Propuesta de metodología para el desarrollo de proyectos de analítica prescriptiva a partir de un Metaanálisis

Trabajo de investigaciónEste trabajo propone una metodología para el desarrollo de proyectos de Analítica Prescriptiva a partir de un Metaanálisis, en cual se reviso de manera sistemática el estado del arte, metodologías y usos en distintas áreas del conocimiento de dicha analítica, encontrando patrones en sus procesos que son comunes a metodologías orientadas a Data Mining como KDD, CRISP-DM y SEMMA.GLOSARIO RESUMEN INTRODUCCIÓN 1. PLANTEAMIENTO DEL PROBLEMA 2. JUSTIFICACIÓN 3. OBJETIVOS 4. ALCANCES Y LIMITACIONES 5. MARCO CONCEPTUAL 6. MARCO TEÓRICO 7. ESTADO DEL ARTE 8. METODOLOGÍA 9. DESARROLLO DEL PROYECTO 10. CONCLUSIONES REFERENCIAS ANEXOSPregradoIngeniero de Sistema

Three analytics-based essays examining the use and impact of Intelligent Voice Assistants (IVA) and Health Information Technologies (HIT) in service contexts

Recent advancements in information technology (IT) innovation, such as artificial intelligence (AI) and machine learning (ML), are changing the dynamics in the service sector by driving smart reinvention of service tasks and processes. Additionally, organisations are leveraging the capabilities of emerging information systems (IS) to make their services more efficient and customer centric. However, the decision to use recent advancements in IT can be challenging for organizations since the required initial investment for implementation is often high and the economic value and impact on service performance cannot be gauged with certainty (Kwon et al. 2015). This forces many organizations to prioritise which IT functionalities may best be suited for their needs. To support the decision making process of organizations, regarding the adoption and use of innovative IT, scholars in the information systems (IS) and related fields are called to improve knowledge and understanding about various IT components and functionalities as well as their corresponding impact on individual users and organizations. Scholars are also expected to provide the means by which businesses can meaningfully predict the potential impact and economic value of innovative IT (Ravichandran 2018). In this three essay dissertation, we investigate how the use of various components and functionalities of innovative information systems can individually (or together) impact the quality of service delivered to end consumers. The essays are broadly based on the intersection of artificial intelligence (AI), machine learning(ML) and services. In the first study, we found that during encounters between eService consumers and Intelligent Voice Assistants (IVAs), typically powered by artificial intelligence, machine learning and natural language processing, the following dimensions are important for the perceived quality of service: IVA interactivity, IVA personalization, IVA flexibility, IVA assurance and IVA reliability. Among the five dimensions of IVA encounter, we found that IVA interactivity, IVA personalization and IVA reliability had positive impacts on the effective use of IVAs. In study 2, we investigated performance of hospitals in the health service sector. We proposed a smart decision support system (DSS) for predicting the performance of hospitals based on the Health Information Technology (HIT) functionalities as applied and used in these hospitals for patient care and in improving hospital performance. We found that the predictive performance of our proposed smart DSS was most accurate when HIT functionalities were used in certain bundles than in isolation. In study 3, we investigated the effect of hospital heterogeneity on the accuracy of prediction of our proposed smart DSS as we recognize that not all hospitals have the same set of context, opportunity, location and constraints. We found that the following sources of variations in hospitals had significant moderator effects on the accurate prediction of our smart DSS: hospital size, ownership, region, location (urban/rural) and complexity of cases treated. In summary, this dissertation contributes to the IS literature by providing insight into the emergent use of artificial intelligence and machine learning technologies as part of IS/IT solutions in both consumer-oriented services and the healthcare sector

Skill-mix Innovation, Effectiveness and Implementation

Systemically analyses health workforce skill-mix innovations, implementation and outcomes by focusing on six core segments of health systems: health promotion and prevention, acute care, chronic care, long-term and palliative care, as well as access for vulnerable groups and people living in underserved areas

Social Public Health System and Sustainability

This edited volume contains 18 articles published in Sustainability from late 2018 to early 2021. During that time, the world faced the fatal and widespread health crisis, COVID-19, which had threatened the social and public health systems at every corner for quite some time.As the Guest-Editors and also a contributing authors, we are glad that the academic contents from the Special Issue will now be put together in this volume, making the authors' hard work and efforts accessible to the larger audience

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields