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The outbreak of the COVID-19 pandemic has significantly impacted healthcare systems, presenting unforeseen challenges that necessitated the implementation of change management strategies to adapt to the new contextual conditions. This study aims to analyze organizational changes within the total hip replacement (THR) surgery pathway at multiple levels, including macro, meso and micro. It employs data triangulation from various sources to gauge the complexity of the change process and comprehend how multi-level decision-making influenced an unexpected shift.

A multicentric, single in-depth case study was conducted using a mixed-methods approach. Data sources included patient-reported outcome measures specific to the THR pathway and carefully structured in-depth interviews administered to managers and clinicians in two healthcare organizations serving the same population.

Decisions made at the macro level resulted in an overall reduction in surgical activities. Organizational changes at the meso level led to a complete cessation or partial reorganization of activities. Micro-level actions for change and adaptation revealed diverse and fragmented change management strategies.

Organizations with segmented structures may require a robust and structured department for coordinating change management responses to prevent the entire system from becoming stuck in the absorptive phase of change. However, it is important to recognize that absorptive solutions can serve as a starting point for genuine innovations in change management.

The utilization of data triangulation enables the authors to visualize how specific changes implemented in response to the pandemic have influenced the observed outcomes. From a managerial perspective, it provides insights into how future innovations could be introduced.

Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given.

Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations.

The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

Evaluating primary care for children has not before been undertaken on a national level, and only infrequently on an international level, an adult-focused perspective is the norm. The Models of Child Health Appraised (MOCHA) project explored the evaluation of quality of primary care for children in a nationally comparable way, which recognises the influence of all components of child well-being and well-becoming. Using adult-focused metrics fails to account for children’s physical and psycho-social development at different ages, differences in health and non-health determinants, patterns of disease and risk factors and the stages of the life course. To do this, we attempted to identify comparable measures of child health in the European Union and European Economic Area countries, we aimed to perform a structural equation modelling technique to identify causal effects of certain policies or procedures in children’s primary care and we aimed to identify and interrogate large datasets for key tracer conditions. We found that the creation of comparative data for children and child health services remains a low priority in Europe, and the largely unmet need for indicators covering all the healthcare dimensions hampers development of evidence-based policy. In terms of the MOCHA project objective of appraising models of child primary health care, the results of this specific work show that the means of appraisal of system and service quality are not yet agreed or mature, as well as having inadequate data to fuel them.

Medical training is a foundation on which better health care quality has been built. Freshly graduated doctors have required a good knowledge of practical competencies, which demands the importance of medical training activities. As of this, we propose a methodology to discover a process model for identifying the sequence of medical training activities that had implemented in the installation of a Central Venous Catheter (CVC) with the ultrasound technique. A dataset with twenty medical video recordings were composed with events in the CVC installation. To develop the process model, the adoption of process mining techniques of infrequent Inductive Miner (iIM) with a noise threshold value of 0.3 had done. A combination of parallel and sequential events of the process model was developed. Besides, process conformance was validated with replay fitness value about 61.1%, and it provided evidence that four activities were not correctly fit in the process model. The present study can assist upcoming doctors involved in CVCs surgery by providing continuous training and feedback on better patient care.

Particles bed binding by selective cement activation (SCA) method is a computer-aided manufacturing (CAM) technique used to produce cementitious elements. A computer-aided design file is sliced to generate G-codes before printing. This paper aims to study the effect of key input parameters for slicer software on the final properties of printed products.

The one factor at a time (OFAT) methodology is used to investigate the impact of selected parameters on the final properties of printed specimens, and the causes for the variations in outcomes of each variable are discussed.

Finer aggregates can generate a more compact layer, resulting in a denser product with higher strength. Fluid pressure is directly determined by voxel rate (r_V); however, high pressures enable better fluid penetration control for fortified products; for extreme r_Vs, residual voids in the interfaces between successive layers and single-line primitives impair mechanical strength. It was understood that printhead movement along the orientation of the parts in the powder bed improved the mechanical properties.

The design of experiment (DOE) method assesses the influence of process parameters on various input printing variables at the same time. As the resources are limited, a fractional factorial plan is carried out on a subset of a full factorial design; hence, providing physical interpretation behind changes in each factor is difficult. OFAT aids in analyzing the effect of a change in one factor on output while all other parameters are kept constant. The results assist engineers in properly considering the influence of variable variations for future DOE designs.

Using data from a continuous and ongoing cross-sectional web survey on hospitalisation service experiences in two Italian regions, the authors used multilevel and multivariate logistic regression models to identify factors related to users' demographics, emotional and informative support, technical and physical aspects of the provision, influencing satisfaction and willingness-to-recommend, before and during a crisis.

The value-in-use, defined in terms of a positive or negative value given by the experience with services, can be evaluated by users and influenced by the context of provision. The authors tested whether and how the value-in-use of services changed in a context of crisis. This study is applied to the healthcare sector during the coronavirus disease 2019 (COVID-19) epidemic, by evaluating the impact of the pandemic on hospitalisation experience.

Overall, analyses of 8,712 questionnaires found a greater value after the pandemic spread. In a time of crisis, technical and informative aspects of care were found to be most valued by patients that may recognise the extraordinary professionalism of workers during the crisis.

This study empirically suggests that context can affect the evaluation of value-in-use by patients during unprecedented circumstances, producing additional value-in-context.

These findings imply that during critical periods where there is less scope for expressions of gratitude and appreciation towards front-line workers, user-reported data can be used for motivating professionals and increase resilience. These results reiterate the need to continue collecting and reporting the service users' voices, including as activity within plans for managing challenging situations.

The level of healthcare system distress, due to the COVID-19 epidemic, positively affects patients' propensity to recommend, which the authors suggest is driven by healthcare services' feelings of reverse compassion. These findings imply that during critical periods where there is less scope for expressions of gratitude and appreciation towards front-line workers, user-reported data can be used for motivating professionals and increase resilience, which can have positive social implications. These results reiterate the need to continue collecting and reporting the service users' voices, including as activity within plans for managing challenging situations.

Research based on the intersection of theoretical and empirical research regarding value-in-use, value-in-context and service quality measured through user experience is scarce, in particular in the healthcare sector. The authors' findings set the direction for future research on the influence of context on value creation and value creation's perception by users, on the concept of reverse compassion and on reverse compassion's impact on organisational well-being, particularly in times of crisis.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide insights into patients' perceptions of satisfaction, experience and self-reported outcomes. However, little attention has been devoted to questions about factors fostering the use of patient-reported information to create value at the system level.

Action research design is carried out to elicit possible triggers using the case of patient-reported experience and outcome data for breast cancer women along their clinical pathway in the clinical breast network of Tuscany (Italy).

The case shows that communication and engagement of multi-stakeholder representation are needed for making information actionable in a multi-level, multispecialty care pathway organized in a clinical network; moreover, political and managerial support from higher level governance is a stimulus for legitimizing the use for quality improvement. At the organizational level, an external facilitator disclosing and discussing real-world uses of collected data is a trigger to link measures to action. Also, clinical champion(s) and clear goals are key success factors. Nonetheless, resource munificent and dedicated information support tools together with education and learning routines are enabling factors.

Current literature focuses on key factors that impact performance information use often considering unidimensional performance and internal sources of information. The use of patient/user-reported information is not yet well-studied especially in supporting quality improvement in multi-stakeholder governance. The work appears relevant for the implications it carries, especially for policymakers and public sector managers when confronting the gap in patient-reported measures for quality improvement.

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.