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Severe post-operative bleeding can lead to adverse outcomes for cardiac surgery patients and is a relatively common complication of cardiac surgery. One of the most effective drugs to prevent such bleeding, aprotinin, has been withdrawn from the market due to concerns over its safety. Alternative prophylactic drugs which can be given to patients to prevent bleeding can result in significant side effects and are expensive. For this reason it is difficult to make a clinical or economic case for administering these drugs to all cardiac surgery patients, and the prevailing view is that their use should be targeted at patients considered to be at relatively high risk of post-operative bleeding. However, there is currently no objective method for identifying such patients. Over the past 7 years, a team of clinicians and researchers at Papworth Hospital has collected data concerning post-operative blood loss for each cardiac surgery patient, totalling 11,592 consecutive records. They approached a team of operational researchers (MU, ACP, BR) with extensive experience of developing clinical risk models with the aim of devising a risk stratification scheme that could potentially be used to identify a cohort of higher risk patients. Such patients could be treated with the available prophylactic drugs or recruited to studies to evaluate new interventions. This paper is intended to describe the Operational Research process adopted in the development of this scheme. A concise description of the scheme and its clinical interpretation is published elsewhere.

A common method for modelling emergency referrals to a hospital in-patient unit is to assume that they follow a Poisson distribution. If lengths of stay are independent and identically distributed, then the distribution of bed demand can be derived by analysis of a queueing system with an infinite number of servers. Undemanding analysis can be used to express the mean and variance of bed demand in terms of simple formulae involving mean referral rates and the distribution of lengths of stay.

Recent estimates suggest that there are 2 million people in the UK living with or beyond a diagnosis of cancer and there is increasing attention being given to assessing the health and social care needs of this growing population. A simple analytical model has been constructed to estimate future trends in cancer prevalence, using existing prevalence estimates and trends in cancer incidence and survival. Separate estimates are generated for the contribution to future prevalence due to the current prevalent population and that due to future diagnoses. For the current prevalent population, we adopt a conditional survival model incorporating time since diagnosis in addition to age, tumour type and other factors. We discuss the analytical framework that has been constructed and its intended use in providing information that is useful to those planning health and social care services.

There is considerable evidence that prescribed drugs are not taken as intended. We present a stochastic pharmacokinetic mathematical model that can be used to assess the implications of non-adherence to prescribed drug regimens, on the part of either patients or health professionals. In the context of an orally administered drug, explicit equations are derived for the time varying mean and variance of the concentration of the drug in the serum, depending on the probability of each scheduled administration being omitted. The analysis presented here can be used to assess whether a given level of non-adherence is likely to have a clinical impact. The methods used can easily be adapted for use in the context of other routes of drug administration.

Pharmacokinetic models typically rely on a key assumption that patients take their medication as prescribed, whereas this is often not the case. We present a branch and bound algorithm that can be used to estimate the time-varying probability that, given a specified pattern of nonadherence to a prescribed regimen, a patient receives no therapeutic benefit from treatment. Use of this algorithm is a much faster method for obtaining this probability than exhaustive computation of the relevant probability distribution. The use of this algorithm to assess, in quantitative terms, the impact of non-adherence on the effectiveness of treatment provides a rational basis for evaluating the potential harm to patients.

The U.K. Government has introduced a new class of health service providers called Treatment Centres that provide routine elective services but that do not deal with emergency cases or non-routine elective patients. The introduction of these centres provides a possible mechanism for improving the efficiency of service delivery in terms of overall capacity requirements. In this paper we discuss a mathematical modelling approach that has been used to examine circumstances under which such benefits might be realised. As an illustration of the analysis, we present results obtained using data concerning urological services, for which there would seem to be benefits associated with the introduction of a TC in only a limited range of circumstances.

A consensus process has been developed to assist the UK National Confidential Enquiry into Patient Outcome and Death in identifying questions to be addressed in its studies. The process utilises the knowledge and experience of a panel of experts via a facilitated brainstorming exercise and employs a robust voting system to produce a list of candidate questions ordered in terms of the preferences expressed by individual panel members. The process which is described has been used successfully to assist the design of two national studies and it is anticipated that the process will be adopted for many studies in the future.

Haemoglobinopathies are a group of genetic disorders which are particularly prevalent among certain risk groups such as ethnic groups. Antenatal screening of potential haemoglobinopathy carriers allows early diagnosis for affected fetuses, leading to therapeutic intervention or termination. However, it has drawbacks such as screening costs and possible miscarriage as a result of fetal testing. This paper describes a model that allows the outcomes of a screening programme to be estimated for different risk groups. The model has been implemented as a computer package that can be used to inform decisions made by health care planners.

Environmental impact assessments are conducted on many developments as part of the planning process. There is currently wide interest in developing tools for assessing the impact on the health of the local population of proposed developments that will cause environmental changes. A computer model called ARMADA (Age Related Morbidity And Death Analysis) is described that provides a framework for investigating such health impacts. ARMADA generates estimates of age-related patterns of morbidity and mortality within the local population. These estimates incorporate the demographic features of the population in question and base-line information about the incidence of the disease classes being considered.

In the UK, hospitals are being encouraged to introduce booked admissions policies for elective inpatient services whereby patients are given a date for hospital admission months in advance rather than being put on a waiting list and then informed of their admission date at short notice. We address the question of what level of capacity is required to operate such a system if cancellations of booked elective patients are to be kept to a low level. Methods are presented for quantifying the day to day variation in bed demand due to emergency admissions, patient initiated cancellations and variable lengths of stay amongst patients.