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

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

A Dimensional Comparative by Computed Tomography of Path Printed Fabrication Algorithms of a Multi-Geometric Piece

The use and advancement of manufacturing technologies related to additive manufacturing significantly increased in the final decade of the 20th century. Technology progressions have led to the creation of methods for streamlining the printing process, emphasizing cutting down on manufacturing time, including the use of Genetic Algorithms (GA). The effect produced by changing the path pattern is interesting for two reasons: a) dimensional accuracy focused on preserving the component’s dimensions and b) the structural composition and strength that the printing process itself can produce. The objective of this article is to compare the effect of modifying the path (GA) versus the manufacturer algorithm (MA) of the 3d printed in two ways, one of them focused on the accuracy dimension of the geometries and the second from the structural point of view through the comparison by using Computed Tomography. Twenty-three geometrical pieces were employed in a template printed using FFF technology and PLA as the foundation material. The total process time needed to print the component was reduced by 11% due to the findings. Regarding the dimensional analysis, the average deviation produced by the GA path is less than that produced by the manufacturer’s suggestion. Regarding the porosity analysis, the GA shows a more significant void percentage but less void dispersion; ironically, the porosity concentrates based on the suggested materials. These results are crucial for product conceptualization and deposition printing planning

Impact of job strain and being overweight on middle and senior managers from the manufacturing sector in the Mexican industry

This research work establishes the relationship between job strain and being overweight among Mexican managers. Recently in Mexico, there has been a sharp increase in work-related diseases and mental health disorders. Furthermore, evidence shows that Mexicans rank top among employees who suffer from stress, yet research on the impact of job strain on the phenomena of obesity and being overweight among such vulnerable job positions in the industrial field is scarce. METHODS:The sample included 170 overweight middle and senior managers from six companies in the Mexican Manufacturing Industry. Cedillo’s Spanish version of the Job Content Questionnaire by Karasek was used, and the Body Mass Index (BMI) was used to characterize an overweight condition. Structural Equations Modelling studied the relationships among variables. RESULTS:Even though, the model shows a power of explanation of 6%(R2 = 0.06), the variable showing the greatest direct effect on the overweight variable is social support, with 21%(p < 0.01, β= –0.21). Regarding the total effects, only two of the four variables studied contributed directly to the overweight variation: the social support variable and the job demand variable. CONCLUSIONS:The results of the model hold a relatively low explanatory power; however, they do show a relationship between the studied variables. Also, the importance of the supervisor and co-workers’ support should be considered when developing organizational strategies for the prevention of work stress and an overweight condition

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

Burnout Syndrome in Middle and Senior Management in the Industrial Manufacturing Sector of Mexico

Due to globalization and the accelerated growth of technology, ever more employees of companies are affected by burnout syndrome, the psychological nature of which requires a prolonged response to chronic interpersonal stressors in work environments. The present research aims to validate the operability of the Maslach Burnout Inventory-General Survey (MBI-GS) using a sample of 378 professionals belonging to middle and senior management working in companies within the IMMEX sector (comprising the industrial-manufacturing, maquiladora and export services) located in the state of Baja California, Mexico. Firstly, an exploratory factor analysis using the principal components method and Varimax rotation was performed and the results revealed the existence of three factors representing more than 67 percent of the total variance. Secondly, a confirmatory factorial analysis was carried out performing appropriate results for the indices Chi-square goodness-of-fit model, Root Mean Square Error of Approximation (RMSEA), Normed Fit Index (NFI), Comparative Fit Index (CFI), Relative Fit Index (RFI), Parsimony Ratio (PRATIO) and Parsimony Normed Fit Index (PNFI), which are highly recommended by literature in these types of studies. Additionally, construct validity was satisfactorily verified. The factorial solution coincided with the Maslach Burnout Inventory original proposal so that this instrument can be considered a valid and reliable option to analyze the burnout levels in people pertaining to middle and senior management in these types of industries

Burnout and Obesity in Middle and Upper Management in the Manufacturing Industry of Baja California

Globally, companies are increasingly considering the importance of mental health in workers and their relationship with productivity, which has led to increased research on work stress, which showed that there is a relationship between stress related to work and health disorders, both physical and mental. This chapter addresses the analysis of two of the main consequences that a worker can develop when having work stress: burnout syndrome, measured by the Maslach burnout inventory general survey (MBI-GS) and obesity, through the body mass index (BMI). The study was carried out in 118 people who occupy middle and upper management of the manufacturing industry of Baja California, having as objective to find the relationship that exists between the two variables, using ordinal logistic regression, as well as to characterize the sample using mean difference and hypothesis testing. From this perspective, this chapter can serve as a guide to study the behavior of variables and propose organizational development strategies aimed at reducing and preventing these problems

Improvement project in higher education institutions: A BPEP-based model.

Improvement projects (IPs) are a fundamental element in any quality management system from any organization. In Higher Education Institutions (HEIs), IPs are constantly implemented to maintain excellence in academic and administrative processes. In this study, we propose a model for IP implementation that is based on the Baldrige Performance Excellence Program (BPEP). As a part of the model, we propose a series of research hypotheses to be tested. The data used to test the hypotheses were gathered from a questionnaire that was developed after an extensive literature review. The survey was administered to Mexican public HEIs, and more than 700 responses were collected. The data were assessed in terms of convergent and discriminant validity, obtaining satisfactory results. To test the proposed relationships between the model constructs, we utilized Structural Equation Modeling (SEM) using the software IBM SPSS Amos. The analysis confirmed the statistical validity of both the model and the hypotheses. In conclusion, our model for IP implementation is a useful tool for HEIs that seek to attain excellence in their processes through IPs

Assessing the Impact of Lean Healthcare on Inpatient Care: A Systematic Review

Healthcare services are facing challenges in increasing their efficiency, quality of care, and coping with surges in demand. To this end, some hospitals have implemented lean healthcare. The aim of this systematic review is to evaluate the effects of lean healthcare (LH) interventions on inpatient care and determine whether patient flow and efficiency outcomes improve. The review was performed according to PRISMA. We used six databases to search for studies published from 2002 to 2019. Out of 5732 studies, 39 measuring one or more defined outcomes were included. Hospital length of stay (LOS) was measured in 23 studies, 16 of which reported a reduction, turnover time (TOT) decreased in six out of eight studies, while the turnaround time (TAT) and on-time starts (OTS) improved in all five and seven studies, respectively. Moreover, eight out of nine studies reported an earlier discharge time, and the boarding time decreased in all four cases. Meanwhile, the readmission rate did not increase in all nine studies. Lastly, staff and patient satisfaction improved in all eight studies. Our findings show that by focusing on reducing non-value-added activities, LH contributed to improving patient flow and efficiency within inpatient care