Performing or not performing: what's in a target?

This paper analyses how providers have coped with the 4-hour target over the past 7 years. To do this, we used publicly available data from NHS Digital to track how long patients remain in accident and emergency (A&E) departments and their 'attendance disposal method'. Using this tool, we compared two A&E departments with similar arrival patterns and age profiles and that perform equally well against the target in a specific year.

Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model.

Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months.

Erratum to: Methods for evaluating medical tests and biomarkers.

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.

Models that represent the mechanisms that initiate and sustain atrial fibrillation (AF) in the heart are computationally expensive to simulate and therefore only capture short time scales of a few heart beats. It is therefore difficult to embed biophysical mechanisms into both policy-level disease models, which consider populations of patients over multiple decades, and guidelines that recommend treatment strategies for patients. The aim of this study is to link these modelling paradigms using a stylised population-level model that both represents AF progression over a long time-scale and retains a description of biophysical mechanisms.

Meeting the four-hour deadline in an A&E department.

Purpose: Accident and emergency (A&E) departments experience a secondary peak in patient length of stay (LoS) at around four hours, caused by the coping strategies used to meet the operational standards imposed by government. The aim of this paper is to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department.

Design/methodology/approach: A discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient.

Simulating the use of re-prioritisation as a wait-reduction strategy in an emergency department.

Background/aim: Simulation modelling has proven a useful approach for capturing the dynamic nature of emergency departments (EDs) and informing improvements to clinical and operational processes alike. However, few models have simulated the impact of the UK Department of Health's 4 h operational standard, which arguably has placed pressure to improve standards and performance, promoting the use of wait-reduction strategies to cope with the target in practice. The aim of this study was to determine the impact a re-prioritisation strategy has on the 4 h target by simulating the operation of an ED using a model that represents the flow of patients through the department.

Evaluating telemedicine: a focus on patient pathways.

Evaluations of telemedicine have sought to assess various measures of effectiveness (e.g., diagnostic accuracy), efficiency (e.