Complexity and Conundrums in Pollution Control:Improving Effectiveness in Regulatory Compliance

The last century has witnessed a dramatic increase in the wealth of European nations and the well being of their inhabitants. The focus has, however, largely been upon economic growth to the detriment of people and the environment. It is only in recent years that governments have taken cognisance of the impacts of our actions and there is a growing realisation that the causal factors must be identified and addressed as a matter of urgency. One of the key problem areas is pollution and as such environmental protection has become increasingly important as a mechanism for safeguarding the quality of air, water and land. This involves a range of activities from setting standards to monitoring and reporting on discharges and emissions, through to the enforcement of legislation. In theory, this is a simple challenge, in practice, it has proven to be an extremely complex equation that might only begin to be addressed through research. In this context it is strange, and alarming, to find that while it is an axiom of good practice that policy is informed by research there has been a dearth of investigation in this field. The purpose of this paper is, therefore, to consider the issue of pollution, how it impacts on the environment, what measures have been established in pursuit of reducing the number of incidences and, most significantly, which strategies might be employed to avoid or ameliorate detrimental impacts.

Modelling Patient Duration of Stay to Facilitate Resource Management of Geriatric Hospitals

A fundamental aspect of health care management is the effective allocation of resources. This is of particular importance in geriatric hospitals where elderly patients tend to have more complex needs. Hospital managers would benefit immensely if they had advance knowledge of patient duration of stay in hospital. Managers could assess the costs of patient care and make allowances for these in their budget. In this paper, we tackle this important problem via a model which predicts the duration of stay distribution of patients in hospital. The approach uses phase-type distributions conditioned on a Bayesian belief network.

Using Coxian Phase-Type Distributions to Identify Patient Characteristics for Duration of Stay in Hospital

Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their (anticipated) length of stay in hospital. The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards.