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Purpose – The purpose of this paper is to verify whether an evolutionary model outperforms logistic regression in determining the institutional placement decisions made by a London social service department panel. Design/methodology/approach – Genetic chromodynamics models an algorithm within the Michigan evolutionary classifier. Hence multiple classification rules evolve simultaneously. The dataset as described by Xie et al. is used. Two‐thirds of randomly selected cases are for training and one third for testing. Indicator weights are set between 0 and 1. Findings – Of 275 placements, 40 per cent represent residential homes, 48 per cent nursing homes, 12 per cent nursing long‐stay and two hospital long‐stay. In ten runs, 89.18 per cent were correctly placed (range 81.6 to 97.7 per cent); 5.07 per cent wrongly placed (range 1.2 to 8.0 per cent) and 5.75 per cent unplaced (range 0.0 to 11.5 per cent). Changing the 0.99 weights to 0.90 and 0.80 placed 87.6 and 87.9 per cent correctly. Research limitations/implications – Data came from written records. Errors in transcription and placement could not be checked. Other facts, or the weights, may be influencing placement decisions. Practical implications – Xie et al. matched 78 per cent of 195 placements. The evolutionary model outperformed logistic regression both in placements evaluated (275/195) and accuracy (89/78 per cent). Therefore, it could be used as a first line management information tool, revealing whether guidelines are followed. Originality/value – The authors have developed and tested a computational model, which could be used to evaluate institutional placement decisions in the UK “market”. Further development and exploitation would facilitate greater understanding of the needs old people and the resources necessary for their appropriate management.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions.

A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department’s performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients.

Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds.

This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.

Agglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.

A total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.

Tourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.

The study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.

Anti-epidemic Emergency Projects (AEEPs) have unique characteristics such as a short construction period, high-quality requirements, complex construction environment, many construction participants and many uncertain affecting factors. The purpose of this paper was to propose the establishment method for the Construction Community (CC) of AEEPs (CC-AEEPs) by analyzing the management features of AEEPs, to establish the method of the Social Network Analysis (SNA) for CC-AEEPs, and to apply this method and the framework to Wuhan Huoshenshan Hospital for their verification.

According to the CC theory, this paper explored the member composition and the establishment method for CC-AEEPs. The optimal management factors of CC-AEEPs were proposed by combining the management features of AEEPs and the SNA method for CC-AEEPs was further established. Finally, the applicability of the method was verified through a case study, and some countermeasures for the CC-AEEP social networks were proposed.

The establishment of CC-AEEPs is an important guarantee to complete AEEPs with top speed and high quality. Ten types of CC-AEEP members all played different but irreplaceable roles in cooperative construction, among which the Government, the Contractor, and the Supervisor had outstanding performances. The SNA method could effectively analyze the complexity and cooperative relationship among the members in four aspects. The case study of Huoshenshan Hospital validated the important role of CC-AEEP and its social network in the AEEP research providing beneficial enlightenment for the cooperative optimization path of the AEEP construction participants.

The new establishment method for CC-AEEPs was proposed from the perspective of “cooperation among human, society, and engineering” according to the theories of the engineering sociology and the CC. In this paper, the SNA method was applied to the research on the AEEP construction for the first time and the SNA method for CC-AEEPs was purposed. The optimal management factors of CC-AEEPs and the expansion path of the CC-AEEP social networks were proposed according to the whole-process tracking of AEEPs in Wuhan.

The purpose of this article is to find decision‐making models for the design and control of processes regarding patient flows, considering various problem types, and to find out how usable these models are for managerial decision making.

A systematic review of the literature was carried out. Relevant literature from three databases was selected based on inclusion and exclusion criteria and the results were analyzed.

A total of 68 articles were selected. Of these, 31 contained computer simulation models, ten contained descriptive models, and 27 contained analytical models. The review showed that descriptive models are only applied to process design problems, and that analytical and computer simulation models are applied to all types of problems to approximately the same extent. Only a few models have been validated in practice, and it seems that most models are not used for their intended purpose: to support management in decision making.

The comparability of the relevant databases appears to be limited and there is an insufficient number of suitable keywords and MeSH headings, which makes searching systematically within the broad field of health care management relatively hard to accomplish.

The findings give managers insight into the characteristics of various types of decision‐support models and into the kinds of situations in which they are used.

This is the first time literature on various kinds of models for supporting managerial decision making in hospitals has been systematically collected and assessed.

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.

The intensive care unit (ICU) in a hospital caters for critically ill patients. The number of the ICU beds has a direct impact on many aspects of hospital performance. Lack of the ICU beds may cause ambulance diversion and surgery cancellation, while an excess of ICU beds may cause a waste of resources. This paper aims to develop a discrete event simulation (DES) model to help the healthcare service providers determine the proper ICU bed capacity which strikes the balance between service level and cost effectiveness.

The DES model is developed to reflect the complex patient flow of the ICU system. Actual operational data, including emergency arrivals, elective arrivals and length of stay, are directly fed into the DES model to capture the variations in the system. The DES model is validated by open box test and black box test. The validated model is used to test two what‐if scenarios which the healthcare service providers are interested in: the proper number of the ICU beds in service to meet the target rejection rate and the extra ICU beds in service needed to meet the demand growth.

A 12‐month period of actual operational data was collected from an ICU department with 13 ICU beds in service. Comparison between the simulation results and the actual situation shows that the DES model accurately captures the variations in the system, and the DES model is flexible to simulate various what‐if scenarios.

DES helps the healthcare service providers describe the current situation, and simulate the what‐if scenarios for future planning.

Students with developmental/intellectual disabilities (ID/DD) often have serious health issues that require additional medical care and supervision. Serious health issues also mean increased absence and additional lags in academic achievement and development of adaptive and social skills. The incorporation of artificial intelligence in the education of a child with ID/DD could ameliorate the educational, adaptive and social skill gaps that occur as a direct result of persistent health problems.

The literature regarding the use of artificial intelligence in education for students with ID/DD was collected systematically from international online databases based on specific inclusion and exclusion criteria. The collected articles were analyzed deductively, looking for the different gaps in the domain. Based on the literature, an artificial intelligence–based architecture is proposed and sketched.

The findings show that there are many gaps in supporting students with ID/DD through the utilization of artificial intelligence. Given that the majority of students with ID/DD often have serious and chronic and comorbid health conditions, the potential use of health information in artificial intelligence is even more critical. Therefore, there is a clear need to develop a system that facilitates communication and access to health information for students with ID/DD, one that provides information to caregivers and education providers, limits errors, and, therefore, improves these individuals' education and quality of life.

This review highlights the gap in the current literature regarding using artificial intelligence in supporting the education of students with ID/DD. There is an urgent need for an intelligent system in collaboration with the updated health information to improve the quality of services submitted for people with intellectual disabilities and as a result improving their quality of life.

This study contributes to the literature by highlighting the gaps in incorporating artificial intelligence and its service to individuals with ID/DD. The research additionally proposes a solution based on the confounding variables of students’ health and individual characteristics. This solution will provide an automated information flow as a functional diagnostic and intervention tool for teachers, caregivers and parents. It could potentially improve the educational and practical outcomes for individuals with ID/DD and, ultimately, their quality of life.

The purpose of this paper is to address a gap in operations management empirical research through the use of diffusion of innovation (DOI) theory to develop further insight into patient flow and bed management, a problem that has been taxing healthcare organizations across the world.

The study used an action research (AR) approach and was conducted over an 18-month period within an acute hospital in the north east of England. Data were generated through enacting AR cycles, interviews, participant observation, document analysis, diaries, meetings, questionnaires and statistical analysis.

The research conducted within this study has not only led to practical outcomes for the hospital in terms of the successful adoption of a new patient flow system but has also led to new knowledge about the determinants of diffusion for technological and process innovations in healthcare organizations which are complex and highly political.

AR is not suited to all organizations and is most appropriate within those that are culturally attuned to participative and democratic ways of working. The results from this study are not generalizable but some similar organizations may see merits in this approach.

The AR approach has supported the hospital in adopting the new system, PFMS. This system is helping to improve the quality of patient care, providing facilities to support the work of clinicians, aiding timely discharge of well patients back into the community and saving the hospital money in terms of not needing to open emergency “winter” wards.

From an operations management perspective this work has demonstrated the potential to bring theory, in this case DOI theory, and practice closer together as well as show how academic research can impact organizations. Local-H intends to continue developing its AR approach and take it into other systems projects.