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.

Rethinking gossip and scandal in healthcare organizations

Purpose – The purpose of this viewpoint paper is to argue that gossip is a neglected aspect of organizational communication and knowledge, and an under-used management resource. Design/methodology/approach – The paper challenges mainstream managerial assumptions that gossip is trivial or tainted talk which should be discouraged in the workplace. Instead, gossip is re-framed at an organizational level of analysis, which provides the opportunity for relational knowledge about systemic failure and poor practice in healthcare to surface. Findings – Rather than simply viewing gossip as an individual behaviour and interpersonal process, it is claimed that organizational gossip is also a valuable early warning indicator of risk and failure in healthcare systems. There is potentially significant value in re-framing gossip as an aspect of organizational communication and knowledge. If attended to (rather than neglected or silenced) gossip can provide fresh insights into professional practice, decision-making and relational leadership. Originality/value – This paper offers a provocative challenge to mainstream health organization and management thinking about gossip in the workplace. It offers new ways of thinking to promote patient safety, and prevent the scandals that have plagued healthcare organizations in recent years.