(1) Background: Cardiac amyloidosis or “stiff heart syndrome” is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the basis of the learning and training techniques of the algorithm. The approach consisted of using the information generated by the patients in each admission and discharge episode and treating it as data vectors to facilitate their aggregation. The large volume of clinical histories implied a high dimensionality of the data, and the lack of diagnosis led to a severe class imbalance caused by the low prevalence of the disease. (3) Results: Although there are few patients with amyloidosis in this study, the proposed approach demonstrates that it is possible to learn from clinical records despite the lack of data. In the validation phase, the algorithm first acted on data from the general study population. It then was applied to a sample of patients diagnosed with heart failure. The results revealed that the algorithm detects disease when data vectors profile each disease episode. (4) Conclusions: The prediction levels showed that this technique could be useful in screening processes on a specific population to detect the disease.

Academic literature and practitioners acknowledge that there is a need to improve efficiency and service quality in the healthcare industry. In Spain, osteoporotic fractures represent a great cost in socio-economic and morbi-mortality terms, hip fracture being the surgical pathology with the second highest consumption of resources. The research questions that govern this study concern the use of Lean principles to identify waste, and an evaluation of the application of an innovative approach in the hip fracture surgery process. A research design based on a case study and action research was developed. Findings relate to (i) the identification of the main types of waste or muda (being the most frequent delay, transportation, over-processing and defects); (ii) the analysis of existing processes based on a Lean approach (identifying opportunities for improvement as a reduction of the number of steps and participants, improving communication, automation, standardization, etc.); and (iii) the application of an innovative process based on the Lean approach and action research in the healthcare industry. This research provides insights for academia, practitioners, management, and society: waste identification and process redesign helps to continue the improvement of operations, increase efficiency, reduce costs, and enhance services, providing benefits to patients, families, hospital employees, and the healthcare system.

The detection and prevention of addictive behaviour at an early age is essential given the relationship between the age of the onset of consumption and the appearance of addiction disorders. The aim of this study was to describe the behavior related to substance use and addictive behaviors in adolescents at secondary school from 12 to 16 years of age. A cross-sectional descriptive study has been conducted. The prevalence of consumption of different addictive substances (alcohol, tobacco, cannabis, cocaine) and addictive behaviours (use of social networks and video games) were collated, and the influence of the surrounding social environment and risk perception were evaluated. The final sample was 1298 students. Alcohol, tobacco and cannabis use reflect the prevalence of last month’s consumption: 14% (11.8–15.6), 15% (13.4–17.4) and 3% (1.9–2.7) respectively. 76% of the sample frequently use the Internet (5–7 days per week). There is a positive association between the frequency of use and use in the immediate environment. The relationships found show the need for educational and preventive intervention aimed at parents and students that will allow them to know and effectively deal with possible problems associated with the consumption of addictive substances.

A number of programs have been developed to promote the contact between adolescents and mentally-ill patients, in order to break the stigma, improve understanding, promote mental health and prevent substance abuse. The aim of this study was to describe the experience of patients with schizophrenia, high school students, and their social context, participating in a short-term cohousing initiative. A qualitative case-study approach was implemented. Patients with schizophrenia from the San Juan de Dios Psychiatric Hospital, female students from Almen High School, and participants from their social context (parents, hospital staff, and teachers) were included, using purposeful sampling. Data were collected from 51 participants (15 patients, nine students, 11 hospital staff, six teachers, 10 parents) via non-participant observation, focus groups, informal interviews, researchers’ field notes and patients’ personal diaries and letters. A thematic analysis was performed. The themes identified included a) learning to live together: students and patients participate and learn together; b) the perception of the illness and the mentally-ill: the barrier between health and disease is very slim, and society tends to avoid contact with those who are ill; c) change: a transformation takes place in students, in their self-perception, based on the real and intense nature of the experience; d) a trial and an opportunity: patients test their ability to live outside the hospital; e) discharge and readmission: discharge is experienced as both a liberation and a difficulty, whereas relapse and readmission are experienced as failures. Our findings can help us to better understand schizophrenia and encourage a more positive approach towards both the illness and those who suffer from it. These results may be used for the development of cohousing programs in controlled environments.

Purpose – This study develops and validates a measurement scale for assessing the corporate image of companies that use data-driven marketing in their decision-making and actions in online retailing. Theoretical framework – The study is grounded in theories of corporate image and consumer behaviour, integrating concepts of data-driven marketing and privacy to develop the DDMI scale. Design/methodology/approach – A mixed methods approach is employed, beginning with a deductive literature review and qualitative expert interviews to generate scale items, followed by a pilot study and a large-scale survey of 301 consumers via Amazon MTurk. Exploratory and confirmatory factor analyses are conducted to validate the scale. Findings – DDM strategies significantly affect how customers perceive a company’s image. The DDMI provides a validated scale that measures aspects that are important to customers, such as privacy concerns and personalised customer experience. It reveals that effective communication, efficient payment processes and robust customer support are vital for a positive corporate image. Practical & social implications – The study offers a novel tool to assess corporate image in environments characterised by high data usage. It enables companies to refine their DDM strategies by identifying how specific practices affect consumer perceptions. Originality/value – This research introduces the first validated scale to measure consumer perceptions of corporate image in DDM contexts. It advances marketing theory by capturing key dimensions of the digital era, personalisation, privacy and support. Objetivo – Este estudo desenvolve e valida uma escala de medição para avaliar a imagem corporativa de empresas que utilizam o marketing orientado por dados em suas decisões e ações no varejo on-line. Referencial teórico – O estudo baseia-se em teorias de imagem corporativa e comportamento do consumidor, integrando conceitos de marketing orientado por dados e privacidade para desenvolver a escala DDMI. Metodologia – Uma abordagem de métodos mistos é empregada, começando com uma revisão dedutiva da literatura e entrevistas qualitativas com especialistas para gerar itens da escala, seguidas de um estudo piloto e uma pesquisa em larga escala com 301 consumidores por meio da Amazon MTurk. São realizadas análises fatoriais exploratórias e confirmatórias para validar a escala. Resultados – As estratégias de marketing orientado por dados afetam significativamente a forma como os clientes percebem a imagem da empresa. A DDMI fornece uma escala validada que mede aspectos importantes para os clientes, como preocupações com a privacidade e a experiência personalizada. Ela mostra que uma comunicação eficaz, processos de pagamento eficientes e um suporte robusto ao cliente são essenciais para uma imagem corporativa positiva. Implicações praticais e sociais – O estudo oferece uma nova ferramenta para avaliar a imagem corporativa em ambientes com alto uso de dados. Ele permite que as empresas aperfeiçoem suas estratégias de marketing orientado por dados, identificando como práticas específicas afetam a percepção dos consumidores. Contribuições – Esta pesquisa apresenta a primeira escala validada para medir a percepção dos consumidores sobre a imagem corporativa em contextos de marketing orientado por dados. Ela contribui para o avanço da teoria de marketing ao capturar as principais dimensões da era digital, personalização, privacidade e suporte.