Integrate Vehicle Health Management); that is the unified capability of integrating PHM within a framework of available resources and operational demand. Therefore, this work aims to integrate task assignment and maintenance recommendation, both based on PHM information, for UAVs (Unmanned Aerial Vehicle) Swarm. Task assignment is the problem of assigning a vehicle to a task. This paper uses a PHM-based task assignment solution; this solution takes into account mission time, task priority and vehicle's health condition. Maintenance recommendation is the operation of defining which component should receive maintenance action, using an algorithm that takes into account PHM information, system architecture and safety margins. Both task assignment and maintenance recomendations take advantage of a combination of PHM information and system architecture to compute the UAV's health condition, referred as S-RUL (System Level Remaining Useful Life). The S-RUL provides information related to the time when the whole system will stop working. In the case study, a simplified pitch control system is used to illustrate the application of the proposed method to UAVs Swarm.
{"title":"Integrated task assignment and maintenance recommendation based on system architecture and PHM information for UAVs","authors":"I. Medeiros, L. R. Rodrigues, C. Kern, Rafael D. C. Santos, E. H. Shiguemori","doi":"10.1109/SYSCON.2015.7116749","DOIUrl":"https://doi.org/10.1109/SYSCON.2015.7116749","url":null,"abstract":"PHM (Prognostics and Health Monitoring) can be defined as the capability of assessing the health condition, forecasting impending failures and the expected RUL (Remaining Useful Life) of a component based on a set of measurements collected from systems. Additionally, an important concept that could stem from PHM is IVHM (Integrate Vehicle Health Management); that is the unified capability of integrating PHM within a framework of available resources and operational demand. Therefore, this work aims to integrate task assignment and maintenance recommendation, both based on PHM information, for UAVs (Unmanned Aerial Vehicle) Swarm. Task assignment is the problem of assigning a vehicle to a task. This paper uses a PHM-based task assignment solution; this solution takes into account mission time, task priority and vehicle's health condition. Maintenance recommendation is the operation of defining which component should receive maintenance action, using an algorithm that takes into account PHM information, system architecture and safety margins. Both task assignment and maintenance recomendations take advantage of a combination of PHM information and system architecture to compute the UAV's health condition, referred as S-RUL (System Level Remaining Useful Life). The S-RUL provides information related to the time when the whole system will stop working. In the case study, a simplified pitch control system is used to illustrate the application of the proposed method to UAVs Swarm.","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77223550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-22DOI: 10.1109/SysCon.2014.6819269
Devarapalli Hari Prasad, Munnamgi Hanumad Vasanth
Workplace teams in software service organizations need to be adaptive to address the needs of complex business systems. Typically teams are formed top-down and go through stages - storm, form and norm before they perform. As knowledge societies move from ‘command and control’ mode to that of ‘connect and communicate’, building effective teams is a formidable challenge that needs great focus and attention. Several scholars in the fields of psychology and organizational behavior have acknowledged the role of Shared Mental Models in team learning and effectiveness. We used Shared Mental Model construct to study the relevance and contribution of bottom-up self-organization to agile software development teams. We found that self-organization develops task and team related shared mental models. Task related shared mental model fostered characteristics that are crucial to agile software development teams and team related shared mental models facilitated members to carry out roles needed for the functioning of agile software development teams.
{"title":"Value of bottom-up team formation for complex adaptive business systems","authors":"Devarapalli Hari Prasad, Munnamgi Hanumad Vasanth","doi":"10.1109/SysCon.2014.6819269","DOIUrl":"https://doi.org/10.1109/SysCon.2014.6819269","url":null,"abstract":"Workplace teams in software service organizations need to be adaptive to address the needs of complex business systems. Typically teams are formed top-down and go through stages - storm, form and norm before they perform. As knowledge societies move from ‘command and control’ mode to that of ‘connect and communicate’, building effective teams is a formidable challenge that needs great focus and attention. Several scholars in the fields of psychology and organizational behavior have acknowledged the role of Shared Mental Models in team learning and effectiveness. We used Shared Mental Model construct to study the relevance and contribution of bottom-up self-organization to agile software development teams. We found that self-organization develops task and team related shared mental models. Task related shared mental model fostered characteristics that are crucial to agile software development teams and team related shared mental models facilitated members to carry out roles needed for the functioning of agile software development teams.","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74762738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-22DOI: 10.1109/SysCon.2014.6819287
J. B. Machado
A new technique for systems identification using ladder-structured generalized orthonormal basis function model is presented. In this approach the model poles and the number of functions are optimized using a genetic algorithm. A fitness function based on the Akaike information criterion considering model accuracy and model parsimony provides optimal number of functions and poles of the system model. Simulated and a real examples illustrate the performance of the proposed technique.
{"title":"GA optimization of ladder-structured GOBF models","authors":"J. B. Machado","doi":"10.1109/SysCon.2014.6819287","DOIUrl":"https://doi.org/10.1109/SysCon.2014.6819287","url":null,"abstract":"A new technique for systems identification using ladder-structured generalized orthonormal basis function model is presented. In this approach the model poles and the number of functions are optimized using a genetic algorithm. A fitness function based on the Akaike information criterion considering model accuracy and model parsimony provides optimal number of functions and poles of the system model. Simulated and a real examples illustrate the performance of the proposed technique.","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76396986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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