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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.

In view of the decline in the number of residential and nursing homes over the last few years, East Sussex, Brighton and Hove Health Authority was concerned about optimum usage of places. This project aimed to ascertain the views of home owners and managers on their working relationship with the health authority, local hospitals and social services.Respondents felt that the incoming residents were generally frailer and more dependent than a few years ago, funding allocations were inadequate, given the standards now expected of care homes, and there were delays of up to a year in reaching agreement. Several indicated that they would no longer take publicly funded clients unless the families could top up the payments. Ten percent of the private residential homes surveyed were for sale or due to close. Between 40 and 50 older people were estimated to be awaiting transfer to EMI or nursing homes in East Sussex. Over 35% of homes complained about inappropriate discharges of their residents from hospital, and a poor standard of nursing care. Communication with hospitals was poor and relationships with the health authority and social services needed strengthening. Routine admissions were appropriate, but hospital discharges may have been premature. Home owners/managers were dissatisfied with their relationship with the NHS. Improvements are needed if partnership working is to be developed.

This paper aims to propose an approach by human and material resources combination to reduce hospitals crowding. Hospitals crowding is becoming a serious problem. Many research works present several methods and approaches to deal with this problem. However, to the best of the authors’ knowledge – after a deep reading of literature – in all the proposed approaches, human and material resources are studied separately while they must be combined (to a given number of material resources an optimal number of human resources must be assigned and vice versa) to reflect reality and provide better results.

Hospital inpatient unit is chosen as framework. This unit crowding reduction is carried out by its capacity increasing. Indeed, inpatient unit modeling is performed to find the adequate combinations of human and material resources numbers insuring this unit stability and providing optimal service rates. At first, inpatient unit is modeled using queuing networks and considering only two resources (beds and nurses). Then, the obtained service rate formula is improved by including other resources and parameters using Baskett, Chandy, Muntz and Palecios (BCMP) queuing networks. This work is applied to “Princess Lalla Meryem” hospital inpatient unit.

Results are patients’ average number reduction by an average (in each block) of three patients, patients’ average waiting time reduction by an average of 9.98 h and non-admitted patients (to inpatient wards) access percentage of 39.26 per cent on average.

Previous works focus their studies on either human resources or material resources. Only a few works study both resources types, but separately. The context of those studies does not meet the real hospital context (where human resources are combined with material resources). Therefore, the provided results are not very reliable. In this paper, an approach by human and material resources combination is proposed to increase inpatient unit care capacity. Indeed, this approach consists of developing inpatient unit service rate formula in terms of human and material resources numbers.

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.

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 purpose of the paper is to investigate the potential relationships between emergency-care flow, patient mortality and healthcare costs using a patient-flow model.

The researchers used performance data from one UK NHS trust collected over three years to identify periods where patient flow was compromised. The delays’ root causes in the entire emergency care system were investigated. Event time-lines that disrupted patient flow and patient mortality statistics were compared.

Data showed that patient mortality increases at times when accident and emergency (A&E) department staff were struggling to admit patients. Four delays influenced mortality: first, volume increase and mixed admissions; second, process delays; third, unplanned hospital capacity adjustments and finally, long-term capacity restructuring downstream.

This is an observational study that uses process control data to find times when mortality increases coincide with other events. It captures contextual background to whole system issues that affect patient mortality.

Managers must consider cost-decisions and flow in the whole system. Localised, cost-focused decisions can have a detrimental effect on patient care. Attention must also be paid to mortality reports as existing data-presentation methods do not allow correlation analysis.

Previous studies correlate A&E overcrowding and mortality. This method allows the whole system to be studied and increased mortality root causes to be understood.

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.

The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users’ online behavior. This information enables real-time personalization of web content.

This paper models transitions between pages based upon the dwell time of the initial state and then analyzes data from a web shop, illustrating how pages that are linked “compete” against each other. Relative risks for web page transitions are estimated based on the dwell time within a clickstream and survival analysis is used to predict clickstreams.

Using survival analysis and user dwell times allows for a detailed examination of transition behavior over time for different subgroups of internet users. Differences between buyers and non-buyers are shown.

As opposed to other academic fields, survival analysis has only infrequently been used in internet-related research. This paper illustrates how a novel application of this method yields interesting insights into internet users’ online behavior.

A key goal of any online retailer is to increase their customer conversation rates. Using survival analysis, this paper shows how dwell-time information, which can be easily extracted from any server log file, can be used to predict user behavior in real time. Companies can apply this information to design websites that dynamically adjust to assumed user behavior.

The method shows novel clickstream analysis not previously demonstrated. Importantly, this can support the move from web analytics and “big data” from hype to reality.