An Investigation into Healthcare-Data Patterns

Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%

Temporal variability and social heterogeneity in disease transmission: The case of SARS in Hong Kong

The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (±0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings. © 2009 Cori et al.published_or_final_versio

Understanding unconventional medicine

The phenomenon of unconventional medicine is an important feature of any contemporary society. Considering the increasing popularity of various forms of non-biomedical methods of healing among various groups of people, the necessity of an in-depth investigation of traditional, complementary and alternative therapies continues to grow. Existing terminology along with prevalence rates, legal status and historical development, vary greatly in European countries. The main reason behind the compilation of this publication was, therefore, to provide an overview of the field of unconventional medicine in Slovakia, where social science research into medicine has largely been neglected and only limited data exist in relation to medical practices and products, not associated with standard healthcare. Despite various concerns and controversies that have been raised regarding alternative medicine, the intent of the book is not to provoke criticism, the representatives of which are sufficiently represented in the public debate. Likewise, it does not try to reinforce the idealized and non-critical image of unconventional therapies, adopted by a number of alternative healthcare practitioners and providers. Rather than contribute to the polarization of the topic, the purpose of this monography is to provide a comprehensive understanding of the historical background of unconventional therapies, the main trends in this area, the patterns and reasons for the use of alternative medicine and the factors determining the efficacy of alternative therapies. The first section briefly introduces the historical development of the most notable forms of unconventional medicine in Slovakia, while highlighting various institutionalization and professionalization strategies, that have dominated over the last few decades. The second section mainly concerns a representative survey, investigating the patterns and trends of unconventional medicine use and concentrates on the execution of data on prevalence and types of non-conventional medicine, examining attitudes towards different topics related to alternative healthcare. Lastly, the book briefly enters the ongoing discussion as to how unconventional medicine might work and how efficacy is negotiated between the different actors involved in the healing process

Using discrete event simulation (DES) to manage theatre operations in healthcare: An audit-based case study

This paper discusses the application of Discrete Event Simulation (DES) in modelling the complex relationship between patient types, case-mix and operating theatre allocation in a large National Health Service (NHS) Trust in London. The simulation model that was constructed described the main features of nine theatres, focusing on operational processes and patient throughput times. The model was used to test three scenarios of case-mix and to demonstrate the potential of using simulation modelling as a cost effective method for understanding the issues of healthcare operations management and the role of simulation techniques in problem solving. The results indicated that removing all day cases will reduce patient throughput by 23.3% and the utilization of the orthopaedic theatre in particular by 6.5%. This represents a case example of how DES can be used by healthcare managers to inform decision making

Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Health services research in the public healthcare system in Hong Kong: An analysis of over 1 million antihypertensive prescriptions between 2004-2007 as an example of the potential and pitfalls of using routinely collected electronic patient data

<b>Objectives</b> Increasing use is being made of routinely collected electronic patient data in health services research. The aim of the present study was to evaluate the potential usefulness of a comprehensive database used routinely in the public healthcare system in Hong Kong, using antihypertensive drug prescriptions in primary care as an example.<p></p> <b>Methods</b> Data on antihypertensive drug prescriptions were retrieved from the electronic Clinical Management System (e-CMS) of all primary care clinics run by the Health Authority (HA) in the New Territory East (NTE) cluster of Hong Kong between January 2004 and June 2007. Information was also retrieved on patients’ demographic and socioeconomic characteristics, visit type (new or follow-up), and relevant diseases (International Classification of Primary Care, ICPC codes). <p></p> <b>Results</b> 1,096,282 visit episodes were accessed, representing 93,450 patients. Patients’ demographic and socio-economic details were recorded in all cases. Prescription details for anti-hypertensive drugs were missing in only 18 patients (0.02%). However, ICPC-code was missing for 36,409 patients (39%). Significant independent predictors of whether disease codes were applied included patient age > 70 years (OR 2.18), female gender (OR 1.20), district of residence (range of ORs in more rural districts; 0.32-0.41), type of clinic (OR in Family Medicine Specialist Clinics; 1.45) and type of visit (OR follow-up visit; 2.39). <p></p> In the 57,041 patients with an ICPC-code, uncomplicated hypertension (ICPC K86) was recorded in 45,859 patients (82.1%). The characteristics of these patients were very similar to those of the non-coded group, suggesting that most non-coded patients on antihypertensive drugs are likely to have uncomplicated hypertension. <p></p> <b>Conclusion</b> The e-CMS database of the HA in Hong Kong varies in quality in terms of recorded information. Potential future health services research using demographic and prescription information is highly feasible but for disease-specific research dependant on ICPC codes some caution is warranted. In the case of uncomplicated hypertension, future research on pharmaco-epidemiology (such as prescription patterns) and clinical issues (such as side-effects of medications on metabolic parameters) seems feasible given the large size of the data set and the comparability of coded and non-coded patients