Efficient algorithms for real-time syndromic surveillance.

Objective: Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks.

Syndromic surveillance to detect disease outbreaks using time between emergency department presentations.

Objective: Early warning of disease outbreaks is paramount for health jurisdictions. The objective of the present study was to develop syndromic surveillance monitoring plans from routinely collected ED data with application to detecting disease outbreaks.

Methods: The study involved secondary data analysis of ED presentations to major public hospitals in Queensland and South Australia spanning 2017-2020.

Characteristics of patients with COVID-19 hospitalised in South Australia during the first wave of the pandemic.

Objective: To describe the first wave of hospitalisations of patients testing positive for COVID-19 in South Australia.

Methods: Pathology test results for COVID-19 between January and June 2020 were matched against state-wide ED and inpatient data sets.

Results: The impact of the first wave of COVID-19 on South Australian hospitals was 440 unique patients with COVID-19; median ED, hospital and ICU lengths of stay of 4.

Automated monitoring of tweets for early detection of the 2014 Ebola epidemic.

First reported in March 2014, an Ebola epidemic impacted West Africa, most notably Liberia, Guinea and Sierra Leone. We demonstrate the value of social media for automated surveillance of infectious diseases such as the West Africa Ebola epidemic. We experiment with two variations of an existing surveillance architecture: the first aggregates tweets related to different symptoms together, while the second considers tweets about each symptom separately and then aggregates the set of alerts generated by the architecture.

Harnessing Tweets for Early Detection of an Acute Disease Event.

Background: Melbourne, Australia, witnessed a thunderstorm asthma outbreak on 21 November 2016, resulting in over 8,000 hospital admissions by 6 P.M. This is a typical acute disease event.

Impact of At-Home Telemonitoring on Health Services Expenditure and Hospital Admissions in Patients With Chronic Conditions: Before and After Control Intervention Analysis.

Background: Telemonitoring is becoming increasingly important for the management of patients with chronic conditions, especially in countries with large distances such as Australia. However, despite large national investments in health information technology, little policy work has been undertaken in Australia in deploying telehealth in the home as a solution to the increasing demands and costs of managing chronic disease.

Objective: The objective of this trial was to evaluate the impact of introducing at-home telemonitoring to patients living with chronic conditions on health care expenditure, number of admissions to hospital, and length of stay (LOS).

Modelling hospital length of stay using convolutive mixtures distributions.

Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness in the distribution of LOS poses problems in statistical modelling because it fails to adequately follow the usual traditional distribution of positive variables such as the log-normal distribution. We present in this paper a model using the convolution of two distributions, a technique well known in the signal processing community.

Model based methods for the analysis of non-stationary effects of telemonitoring as an intervention for the management of chronic conditions at home.

The monitoring of vital signs for the management of chronic conditions at home have been demonstrated in numerous trials to have a positive impact on the patient's healthcare outcomes as well as to reduce hospitalization and improve quality of life. The CSIRO has just completed a two year clinical trial designed to evaluate a large number of qualitative and quantitative outcomes of at home telemonitoring. As preliminary data demonstrates that before and after data is not stationary, a model based BACI (Before-After-Control-Impact) design frequently used in environmental and agricultural yield studies, but rarely in clinical trials, has been developed to model the effects of the intervention on healthcare outcomes over time as well as possible secondary effects associate with environmental and seasonal conditions.

Design of a multi-site multi-state clinical trial of home monitoring of chronic disease in the community in Australia.

Background: Telehealth services based on at-home monitoring of vital signs and the administration of clinical questionnaires are being increasingly used to manage chronic disease in the community, but few statistically robust studies are available in Australia to evaluate a wide range of health and socio-economic outcomes. The objectives of this study are to use robust statistical methods to research the impact of at home telemonitoring on health care outcomes, acceptability of telemonitoring to patients, carers and clinicians and to identify workplace cultural factors and capacity for organisational change management that will impact on large scale national deployment of telehealth services. Additionally, to develop advanced modelling and data analytics tools to risk stratify patients on a daily basis to automatically identify exacerbations of their chronic conditions.

Home telemonitoring of vital signs--technical challenges and future directions.

The telemonitoring of vital signs from the home is an essential element of telehealth services for the management of patients with chronic conditions, such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), diabetes, or poorly controlled hypertension. Telehealth is now being deployed widely in both rural and urban settings, and in this paper, we discuss the contribution made by biomedical instrumentation, user interfaces, and automated risk stratification algorithms in developing a clinical diagnostic quality longitudinal health record at home. We identify technical challenges in the acquisition of high-quality biometric signals from unsupervised patients at home, identify new technical solutions and user interfaces, and propose new measurement modalities and signal processing techniques for increasing the quality and value of vital signs monitoring at home.

Detecting and diagnosing hotspots for the enhanced management of hospital Emergency Departments in Queensland, Australia.

Background: Predictive tools are already being implemented to assist in Emergency Department bed management by forecasting the expected total volume of patients. Yet these tools are unable to detect and diagnose when estimates fall short. Early detection of hotspots, that is subpopulations of patients presenting in unusually high numbers, would help authorities to manage limited health resources and communicate effectively about emerging risks.

Impact of influenza across 27 public emergency departments in Australia: a 5-year descriptive study.

Objective: To describe the incidence, characteristics and outcomes of patients with influenza-like symptoms presenting to 27 public hospital emergency departments (EDs) in Queensland, Australia.

Methods: A descriptive retrospective study covering 5 years (2005-9) of historical data from 27 hospital EDs was undertaken. State-wide hospital ED Information System data were analysed.

Prediction and surveillance of influenza epidemics.

Objective: To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza.

Methods: We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy.

Remote access methods for exploratory data analysis and statistical modelling: Privacy-Preserving Analytics.

This paper is concerned with the challenge of enabling the use of confidential or private data for research and policy analysis, while protecting confidentiality and privacy by reducing the risk of disclosure of sensitive information. Traditional solutions to the problem of reducing disclosure risk include releasing de-identified data and modifying data before release. In this paper we discuss the alternative approach of using a remote analysis server which does not enable any data release, but instead is designed to deliver useful results of user-specified statistical analyses with a low risk of disclosure.