Dynamic Healthcare Connectivity and Collaboration with Multi-Agent Systems

With the growth of international healthcare operations, methods to improve connectivity are sought, along with a reduction in major barriers of electronic connectivity between global trading partners. To address these barriers, a conceptual agent-based framework following a proposed methodology for the analysis and design stages is developed to allow for improved ease of connectivity and interpretability between international trading partners. This framework is comprised of agents and is applied to connectivity between healthcare entities such as payers and providers. While many healthcare entities exchange information electronically, few do so without some form of manual intervention. Information systems may be engaged to further enhance the healthcare industry. Given the increases in costs and international presence, it is vital to make use of electronic systems that improve overall quality and cost of healthcare

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

The Occupation of Japan: An Analysis of Three Phases of Development

identifies and describes three phases involved in the reconstruction of Japan during the Allied Powers occupation after World War II: repatriation, economic and political reconstruction, and the development of treaties between Japan and the Allied Powers. Concludes that this period of Pacific reconstruction after World War II was one of the greatest achievements for the United States and the Allied Powers following the war

Systems Analysis and Design Application: Future Vehicle Prototyping

This nifty assignment research is presented in an undergraduate level course on Systems Analysis and Design to provide additional student engagement in the context of an applied team problem solving exercise to prototype a future vehicle. Systems Analysis and Design (SAND) is the process of identifying business opportunities or problems, analyzing the business opportunity, developing solutions to address the opportunity, and finally implementing the solution. This project provides the following contributions: 1.) An experiential learning activity provides students the opportunity to apply the learning classroom content to a real-world scenario, and 2.) The experiential learning method engages students to apply their current knowledge and reflect on the scenario to build upon their knowledge. The Learning Objectives include: 1.) Practice and perform prototyping, 2.) Apply the agile approach, and 3.) Utilize underlying analyst competencies

Advancing Customer Experience Theory: Five-Way Conversations in Two-Person Customer-Marketer Talk

This study advances customer experience theory (CET) by configuring research on talk, storytelling, customer-marketer interactions, and customer assessments of experiences in encounters with sales and hospitality/service representatives. Customers’ introspections and assessments of their meetings with marketers constitutes one genre of storytelling that include not only surface talk between two persons but surface and subsurface (nonconscious) talk between persons and within self. Practical implications include creative storytelling scripts for performing in sales and service training programs in firms and classroom contexts. Given the centrality of face-to-face meetings in many consumer shopping contexts (e.g., cars, houses, medical services; campus visits by high school seniors and parents, insurance selling, clothes shopping; tourism and hospitality), advancing CET and personal selling/buying effectiveness represent worthwhile pursuits. The study is one step forward in reducing the relative scarcity of extant research on customer-marketer talk. Empirically, the study includes customers’ thick descriptions of their self-marketer interactions via subjective personal introspections (SPI) and assessments of these exchanges. Interpersonal verbalization is only one of five levels of processing that take place when a researcher observes a decision-maker in a marketing organization interact with a decision-maker in a customer organization. At level 2, the speaker, listener, and observer are consciously editing thoughts as well as surfacing unconscious thoughts to combine and change conscious editing of what is said, heard, or observed. Level 3 is an automatic process in which unconscious thoughts are brought into working memory to mingle with conscious processing and to send some of the conscious processing into unconscious storage. Level 4 includes unconscious processing between or among individuals that do not become part of conscious processes or verbalization. Level 5 processing spreads activation within the person’s unconscious so that automatic thoughts and behaviors are set into motion without the individual being aware of the process. Customers’ introspections and assessments of their meetings with marketers constitutes one genre of storytelling that include not only surface talk between two persons but surface and subsurface (nonconscious) talk between persons and in within self. The study here includes customers’ thick descriptions of their self-marketer interactions via subjective personal introspections (SPI) and assessments of these exchanges. SPI uses the researcher as the subject of the study and allows for rich, thick, impressionistic narratives of the author’s own experiences in a particular context. Students in various marketing classes in five nations participated in a Trade Tales project. The Appendix provides a common set of instructions used by the Trade Tales Team members. All Trade Tales had a title page, abstract, story (with dialogue), five possible solutions with points awarded for choosing a particular solution and the rationale behind the choice, and surface (explicit) and deep (implicit/personal) assessments of the situation, story, and outcome assessments. Theory and practical implications: the participating student experiences “proper pleasure” in the re-telling of his or her story and also achieves better sense making and problem solving. The finalized versions of the Trade Tales can be used in other classes as case lets for studying customer-marketer interactions. The following case study illustrates one of the stories collected for the Trade Tales Team project. To achieve anonymity, the names of firms and persons are disguised. “AbsolutelyBest Ham to Pocatello, Idaho, USA: Arrival Delay in Customer\u27s Order” is the title of the case study. A customer goes on-line at firm’s (AbsolutelyBest) website and orders 9-lb ham to be delivered to daughter’s home in Pocatello, Idaho, on December 29th. Customer pays extra for two-day delivery service. Ham fails to arrive on December 29 due date. Customer asks for a credit on service not received. Bad weather hit most of the U.S. on December 28. What should the firm do? The full story appears in the paper with possible solutions for students to assess. Practical implications for Trade Tales include creative scripts for performing in sales and service training programs in firms and classroom contexts. Trade Tales are useful as case studies in classroom instruction. Given the centrality of face-to-face meetings in many consumer shopping contexts (e.g., cars, houses, medical services; campus visits by high school seniors, insurance selling; clothes shopping; tourism and hospitality), the relative scarcity of extant research on customer-marketer talk is surprising and represents a vacuum that researchers need to fill

ATOM: model-driven autoscaling for microservices

Microservices based architectures are increasinglywidespread in the cloud software industry. Still, there is ashortage of auto-scaling methods designed to leverage the uniquefeatures of these architectures, such as the ability to indepen-dently scale a subset of microservices, as well as the ease ofmonitoring their state and reciprocal calls.We propose to address this shortage with ATOM, a model-driven autoscaling controller for microservices. ATOM instanti-ates and solves at run-time a layered queueing network model ofthe application. Computational optimization is used to dynami-cally control the number of replicas for each microservice and itsassociated container CPU share, overall achieving a fine-grainedcontrol of the application capacity at run-time.Experimental results indicate that for heavy workloads ATOMoffers around 30%-37% higher throughput than baseline model-agnostic controllers based on simple static rules. We also find thatmodel-driven reasoning reduces the number of actions needed toscale the system as it reduces the number of bottleneck shiftsthat we observe with model-agnostic controllers