Intuitive Decision Research Articles

Passionate projects: practitioner reflections on emotion management

PurposeThe fundamental challenge for project management is dealing with people and their feelings. While there has been sporadic attention to the importance of emotions in project work, project management practices tend to neglect the role of emotions and emotional reflexivity. The authors use a symbolic interaction framework to present an in-depth exploration of emotions and emotional reflexivity in projects.Design/methodology/approachEmpirical data was gathered in 19 semi-structured interviews with diverse project managers to assess their experience of emotion (15 male, 4 female, early 20s to late 50s, 3–38 years of expertise). Transcribed interviews were thematically analysed using a sociology of emotions informed, grounded theory, interactional framework.FindingsThe data revealed that emotional states are framed by factors specific to project management, including organisational change, project constraints and dealing with stakeholders. Explicitly managing emotions improved team engagement and project performance by acting as a catalyst for engaging in reflective practice and intuitive decision making.Practical implicationsGiven the widely held misconceptions of emotion as maladaptive, project management education must focus on empathy in communication and leadership if practitioners are to master valuable soft skills. Techniques for emotional reflection and learning feeling lessons must be incorporated into practice.Originality/valueThe authors contribute to the emerging understanding that emotions matter in project management. The authors demonstrate the centrality of emotions in projects and the substantial impact they have on the wellbeing of practitioners and staff. Emotional reflexivity in practice, which is widely acknowledged yet tends to be ignored, is an essential part of the project manager's toolkit.

THE EFFECT OF ENHANCED EXTENSIVE READING TO THE INCIDENTAL ENGLISH PREPOSITION ACQUISITION

The English prepositions are still problematic for EFL learners since the existing knowledge of their first language hinders them from understanding the use of English prepositions. This study investigated the effect of enhanced extensive reading on their incidental English preposition acquisition. A total of 16 Indonesian University learners of English as a foreign language participated in this study. The treatment lasted for four weeks, initiated with the pretest. Two weeks after the treatment, the posttest was administered, followed by distributing questionnaires to find out the factors influencing them in doing the posttest. The results showed a significant improvement in the students' incidental acquisition of the targeted English prepositions after they received repeated exposure to the English prepositions through enhanced extensive reading. The factors influencing the improvement of their incidental acquisition involve their existing knowledge of the English prepositions and their built intuition in decision making. It is hoped that teachers and educational institutions are encouraged to add extensive reading to their curriculum.

Pengambilan Keputusan di Perguruan Tinggi Pesantren di Universitas Ibrahimy Sukorejo Situbondo dan Universitas Nurul Jadid Paiton Probolinggo

The purpose of the study was to describe the decision-making process, participation in decision-making, and decision-making methods at Ibrahimy Sukorejo University Situbondo and Nurul Jadid University Paiton Probolinggo. The research methodology is a qualitative study, the type is descriptive with a multi-site design at the Sukorejo Islamic boarding school in Situbondo and the Nurul Jadid Islamic boarding school in Paiton Probolinggo. Data collection technique; Observations, interviews, and documentary studies. The data were organized, interpreted, tested for validity by triangulation procedures, then analyzed using the interactive model of Milles and Huberman. The findings of this study are: (1) the decision-making process is carried out by identifying the problem, developing it by conducting internal and external analysis, solving it with a new strategy, implementing and evaluating the results of the decision; (2) forms of participation are ideas, thoughts, suggestions, criticisms, and energy in carrying out organizational tasks according to the structure. And the factors that influence participation are the factors of intention, sincerity, dedication, age, knowledge, and a sense of responsibility in carrying out organizational tasks to achieve goals; (3) the decision-making methods are autocratic decisions, consultative decisions, joint decisions, and religious intuitive decisions.

Intelligent Tools to Monitor, Control and Predict Wastewater Reclamation and Reuse.

Contemporary wastewater reclamation units entail several diverse treatment and extraction processes, with a multitude of monitored quality characteristics, controlled by a variety of key operational parameters directly affecting the efficiency of treatment. The conventional optimization of this highly complex system is time- and energy- consuming, frequently relying on intuitive decision making by operators, and does not predict or forecast efficiency changes and system maintenance. In this paper, we introduce intelligent solutions to enhance the operational control of the unit with minimal human intervention and to develop an AI-powered DSS that is installed atop the sensors of a water treatment module. The DSS uses an expert model, both to assess the quality of water and to offer suggestions based on current values and future trends. More specifically, the quality of the produced water was successfully visualized, assessed and rated, based on a set of input operational variables (pH, TOC for this case), while future values of monitored sensors were forecasted. Additionally, monitoring services of the DSS were able to identify unexpected events and to generate alerts in the case of observed violation of operational limits, as well as to implement changes (automatic responses) to operational parameters so as to reestablish normal operating conditions and to avoid such events in the future. Up to now, the DSS suggestion and forecasting services have proven to be adequately accurate. Though data are still being collected from early adopters, the solution is expected to provide a complete water treatment solution that can be adopted by a vast range of parties.

Qualitative evaluation of pharmaceuticals and metabolites in hospital effluent: Influence of sample preparation technique and outranking by environmental risk using the ELECTRE method

The presence of pharmaceuticals and metabolites in effluents has become a serious environmental problem, so it is essential to be able to monitor these microcontaminants using qualitative approaches, as well as to assess the potential environmental risks that such compounds may present. Therefore, in this study, suspect screening analysis was performed of 2030 pharmaceuticals and metabolites in hospital effluent samples, applying different sample preparation techniques. Additionally, a pioneering association of (Q)SAR assessment of identified contaminants with the ELECTRE multi-criteria decision analysis technique made it possible to prioritize analytes according to their environmental risk, in order to enable their inclusion in environmental monitoring programs. The results showed that the most advantageous alternative sample preparation technique consisted of cleanup (100 mg of silica/alumina sorbent) + dispersive liquid-liquid microextraction (7.5 of aqueous matrix, 325 μL of chloroform (extracting solvent), and 500 μL of acetonitrile as dispersing solvent). This procedure resulted in the identification of 70 pharmaceuticals and metabolites in the composite sample tested. The suspect screening analysis found a total of 105 microcontaminants, 28 of them being “confirmed compounds” and 77 being “suspect compounds”. Of the compounds identified, 87% were pharmaceuticals and 13% were metabolites. The compounds identified were subsequently evaluated using different open access software packages, considering eight endpoints: mobility, persistence, estrogen receptor binding, wastewater treatment plant total removal, biodegradability, PBT (persistent, bioaccumulation and toxic), mutagenicity, and carcinogenicity. The (Q)SAR prediction results were used as input data for the ELECTRE outranking method. Categorization of the identified compounds by ELECTRE resulted in the kernel (priority compounds) and a further 19 groups. ELECTRE sensitivity evaluation indicated that for all the cases, the kernel and the following two groups coincided. The categorization provided by the ELECTRE method constitutes a highly intuitive decision and choice tool, which can assist in the selection of compounds if subsequent quantitative analysis is to be carried out.

A complex network-based response method for changes in customer requirements for design processes of complex mechanical products

The soaring demand, inevitable changes, and substantial change costs associated with complex mechanical products (CMPs) have accelerated the need to reasonably and accurately respond to changes in customer requirements during the product design process. However, current related studies cannot provide a simple and intuitive decision reference for decision-makers (DMs) to respond to these changes. In this work, a complex network theory-based methodology is proposed. First, a complex network model of CMPs is constructed; this model is processed unidirectionally through analysis of the constraint relation and affiliation among parts. Second, all nodes in this network are divided into levels, and all feasible change propagation paths are selected by breadth-first search. Furthermore, to quantify the change losses of paths, a novel “change workload” is proposed, which is a comprehensive indicator, and a distinct decision reference. The “change workload” is composed of “network change rate,” “change magnification node rate,” and “change magnification rate,” whose weights are evaluated by The Entropy Method and Technique for Order Preference by Similarity to an Ideal Solution. Due to the independence of the “change workload” from expert experience, the proposed methodology is reasonable and capable of outputting a list of affected parts and a preferred ordering of propagation paths, which could provide clearer and more direct guidance for DMs. This presented method is fully proven through a real-world case study of a wind turbine.

Internet of things based smart city design using fog computing and fuzzy logic

• “Internet of Things based Smart City Design using Fog Computing and Fuzzy Logic”. • An edge computing paradigm using human interactions for their decision-making process. • Employs probabilistic history-based parameters that keep track of node interactions. • Context-aware smart paradigm considering predictability ratios. • A nature-inspired algorithm used to make the routing considerations. • A fuzzy rule-based system using power-buffer capacity-based index to make classes. The prevalence of IoT based technology in contemporary society allows for the planning of smart city infrastructure with individually owned devices as participating nodes in a social smart network. Edge computing application into such an IoT network facilitates the distributed computing framework by bringing the computational application closer to the end-users in form of Fog sub-networks, comprising the local participating end-user IoT nodes as service requesters and specialized servers catering to the requests, modelled for the infrastructure category they are delivering (Smart healthcare, ITS, Smart architecture, etc.). Opportunistic-based last-mile connectivity between the Fog servers and participating nodes aides the dynamicity of the topology for efficient information. Aiding the same, the proposed edge computing enabled, sustainable, opportunistic IoT-based intuitive data exchange, collection and operation scheme that employs a dynamic routing procedure that considers human interactions, their movement patterns and their decision-making process. The proposed scheme employs probabilistic history-based parameters that keep track of node interactions. The interactions are modelled on the Firefly PSO, allowing exploitation of the social mobility. The Fuzzy logic is used for hop decision making based on the social and attribute-based parameters which mimic intuitive and pragmatic decision making of humans in terms of social interactions and mobility.

A simple model for mixing intuition and analysis

Firefighters, emergency paramedics, and airplane pilots are able to make correct judgments and choices in challenging situations of scarce information and time pressure. Experts often attribute such successes to intuition and report that they avoid analysis. Similarly, laypeople can effortlessly perform tasks that confuse machine algorithms. OR should ideally respect human intuition while supporting and improving it with analytical modelling. We utilize research on intuitive decision making from psychology to build a model of mixing intuition and analysis over a set of interrelated tasks, where the choice of intuition or analysis in one task affects the choice in other tasks. In this model, people may use any analytical method, such as multi-attribute utility, or a single-cue heuristic, such as availability or recognition. The article makes two contributions. First, we study the model and derive a necessary and sufficient condition for the optimality of using a positive proportion of intuition (i.e., for some tasks): Intuition is more frequently accurate than analysis to a larger extent than analysis is more frequently accurate than guessing. Second, we apply the model to synthetic data and also natural data from a forecasting competition for a Wimbledon tennis tournament and a King’s Fund study on how patients choose a London hospital: The optimal proportion of intuition is estimated to range from 25% to 53%. The accuracy benefit of using the optimal mix over analysis alone is estimated between 3% and 27%. Such improvements would be impactful over large numbers of choices as in public health.

The influence of ergodicity on risk affinity of timed and non-timed respondents

Expected values are the metric most often used to judge human decision-making; when humans make decisions that do not optimize expected values, these decisions are considered irrational. However, while convenient, expected values do not necessarily describe the evolution of an individual after making a series of decisions. This dichotomy lies at the core of ergodicity breaking, where the expected value (ensemble average) differs from the temporal average of one individual. In this paper, we explore whether the intuition behind human decision-making optimizes for expected values or instead takes time growth rates into account. We do this using several stated choice experiments, where participants choose between two stochastic bets and try to optimize their capital. To evaluate the intuitive choice, we compare two groups, with and without perceived time pressure. We find a significant difference between the responses of the timed and the control group, depending on the dynamic of the choices. In an additive dynamic, where ergodicity is not broken, we observe no effect of time pressure on the decisions. In the non-ergodic, multiplicative setting, we find a significant difference between the two groups. The group that chooses under time pressure is more likely to make the choice that optimizes the experiment’s growth rate. The results of this experiment contradict the idea that people are irrational decision-makers when they do not optimize their expected value. The intuitive decisions deviate more from the expected value optimum in the non-ergodic part of our experiment and lead to more optimal decisions.

Steering through the pandemic: narrative analysis of school leader experiences in India

ABSTRACT The COVID −19 pandemic has disrupted the regular functioning of schools. Transitioning to online learning posed significant challenges to all stakeholders in the educational system. The continued changes and challenges due to the pandemic require school leaders to make intuitive decisions. School leaders’ vision and leadership styles can considerably impact successfully managing crises and challenges. The current study looks at the lived experiences of eight school leaders working in India. The data collected using an interview guide was subjected to narrative thematic analysis. The interviews were designed primarily in an open-ended manner to captivate the story of their experiences. The results yielded an understanding of how school leaders navigated through multiple challenges such as transitioning online, attending to student needs, financial challenges adopting crisis and collaborative leadership. The results highlight various personal feelings and experiences that helped the school leaders to hold up during the crisis. School leaders lack training in crisis management, and their mental health needs are neglected. The paper calls for professional support for school leaders in managing professional and personal challenges. The article gives direction for school professionals on focus areas and requirements in Indian schools.

Investigator's Intuition: Conditions of Formation and the Possibility of Implementation of Artificial Intelligence

The paper examines the issues of intuition in investigative practice. Relying on the positions of prominent forensic scientists, the importance of this heuristic mechanism for overcoming deadlocks caused not only by a shortage of information, but also by its overabundance is substantiated. The author suggests mechanisms for the development of intuition, the main of which is called the expansion of erudition, the accumulation of a large number of background knowledge. The second part of the article discusses the problems of artificial intelligence. Firstly, the topic of its potential to replace a person is covered. Secondly, the position existing in science is refuted, according to which the work of artificial neural networks is characterized as intuitive. Based on the analysis of practical examples and the synthesis of various scientific positions, the author formulated the following conclusions: 1. The intuitive decisions based on the probabilistic assumption assist the investigator in choosing specific investigative actions or tactics for their conduct. 2. Intuition is best developed among investigators with average work experience and broad erudition. 3. Intuition can and should be developed, mainly by expanding erudition, accumulating background experience. 4. The work of artificial neural networks cannot be called completely intuitive, since they act according to a given algorithm, even if generating a solution that is not accessible to human perception, whereas intuition is a creative process that goes beyond standard models. 5. To date, there is only a weak artificial intelligence, which is able to cover only situations of an overabundance of information, but not its deficit, although the latter very often require an appeal to intuition. 6. Intuition is a competitive advantage of humans over artificial intelligence, allowing our species to think more broadly, more versatile.

Understanding knowledge management and upskilling in Fourth Industrial Revolution: transformational shift and SECI model

PurposeThe adoption of knowledge management (KM) to steer new skills and capabilities among people provides evidence that KM not only offers competitive advantages but also provides a means for organisational survival, by improvising core capabilities or generate new ones that can drive people in the Fourth Industrial Revolution (4IR) era. This paperaim to identify critical new skills and capabilities among people within an organisation to stay competitive, innovative and relevant.Design/methodology/approachThe paper presents the findings on new skills assessment for Fourth Industrial Revolution. The study was carried out through an interview with a focus group discussion technique to gather data on the role of KM in creating new set of skills or capabilities in Fourth Industrial Revolution’s landscape. The study also reports a bibliographic study of critical skills based on more than a decade of related academic and industry publications to portray research trends and future directions.FindingsThere is a demand in “must-have” skills related to Industry 4.0 such as capability for complex decision-making, complex problem-solving, collaborative innovation, project management, creativity and critical thinking, social skill and social responsibility. While these skills are critical enablers to aiding individuals in the scenarios of plausible 4IR futures, several important new research trends that emerge have also not been adequately explored including KM and Industry 4.0 skill gap, skill evolution, machine knowledge, intuitive decision-making, rational decision-making, technostress, digital fluency, collaborative innovation, industrial policies, human–machine interaction and societal systems.Research limitations/implicationsThis research provides a roadmap for the next research trends and topics in the area of Fourth Industrial Revolution and new skills requirements. The study discusses some of the essential issues and challenges with upskilling required for Industry 4.0. It also focuses on how upskilling learning initiatives influence new knowledge creation. This primarily contributes to the educational field in deciding how and when to adopt appropriate strategies and identify which initiatives to best meet the needs of its community.Practical implicationsKM enables individuals to utilise their existing core capabilities or generate new ones for immediate investment in upskilling to meet current and future skills needs required by an organisation. Simply put, KM will improve the organisation’s talent-driven learning strategy and increase individuals’ ability to learn faster and attain sustainable competitive advantage in a fast-paced ever changing environment.Originality/valueThis paper is useful to academics, practitioners and policymakers in the fields of KM. The research provides initial insight into new skills mapping in the context of Fourth Industrial Revolution and the needs for researchers to understand the recent research trends in KM.

An Improved Intellectual Capital Management Method for Selecting and Prioritizing Intangible-Related Aspects: A Case Study of Small Enterprise in Thailand

This study develops a new integrated approach for improving deficiencies relating to executives’ intuitive or illogical decisions, mainly found in past intellectual capital management (ICM) methods. To simultaneously rectify several flaws, the process model of intellectual capital (IC), a traditional ICM method, is integrated using decision science methods—the analytic network process (ANP) and quality function deployment (QFD). The process model of IC is adopted as a core procedure of the proposed ICM approach. ANP is integrated to improve the ability to consider relationships among the IC critical factors and their impacts, while QFD is included to facilitate the systematic consideration and identification of correlations, linkages, and impacts between all IC-related elements from the business concept to strategic plans. The proposed method was applied to two case studies in one real enterprise in Thailand. The results of the implementation reveal the priorities of all IC-related aspects, and the first priority of key success factors (KSFs), key performance indicators (KPIs), and action plans (APs) are all associated with the organization in the structural capital dimension. The results demonstrate that the method may offer advantages with respect to the conceptual expectations and may prioritize critical IC factors and identify their weights. Furthermore, the improved method could indicate the correlations and impacts between related elements, such as critical factors and associated indicators. This study proposes a new comprehensive and systematic management framework by integrating different concepts—decision science methods and the ICM method. To the best of the authors’ knowledge, this improved approach has not been explored or proposed in earlier studies.

The susceptibility of management accountants to framing bias

PurposeThe emerging business partner role of management accountants (MAs) results in an increased requirement of MAs to make business decisions. Frame dependence cognitive biases regularly influence decisions made in conditions of uncertainty, as is the case in business decision-making. Consequently, this study aims to examine susceptibility of MAs to frame dependence bias.Design/methodology/approachA survey was conducted among an international sample of practising MAs. The proportion of MAs influenced by framing bias was analysed and compared to findings in other populations. Logistic regression was then used to determine whether MAs who exhibit a higher preference for evidence-based (as opposed to intuitive) decision-making are more susceptible to framing bias.FindingsDespite a comparatively high preference for evidence-based decision-making, the prevalence of framing bias among MAs is comparable to that of other populations. A higher preference for evidence-based decision-making was found to only be associated with higher susceptibility to endowment effect bias.Originality/valueTo the best of the authors’ knowledge, this is the first study to comprehensively examine framing bias for MAs as a group of decision-makers. Additionally, this study’s sample consists of practising MAs, and not only students.

Educating translation ethics: a neurocognitive ethical decision-making approach

ABSTRACT Translation ethics today is an area of growing concern, so is its education. In the new millennium, ethics has become an explicit and integrative component of translator education. Meanwhile, the objective of translation ethics education has shifted from preaching abstract, universalistic translator codes of ethics to training translation students’ ethical sensitivity and reflexive moral judgement (i.e., ethical decision-making). This study advances translation ethics education pivoted around students’ translational ethical decision-making competence (TEDC) by first providing a definition and a competence framework for TEDC and then sketching a competence-based education programme targeting translation students’ TEDC. In completing the tasks, this article draws theories and practices from cognitive psychology, where ethical decision-making has been studied extensively. On the one hand, it borrows the Neurocognitive Model of Ethical Decision-making to theorise the dual components of TEDC, i.e., competences of intuitive and rational ethical decision-making. On the other hand, it synthesises the relevant literature on intuition education and rationalist moral education to design a tentative TEDC-targeted translation ethics education programme. It argues that the proposed programme could be put to test in practice, and ethical decision-making holds the potential to become a productive entry point into translator ethics education.

A comparison of various aggregation functions in multi-criteria decision analysis for drug benefit-risk assessment.

Multi-criteria decision analysis is a quantitative approach to the drug benefit–risk assessment which allows for consistent comparisons by summarising all benefits and risks in a single score. The multi-criteria decision analysis consists of several components, one of which is the utility (or loss) score function that defines how benefits and risks are aggregated into a single quantity. While a linear utility score is one of the most widely used approach in benefit–risk assessment, it is recognised that it can result in counter-intuitive decisions, for example, recommending a treatment with extremely low benefits or high risks. To overcome this problem, alternative approaches to the scores construction, namely, product, multi-linear and Scale Loss Score models, were suggested. However, to date, the majority of arguments concerning the differences implied by these models are heuristic. In this work, we consider four models to calculate the aggregated utility/loss scores and compared their performance in an extensive simulation study over many different scenarios, and in a case study. It is found that the product and Scale Loss Score models provide more intuitive treatment recommendation decisions in the majority of scenarios compared to the linear and multi-linear models, and are more robust to the correlation in the criteria.

Intuitive Decision-making by Iranian Nurses of Patients with COVID-19: A Qualitative Study.

Introduction: Clinical decision-making related to coronavirus disease 2019 (COVID-19) is a new experience; thus, there is a lack in knowledge in this area. The aim of this study is to explore critical care nurses’ experience of intuitive decision-making in patients diagnosed with COVID-19. Methods: In this qualitative descriptive study, 16 nurses who had the experience of providing care for patients diagnosed with COVID-19 were selected through purposive sampling and participated in semi-structured interviews. The interviews were transcribed and finally analyzed through the conventional content analysis approach. Results: 62.5% of participants were females and the mean (SD) of the participant’s age and working experience were 36.56 (6.58) and 12.62 (5.59) years, respectively. Three main themes emerged out of the experiences of the nurses, including (a) inner revolution, (b) holistic awareness and (c) clinical wisdom. Conclusion: Critical care nurses use intuition in novel, complex situations where they often have to make quick and independent decisions. Understanding the phenomenon of intuition in clinical decision making increases the professional practice of nursing and leads to better quality care for patients, especially in acute, critical situations and pandemic diseases.

Ontology-Based Emotional Decision-Making in Self-Evolving Defensive Agents

Responding instantaneously in an unprecedented situation is inevitable to mitigate zero day attacks in the cyber world. This situation has strong resemblance with the fight for survival in biological world. Both being complex systems, the biological world and the cyber world have structural similarities. Emotions are the key factor used by biological beings to take intuitive decisions, which are key to survival in the natural world. Inspired from the idea of emotional decision making in natural world and the structural similarity shown by biological and cyber worlds, this paper proposes an ontological mechanism to implement artificial emotion-based decision making for autonomous agents in cyber security scenario. Emotions are modelled as manifestation of the goals of agents. Stimuli and response are connected through emotions using the ontological structure. The mechanism is proven useful in eliciting appropriate response for the stimuli using ontological reasoning through SPARQL queries.

Clinical decision making approaches and associated factors among nurses working in a tertiary teaching hospital

IntroductionThe clinical decision-making approach is an ongoing process in the nursing clinical setting and has an impact on the survival of the patient under their care. ObjectiveTo assess clinical decision-making approaches among hospital nurses. Methods and materialsAn institutional-based cross-sectional study was conducted from March 20 to April 4, 2018. A single population proportion formula for sample size determination and stratified sampling technique was used to select the study participants. The data was gathered using a pretested, structured, self-administered questionnaire and trained data collectors. The data was analyzed using SPSS version 20. To explain the study variables, frequency tables, figures, and percentages were used. Bivariable and multivariable logistic regressions were used to see the association between independent and dependent variables. ResultsAmong the total participants, 44.3% had intuitive and 55.7% had analytical decision-making approaches. Being at a higher educational level (aOR = 3.49, 95 % CI (1.45, 13.16)), having 10 years experience, having a high workload (aOR = 0.54, 95 % CI (0.37,0.42)) and having training (aOR = 3.11, 95 % CI (1.83, 5.28)) were significantly associated with intuitive clinical decision making. ConclusionThe intuitive decision-making approach was lower as compared to most of the previous studies. Variables such as higher experience, the high workload in the working unit, and talking training in clinical decision-making were significantly associated with the intuitive decision-making approach. Therefore, policymakers ought to implement the standard nurse-to-patient ratio and design training on how to handle the patient in different situations to achieve a good decision-making approach.

Business Analytics Components for Public Health Institution - Nursing Decision Area

In the health area, the use of Information Technologies is becoming increasingly indispensable, since they allow the assistance of health professionals by contributing to better health care practices. Thus, the Information Systems, allows to provide information and make it available faster and more accurately to assist health professionals in the decision-making process. The nursing area, in a hospital context, presents some complexities due to the volume and difficulty of data recording. Thus, Business Intelligence (BI) technology allows to be the solution to get around this. BI allows you to integrate data from different sources, offering quality information, that allows healthcare professionals to make more intuitive and effective decisions. This article presents a solution of a BI system developed for the University Hospital Center of Porto (CHUP) in order to allow health professionals, the extraction of knowledge through data analysis from the nursing area, serving as support in the decision-making process. To obtain the solution, it was necessary to perform all the tasks associated with Business Analytics (BA) projects, to create dashboards in the area of ​​efficiency and quality indicators for nursing practice.