A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Differing methodologies to measure microbe dispersal during hand drying

Background: Having previously investigated the dispersal by different hand drying methods of a chemical indicator, fungi and bacteria on the hands of users, this new study assessed the potential for viral dispersal. Aims/Objectives: To determine differences between hand drying methods in their capacity to disperse viruses on the hands of users to other occupants of public washrooms and into the washroom environment. Method: A harmless virus was used to artificially contaminate the hands of participants prior to using three different hand drying devices (jet air dryer, warm air dryer, paper towel dispenser). Viral dispersal was assessed at different heights and distances from the hand drying devices and also at different times after use by means of an air sampler. Results: The jet air dryer was shown to produce significantly more dispersal of virus than the warm air dryer or paper towels. After use of the jet air dryer, high numbers of virus were detected at a range of heights with maximum numbers between 0.61 and 1.22 metres. Virus was also detected at distances of up to 3 metres from the jet air dryer and in the air for up to 15 minutes after its use. The warm air dryer and paper towel dispenser produced low or zero viral counts at different heights, different distances and times after use. Conclusion: Jet air dryers have a greater potential than other hand drying methods to disperse viruses on the hands and contaminate other occupants of a public washroom and the washroom environment.