Digital Transformation of Health Services Value Streams

This research proposes the use of value stream mapping to guide the choice of Healthcare 4.0 (H4.0) digital applications that are more prone to support the enhancement of value flows in health services. A three-step approach is developed, beginning with mapping the current and future state of the value streams, identifying improvement kaizen bursts, and finally evaluating H4.0 digital applications that best underpin improvements and comply with attributes that characterize well-succeeded technological innovations. This method is illustrated through a case study in the sterilization unit of a large university hospital. With the advent of Industry 4.0, the accelerated pace at which digital transformation has been conducted challenges healthcare organisations to prioritise the digital applications with the largest positive impact in their operations assertively. Our propositions provide means to integrate H4.0 into healthcare towards more effective health services values streams

Hospital Investment Decisions in Healthcare 4.0 Technologies: Scoping Review and Framework for Exploring Challenges, Trends, and Research Directions

BACKGROUND: Alternative approaches to analyzing and evaluating health care investments in state-of-the-art technologies are being increasingly discussed in the literature, especially with the advent of Healthcare 4.0 (H4.0) technologies or eHealth. Such investments generally involve computer hardware and software that deal with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decision-making. Besides, the use of these technologies significantly increases when addressed in bundles. However, a structured and holistic approach to analyzing investments in H4.0 technologies is not available in the literature. OBJECTIVE: This study aims to analyze previous research related to the evaluation of H4.0 technologies in hospitals and characterize the most common investment approaches used. We propose a framework that organizes the research associated with hospitals’ H4.0 technology investment decisions and suggest five main research directions on the topic. METHODS: To achieve our goal, we followed the standard procedure for scoping reviews. We performed a search in the Crossref, PubMed, Scopus, and Web of Science databases with the keywords investment, health, industry 4.0, investment, health technology assessment, healthcare 4.0, and smart in the title, abstract, and keywords of research papers. We retrieved 5701 publications from all the databases. After removing papers published before 2011 as well as duplicates and performing further screening, we were left with 244 articles, from which 33 were selected after in-depth analysis to compose the final publication portfolio. RESULTS: Our findings show the multidisciplinary nature of the research related to evaluating hospital investments in H4.0 technologies. We found that the most common investment approaches focused on cost analysis, single technology, and single decision-maker involvement, which dominate bundle analysis, H4.0 technology value considerations, and multiple decision-maker involvement. CONCLUSIONS: Some of our findings were unexpected, given the interrelated nature of H4.0 technologies and their multidimensional impact. Owing to the absence of a more holistic approach to H4.0 technology investment decisions, we identified five promising research directions for the topic: development of economic valuation methodologies tailored for H4.0 technologies; accounting for technology interrelations in the form of bundles; accounting for uncertainties in the process of evaluating such technologies; integration of administrative, medical, and patient perspectives into the evaluation process; and balancing and handling complexity in the decision-making process

Effects of lean interventions supported by digital technologies on healthcare services: a systematic review

Despite the increasing utilization of lean practices and digital technologies (DTs) related to Industry 4.0, the impact of such dual interventions on healthcare services remains unclear. This study aims to assess the effects of those interventions and provide a comprehensive understanding of their dynamics in healthcare settings. The methodology comprised a systematic review following the PRISMA guidelines, searching for lean interventions supported by DTs. Previous studies reporting outcomes related to patient health, patient flow, quality of care, and efficiency were included. Results show that most of the improvement interventions relied on lean methodology followed by lean combined with Six Sigma. The main supporting technologies were simulation and automation, while emergency departments and laboratories were the main settings. Most interventions focus on patient flow outcomes, reporting positive effects on outcomes related to access to service and utilization of services, including reductions in turnaround time, length of stay, waiting time, and turnover time. Notably, we found scarce outcomes regarding patient health, staff wellbeing, resource use, and savings. This paper, the first to investigate the dual intervention of DTs with lean or lean−Six Sigma in healthcare, summarizes the technical and organizational challenges associated with similar interventions, encourages further research, and promotes practical applications

Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies

Studies on the influence of contingency factors on the introduction of novel digital technologies into high- complexity systems, such as hospitals, are still incipient. As the introduction of Healthcare 4.0 (H4.0) usually implies in high capital expenditures and requires a more skilled labor force, such understanding gains relevance when considering hospitals in emerging economies, more likely to be resource-constrained. This study examines the effect of five contingency factors on the adoption of H4.0 technologies and associated barriers to H4.0 adoption in emerging economies; they are: hospital´s ownership and age, number of employees, number of inpatient beds, and functionality (teaching hospital or not). The analysis is based on a transnational survey with 159 middle and senior managers from 16 hospitals, located in Brazil, India, Mexico and Argentina. Results indicate that contingencies do affect both H4.0 technologies adoption and associated barriers although not homogeneously in terms of effect, being more prominent on technologies? adoption than on barriers to H4.0 implementation. Our study sheds light on these relationships, providing hospitals? managers a means to an- ticipate potential issues and handle eventual difficulties inherent to the context in which they are inserted.Fil: Tortorella, Guilherme Luz. Universidade Federal de Santa Catarina; BrasilFil: Fogliatto, Flávio Sanson. Universidade Federal do Rio Grande do Sul; BrasilFil: Espôsto, Kleber Francisco. Universidade de Sao Paulo; BrasilFil: Vergara, Alejandro Mac Cawley. Pontificia Universidad Catolica de Chile. Escuela de Ingeniería; ChileFil: Vassolo, Roberto Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Austral. Instituto de Altos Estudios; ArgentinaFil: Mendoza, Diego Tlapa. Universidad Autónoma de Baja California Sur; MéxicoFil: Narayanamurthy, Gopalakrishnan. University of Liverpool; Reino Unid

The impact of Industry 4.0 on the relationship between TPM and maintenance performance

Purpose In this paper, the authors examine the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance. Design/methodology/approach Data collection was carried out through a multinational survey with 318 respondents from different manufacturing companies located in 15 countries. Multivariate data techniques were applied to analyze the collected data. Diffusion of innovations theory (DIT) was the adopted theoretical lens for our research. Findings The authors’ findings indicate that I4.0 technologies that aim to process information to support decision-making and action-taking directly affect maintenance performance. Technologies oriented to sensing and communicating data among machines, people, and products seem to moderate the relationship between TPM practices and maintenance performance. However, the extent of such moderation varies according to the practices involved, sometimes leading to negative effects. Originality/value With the advances of I4.0, there is an expectation that several maintenance practices and performance may be affected. Our study provides empirical evidence of these relationships, unveiling the role of I4.0 for maintenance performance improvement

Sentiment Classification of Spanish Reviews: An Approach based on Feature Selection and Machine Learning Methods

Sentiment analysis aims to extract users' opinions from review documents. Nowadays, there are two main approaches for sentiment analysis: the semantic orientation and the machine learning. Sentiment analysis approaches based on Machine Learning (ML) methods work over a set of features extracted from the users' opinions. However, the high dimensionality of the feature vector reduces the effectiveness of this approach. In this sense, we propose a sentiment classification method based on feature selection mechanisms and ML methods. The present method uses a hybrid feature extraction method based on POS pattern and dependency parsing. The features obtained are enriched semantically through common-sense knowledge bases. Then, a feature selection method is applied to eliminate the noisy and irrelevant features. Finally, a set of classifiers is trained in order to classify unknown data. To prove the effectiveness of our approach, we have conducted an evaluation in the movies and technological products domains. Also, our proposal was compared with well-known methods and algorithms used on the sentiment classification field. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.786 to 0.898 for the aforementioned domains

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

In the Industry 4.0 era, healthcare services have experienced more dual interventions that integrate lean and six sigma with simulation modeling. This systematic review, which focuses on evidence-based practice and complies with the PRISMA guidelines, aims to evaluate the effects of these dual interventions on healthcare services and provide insights into which paradigms and tools produce the best results. Our review identified 4018 studies, of which 39 studies met the inclusion criteria and were selected. The predominantly positive results reported in 73 outcomes were mostly related to patient flow: length of stay, waiting time, and turnaround time. In contrast, there is little reported evidence of the impact on patient health and satisfaction, staff wellbeing, resource use, and savings. Discrete event simulation stands out in 74% of the interventions as the main simulation paradigm. Meanwhile, 66% of the interventions utilized lean, followed by lean-six sigma with 28%. Our findings confirm that dual interventions focus mainly on utilization and access to healthcare services, particularly on either patient flow problems or problems concerning the allocation of resources; however, most interventions lack evidence of implementation. Therefore, this study promotes further research and encourages practical applications including the use of Industry 4.0 technologies