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NCEPOD states that all emergency patients must have prompt access to theatres, critical care facilities, and appropriately trained staff 24 hours/day, every day of the year. The purpose of this paper is to determine waiting time for plastic surgery trauma patients and the financial implications to the NHS.

The approach was a prospective audit of emergency surgery (Lister Hospital, Stevenage) from July 2005‐January 2006. Delay times were calculated from booking time to time of operation. These were assessed on a standard of a day surgery unit, where the ideal maximum is a half day wait. Financial implications were calculated. The number of UK units without a dedicated list is highlighted.

A total of 615 operations were booked, 60 per cent of which were assessed as suitable for a DSU set up. With an ideal standard of half a day's wait, an average 22 per cent of patients achieved this, with 64 per cent of patients waiting one excess day. On average patients waited an excess of 1.3 days. An average of 24 excess bed days/week (bed cost £300/day) were used waiting for surgery. This equates to 1,400 bed days (£400,000/annum).

The paper shows how it is possible that 1,000 bed days/annum (£300,000) could be saved by a DSU type setup. With 61 plastic surgery units in the UK, 40 per cent reported no dedicated trauma list (93 per cent response rate). This is a potential saving of £8,400,000 per annum. From this audit a half day dedicated DSU list was created, and a re‐auditing process will occur.

Aims to determine the bed requirement for a large district general hospital with a four‐bedded surgical high‐dependency unit (HDU). All admissions from 1 December 1997 to 28 February 1998 (aged over 15 years) were used in this study. Overall, the study showed that an estimated 1.6 per cent of adult, in‐patient admissions required HDU care (there were 10,949 admissions to the hospital during the study period). The bed occupancy for the surgical HDU during the study period was 90 per cent (319 actual bed days/356 potential bed days). The average length of stay was four days. The mean, median and modal daily bed requirement was six. The needs assessment study clearly demonstrated that it was essential for the Trust to provide more comprehensive care to a wider range of patients than was currently available in the hospital, particularly those with serious medical conditions, and recommended the provision of an eight‐bedded HDU.

A reduction in waiting lists for in‐patient oral surgery together with an improved service for minor procedures by the use of a day‐bed facility is the proposed solution to the problem studied. The approach used relies on adopting a production/operations management (POM) viewpoint combined with an interfacing methodology based on that of the soft systems school.

Forecasting the number of bed days (NBD) needed within a large hospital network is extremely challenging, but it is imperative that management find a predictive model that best estimates the calculation. This estimate is used by operational managers for logistical planning purposes. Furthermore, the finance staff of a hospital would require an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.

This chapter examines two ways of estimating the NBD for a large hospital system, and it builds from previous work comparing time regression and an autoregressive integrated moving average (ARIMA). The two approaches discussed in this chapter examine whether using the total or combined NBD for all the data is a better predictor than partitioning the data by different types of services. The four partitions are medical, maternity, surgery, and psychology. The partitioned time series would then be used to forecast future NBD by each type of service, but one could also sum the partitioned predictors for an alternative total forecaster. The question is whether one of these two approaches outperforms the other with a best fit for forecasting the NBD. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting when a large database of information can be partitioned into smaller segments.

The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector.

Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment.

Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds.

The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week.

The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.

The purpose of this paper is twofold: first, to examine the effect of interventional measures on admitted children and, second, to estimate the level of utilization of nutrition rehabilitation centres (NRCs) through bed occupancy rate in India.

A cross-sectional study involving review of records of malnourished children admitted during 1st January to 31st December 2014 in four districts of Northern Karnataka, India. The data were collected during January 2015.

A statistically significant difference was obtained between the weight of children at admission and discharge (p < 0.001). The average weight gain during the stay at the centres was 6.1 ± 7.1 g/kg/day. The proportion of severely malnourished children decreased from 56 per cent at admission to 32 per cent at discharge. The children who stayed for a stipulated period of 14 days were fully recovered and discharged from the centre compared to those stayed for lesser period. Poor follow-up visits of discharged children are observed, and the average bed occupancy rate was only 40 per cent.

This study showed that, the NRCs were effective in improving the condition of admitted children, but the utilization of these NRCs was sub-optimal in Karnataka. This study pointed to a number of operational issues that need to be addressed if these NRCs are being used effectively.

Despite contemporary mental health services shifting to a community‐based model of care, acute inpatient care is still necessary for many patients experiencing an acute psychological crisis. As inpatient services cost the National Health Service nearly £600 million a year, initiatives to reduce time spent in hospital, whilst maintaining safety and quality, are being actively promoted on a national level. Mental health patients in Hertfordshire spend on average two weeks in hospital during their acute crisis. The aim of this study is to reduce bed occupancy rates by implementing a novel approach to inpatient management.

A pragmatic controlled clinical trial design was used to address the aim of this study.

The results demonstrate that, compared to a functionalized inpatient ward (one with a designated inpatient consultant psychiatrist conducting a weekly ward round), it is possible to reduce bed occupancy rates without increasing demand on other wards. Furthermore, 28‐day readmission rates and total admissions over seven days were reduced.

Limitations relating to the study design and potential generalisability to similar services are discussed. Further studies to triangulate the data are suggested.

This novel approach to inpatient management provides exciting data that suggest patients can be moved along the acute pathway more efficiently. Recommendations for further studies are made in light of the findings.

This paper will appeal to acute care clinicians, service managers, and commissioners of mental health services. It provides an evidence base for making efficiencies within the acute service whilst maintaining quality of care for patients.

The study sought to determine the direct and indirect costs of hospitalization incurred by the provider and consumer in a public funded tertiary care hospital in Islamabad, Pakistan. The cost per hospital bed per day in both medical and mixed specialty wards was determined in terms of infrastructure, manpower, diagnostic investigations, drugs and utilities, and the cost to the hospitalized individual in terms of transport, food, drugs, investigations, and indirect costs such as time and material requirements of relatives or friends resulting from and associated with the hospitalization. The average daily cost per occupied bed to the institution amounted to Rs. 777 (US $ 18.95) per day. The average cost borne by the patient was Rs. 1,071 (US $ 26.10) per day or 58 percent of the total daily cost of a (medical) bed. A disproportionate share of this expense was under specific subheadings, namely purchase of medicines, laboratory services, transportation and food. Study findings confirm that in contrast to stated national policy, stationary health care is not only not free but that the consumer may end up directly paying more than the state for the services she/he receives.

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.

Over 8 per cent of the Dutch nursing home population is bedfast, and this number is slowly increasing. The quality of life (QoL) of this population is lower than that of residents who are still mobile. Little research has been conducted on how to improve the QoL of this bedfast population, particularly through making technological adjustments to the bed and the direct surroundings. The purpose of this paper is to gain insight into the QoL of bedfast residents and how to improve this through technology.

A mixed-method multi-case study with thematic analysis was conducted in two nursing homes with seven participants based on semi-structured interviews and Short Form-12 questionnaire.

The major causes of the experienced low QoL were the limited opportunities for engaging in social contacts with others, and coping with the dependency on other people and having limited control. Participants suggested improvements of QoL through the application of modern communication technologies to engage in social contacts and to control the bed itself and environment around the bed.

The results may help improve the design of the bed and the direct environment in order to improve the QoL of bedfast nursing home residents.

The QoL of bedfast nursing home residents has not been studied before in relation to the bed itself and technological solutions that may help improve the QoL and level of control.