Computer‐based teaching and evaluation of introductory statistics for health science students: Some lessons learned

In recent years, it has become possible to introduce health science students to statistical packages at an increasingly early stage in their undergraduate studies. This has enabled teaching to take place in a computer laboratory, using real data, and encouraging an exploratory and research‐oriented approach. This paper briefly describes a hypertext Computer Based Tutorial (CBT) concerned with descriptive statistics and introductory data analysis. The CBT has three primary objectives: the introduction of concepts, the facilitation of revision, and the acquisition of skills for project work. Objective testing is incorporated and used for both self‐assessment and formal examination. Evaluation was carried out with a large group of Health Science students, heterogeneous with regard to their IT skills and basic numeracy. The results of the evaluation contain valuable lessons

Using Markov Models to Characterize and Predict Process Target Compliance

Processes are everywhere, covering disparate fields such as business, industry, telecommunications, and healthcare. They have previously been analyzed and modelled with the aim of improving understanding and efficiency as well as predicting future events and outcomes. In recent years, process mining has appeared with the aim of uncovering, observing, and improving processes, often based on data obtained from logs. This typically requires task identification, predicting future pathways, or identifying anomalies. We here concentrate on using Markov processes to assess compliance with completion targets or, inversely, we can determine appropriate targets for satisfactory performance. Previous work is extended to processes where there are a number of possible exit options, with potentially different target completion times. In particular, we look at distributions of the number of patients failing to meet targets, through time. The formulae are illustrated using data from a stroke patient unit, where there are multiple discharge destinations for patients, namely death, private nursing home, or the patient’s own home, where different discharge destinations may require disparate targets. Key performance indicators (KPIs) of this sort are commonplace in healthcare, business, and industrial processes. Markov models, or their extensions, have an important role to play in this work where the approach can be extended to include more expressive assumptions, with the aim of assessing compliance in complex scenarios

Semi-Markov Models for Process Mining in Smart Homes

Generally, these days people live longer but often with increased impairment and disabilities; therefore, they can benefit from assistive technologies. In this paper, we focus on the completion of activities of daily living (ADLs) by such patients, using so-called Smart Homes and Sensor Technology to collect data, and provide a suitable analysis to support the management of these conditions. The activities here are cast as states of a Markov-type process, while changes of state are indicated by sensor activations. This facilitates the extraction of key performance indicators (KPIs) in Smart Homes, e.g., the duration of an important activity, as well as the identification of anomalies in such transitions and durations. The use of semi-Markov models for such a scenario is described, where the state durations are represented by mixed gamma models. This approach is illustrated and evaluated using a publicly available Smart Home dataset comprising an event log of sensor activations, together with an annotated record of the actual activities. Results indicate that the methodology is well-suited to such scenarios

Some challenges facing Lean Thinking in healthcare

Copyright @ The Authors 2009. Published by Oxford University Press in association with the International Society for Quality in Health Care. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.No abstract available (Editorial).EPSR

Phase-type survival trees and mixed distribution survival trees for clustering patients' hospital length of stay

Clinical investigators, health professionals and managers are often interested in developing criteria for clustering patients into clinically meaningful groups according to their expected length of stay. In this paper, we propose two novel types of survival trees; phase-type survival trees and mixed distribution survival trees, which extend previous work on exponential survival trees. The trees are used to cluster the patients with respect to length of stay where partitioning is based on covariates such as gender, age at the time of admission and primary diagnosis code. Likelihood ratio tests are used to determine optimal partitions. The approach is illustrated using nationwide data available from the English Hospital Episode Statistics (HES) database on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period.peer-reviewe

Formation Control Algorithms for Multi-UAV Systems with Unstable Topologies and Hybrid Delays

Multi-UAV systems rely on the communication network to exchange mission-critical data for their coordination and deployment, while communication delays could cause significant challenges to both tasks. The impact of the delays becomes even more severe if the delay, network structure and formation are all time-varying, a common challenge faced by real-world multiUAV systems. To address this challenge, we consider time-varying delays that exist in multiple channels caused by transmitting information and internal delays that exist in UAVs themselves caused by obtaining and processing their own data. We design an effective distributed formation control protocol for a multiUAV system to achieve time-varying formation; this protocol is particularly useful for dealing with time-varying multi-UAV network topologies as well. We provide rigorous convergence analysis for different scenarios with or without hybrid delays and obtain sufficient conditions for achieving the time-varying formation. Furthermore, we propose an algorithm for quantifying the maximum delay allowed by the system. Based on the designed formation algorithm, a deployment strategy is proposed to coordinate multi-UAV systems in a practical environment. Numerical analysis and UAV hardware experiments are conducted to evaluate the performance of the theoretical results and investigate the feasibility of generated flight trajectories