Incidents during out-of-hospital patient transportation

Publisher's copy made available with the permission of the publisher © Australian Society of AnaesthetistsOut-of-hospital patient transportation (retrieval) of critically ill patients occurs within highly complex environments. Adverse events are not uncommon. Incident monitoring provides a means to better understand such events. The aim of this study was to characterize incidents occurring during retrieval to provide a basis for developing corrective strategies. Four organizations contributed 125 reports, documenting 272 incidents; 91% of forms documented incidents as preventable. Incidents related to equipment (37%), patient care (26%), transport operations (11%), interpersonal communication (9%), planning or preparation (9%), retrieval staff (7%) and tasking (2%). Incidents occurred during patient transport to the receiving facility (26%), at patient origin (26%), during patient loading (20%), at the retrieval service base (18%) and receiving facility (9%). Contributing factors were system-based for 54% and human-based for 42%. Haste (7.5%), equipment malfunctioning (7.2%) or missing (5.5%), failure to check (5.8%) and pressure to proceed (5.2%) were the most frequent contributing factors. Harm was documented in 59% of incidents with one death. Minimizing factors were good crew skills/teamwork (42%), checking equipment (17%) and patient (8%), patient monitors (15%), good luck (14%) and good interpersonal communication (4%). Incident monitoring provides sufficient insight into retrieval incidents to be a useful quality improvement tool for retrieval services. Information gathered suggested improvements in retrieval equipment design and use of alternative power sources, the use of pro formae for equipment checking, patient assessment, preparation for transportation and information transfer. Lessons from incidents in other areas applicable to retrieval should be linked for analysis with retrieval incidents.A. Flabouris, W. B. Runciman, B. Levingshttp://www.aaic.net.au/Article.asp?D=200530

Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes

Continuous superpositions of Ornstein-Uhlenbeck processes are proposed as a model for asset return volatility. An interesting class of continuous superpositions is defined by a Gamma mixing distribution which can define long memory processes. In contrast, previously studied discrete superpositions cannot generate this behaviour. Efficient Markov chain Monte Carlo methods for Bayesian inference are developed which allow the estimation of such models with leverage effects. The continuous superposition model is applied to both stock index and exchange rate data. The continuous superposition model is compared with a two-component superposition on the daily Standard and Poor's 500 index from 1980 to 2000

The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference

This paper introduces a new class of time-varying, measure-valued stochastic processes for Bayesian nonparametric inference. The class of priors is constructed by normalising a stochastic process derived from non-Gaussian Ornstein-Uhlenbeck processes and generalises the class of normalised random measures with independent increments from static problems. Some properties of the normalised measure are investigated. A particle filter and MCMC schemes are described for inference. The methods are applied to an example in the modelling of financial data