Key biological issues in contact lens development

A contact lens is a medical device widely used as an alternative to spectacles in order to correct refractive vision problems. The evolution of polymeric biomaterials has heralded a continuous development in the materials used to produce contact lenses and maximize patient comfort and limit adverse events. Microbial keratitis (MK) is a relatively rare but potentially devastating condition associated with contact lens use, particularly with the extended wear of hydrogel lenses. It is the principal complication related to contact lens wear and the large population at risk make it a public health concern. Bacterial binding to the contact lens material is a precursor to the development of MK and is influenced by properties of the material and the bacteria. In order for bacteria to infiltrate the cornea there must be some degree of corneal damage, usually caused by trauma or hypoxia. The most recent materials available aim to allow the continuous wear of lenses while limiting corneal hypoxia, thus helping to prevent the development of MK. Limitations to the treatment of MK require that novel approaches may be necessary in order to limit bacterial adhesion to contact lens materials.

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.

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.

Simulating Coxian Phase-type Distributions for Patient Survival

Coxian phase-type distributions are a special type of Markov model that can be used to represent survival times in terms of phases through which an individual may progress until they eventually leave the system completely. Previous research has considered the Coxian phase-type distribution to be ideal in representing patient survival in hospital. However, problems exist in fitting the distributions. This paper investigates the problems that arise with the fitting process by simulating various Coxian phase-type models for the representation of patient survival and examining the estimated parameter values and eigenvalues obtained. The results indicate that numerical methods previously used for fitting the model parameters do not always converge. An alternative technique is therefore considered. All methods are influenced by the choice of initial parameter values. The investigation uses a data set of 1439 elderly patients and models their survival time, the length of time they spend in a UK hospital.