Research enabling the e-health revolution

The CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Queensland Government have jointly established the Australian e-Health Research Centre (AEHRC) with the aim of developing innovative information and communication technologies (ICT) for a sustainable health care system. The AEHRC, as part of the CSIRO ICT Centre, has access to new technologies in information processing, wireless and networking technologies, and autonomous systems. The AEHRC’s 50 researchers, software engineers and PhD students, in partnership with the CSIRO and clinicians, are developing and applying new technologies for improving patients’ experience, building a more rewarding workplace for the health workforce, and improving the efficiency of delivering health care. The capabilities of the AEHRC fall into four broad areas: smart methods for using medical data; advanced medical imaging technologies; new models for clinical and health care interventions; and tools for medical skills development. Since its founding in 2004, new technology from the AEHRC has been adopted within Queensland (eg, a mobile phone-based cardiac rehabilitation program), around Australia (eg, medical imaging technologies) and internationally (eg, our clinical terminology tools).

David P Hansen BSc · Phil Gurney BSc, PhD · Gary Morgan MBA · Bruce Barraclough FRACS, FACS, DDU

Emergency departments around Australia use a range of software to capture data on patients’ reason for encounter, presenting problem and diagnosis. The data collected are mainly based on descriptions and codes of the International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM), with each emergency department having a tailored list of terms. The National E-Health Transition Authority is introducing a standard clinical terminology, the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), as one of the building blocks of an e-health infrastructure in Australia. The Australian e-Health Research Centre has developed a software platform, Snapper, which facilitates mapping of existing clinical terms to the SNOMED CT terminology. Using the Snapper software, reference sets of terms for emergency departments are being developed, based on the Australian version of SNOMED CT (SNOMED CT-AU). Existing software systems need to be able to implement these reference sets to support standardised recording of data at the point of care. As the terms collected will be part of a larger terminology, they will be useful for patients’ admission and discharge summaries and for computerised clinical decision making. Mapping existing sets of clinical terms to a national emergency department SNOMED CT reference set will facilitate consistency between emergency department data collections and improve the usefulness of the data for clinical and analytical purposes.

David P Hansen BSc, PhD · Madonna L Kemp MHInf, GradDipHAdminIS(Mgt), BSc(HIM) · Sandra R Mills BHlthSci, GradCertHInf · Megan A Mercer BHSc · Paul A Frosdick BPharm, MBA · Michael J Lawley PhD

Greg has had a lifetime of interacting with the health system with both acute injuries and chronic diseases. For the past 4 years, as Secretary to the Board of the Australian e-Health Research Centre, he has been hearing about how e-health will improve his experience of dealing with health problems. This personal perspective suggests that patients will only consider an e-health system a success if it supports them in their treatment options and empowers them to look after themselves — Supplement Editors The term “wellbeing” annoys me. “Being” well or decrepit is not living. Wellbeing is a health-focused concept and one’s personal health, important as it is, and however widely it is defined, is an enabler, not a reason for living. Many times I’ve had people say to me “your health is your greatest personal asset”. Nope, self-esteem, a sense of purpose, a referential set of values, and a sense of belonging, supported by constructive relationships — these are our most important assets. We cope well with dodgy health when those basics are there. There’s no doubt that a robustly positive attitude is a valuable asset in the sorts of circumstances I’ve faced. However, it’s important to keep it in perspective. If asked to allocate percentages as to why, so far, I’ve always ended up in the happy tail of the Gaussian curve, I’d say good treatment accounts for 15%, good attitude for 5%, and good luck for 80%. But I also know that Fortuna is a capricious goddess, and that life is not priceless. Prevention, while not always possible, is the key — prevention, in which individuals and their communities take the lead role and are given the tools to do so. But how? The constructive curmudgeonIn 1969–1970, the renal transplant unit at Princess Alexandra Hospital, Brisbane, had an admirable approach — “patient heal thyself”. Self-reliance was the key, right down to sometimes taking one’s own blood pressure while in hospital for dialysis. I was encouraged to be independent of mind, and that was 40 years ago. When I emerged from transplant surgery, I immediately took the view that I wasn’t going to live my life focusing on my one notable medical oddity. So, no transplant societies, no transplant games, no involvement in the ongoing life of the transplant unit or its heirs and successors. I do make an exception for the occasional PR appearance because I know the benefits that accrue from the program. And this happy old rationalist would also admit to himself, as I’m doing now to you, that there was, and is, an underlying superstition that I might cruel my good luck by talking about it or parading it too much. I made another decision . . . that I wasn’t going to live my life constantly explaining myself to people. I’d had quite enough of that during dialysis, fending off offended suburban hosts and hostesses when I wouldn’t — couldn’t — eat or drink anything they offered me, to the point where I decided not to go out anywhere. You can easily start to live your life through your medical condition — that is the path of least resistance, because everyone wants to talk about it. The ready and increasing availability of web-based information can feed that interest, as can blanket use of particular interventions. An example is counselling, but with my lengthy experience at the other end of the scalpel, my domestic support team already know how to get me through these things. One type of successful mindsetEven in this relatively enlightened day and age, I’m occasionally given to fuming about the approach taken by some frontline health staff. Why do some nursing staff still treat patients like children, including using baby talk? Why do some medical staff still treat patients as if they have an intellectual disability? Why do some clinicians get personally offended when I decide to take a risk and ignore some of their advice? Why are these clinicians utterly uninterested in any rationale that I try to offer by way of explanation? My modus operandi when confronted with an acute or chronic health issue is: Get the best acute care possible. Invest in recovery (time, resources, whatever); it’s the most effective way of maximising all positive outcomes. Learn about the full range of recommended long-term care (eg, regular check-ups, medication, exercise, physical or dietary issues, etc). Learn about the mechanics and chemistry of what has happened and will happen, but only to the extent of a general understanding sufficient to make a judgement about when you should pull the “doctor” switch, and when you can take a reasonable risk in riding something out. Make a decision on the minimum necessary ongoing care and build it into the background of your life (like cleaning your teeth), wear any downside consequences, annoyances and diversions, and get on with your real life. In other words, take a generic, balanced, risk-mitigation approach. Empower meI’ve never consciously felt a particular need to be “empowered” by anyone, but I suppose some might view my modus operandi as evidence of self-empowerment. Empowered and all, I still rely heavily on the availability of high-quality expertise and facilities. Empowerment sounds great, but it is a debased PR term these days. Non-trivial problems usually require simple strategies and complex solutions. I’m comforted by the fact that a national electronic health record is looming, that the prevention agenda has the profile that it does, and that science that supports productivity is gaining more traction. The world’s not such a bad place after all.

Greg McCallum

A decline in cognition greater than expected with ageing and accompanied by subjective cognitive concerns or functional changes may be indicative of a dementing disorder. The capacity to correctly identify cognitive decline relies on comparisons with normative data from a suitably matched healthy reference group with relatively homogeneous demographic features. Formal assessment of cognition is usually performed by specialist neuropsychologists trained in administration and interpretation of psychometric tests. With a scarcity of normative data from large cohorts of older adults, Australian neuropsychologists commonly use representative data from small international studies. Data from 727 healthy older Australians participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing have been used to create a normative dataset. A web-based calculator was developed to simplify the time-consuming process of comparing cognitive performance scores with these representative data.

and the AIBL Research Group

The prevalence of cardiovascular disease, a major cause of disease burden in Australia and other developed countries, is increasing due to a rapidly ageing population and environmental, biomedical and modifiable lifestyle factors. Although cardiac rehabilitation (CR) programs have been shown to be beneficial and effective, rates of referral, uptake and utilisation of traditional hospital or community centre programs are poor. Home-based CR programs have been shown to be as effective as centre-based programs, and recent advances in information and communication technologies (ICT) can be used to enhance the delivery of such programs. The Care Assessment Platform (CAP) is an integrated home-based CR model incorporating ICT (including a mobile phone and the internet) and providing all the core components of traditional CR (education, physical activity, exercise training, behaviour modification strategies and psychological counselling). The mobile phone given to patients has an integrated accelerometer and diary application for recording exercise and health information. A central database, with access to these data, allows mentors to assess patients’ progress, assist in setting goals, revise targets and give weekly personal feedback. Mentors find the mobile-phone modalities practical and easy to use, and preliminary results show high usage rates and acceptance of ICT by participants. The provision of ICT-supported home-based CR programs may enable more patients in both metropolitan and remote settings to benefit from CR.

Marlien Varnfield MSc · Mohanraj K Karunanithi BEng, MBiomedEng · Antti Särelä MSc · Elsa Garcia MEng · Anita Fairfull BSpPath · Brian F Oldenburg BSc(Hons), MPsychol, PhD · Darren L Walters MB BS, MPhil, FRACP

Longer life expectancies lead to increases in the prevalence of age-associated illnesses. The number of Australians with dementia is predicted to rise, from 234 000 in 2009 to over 1 million by 2050, as a result of the increased prevalence of Alzheimer’s disease (AD), the leading cause of dementia in the elderly. Early diagnosis of AD will become more important as disease-modifying therapies emerge within the next decade. Advances in molecular neuroimaging with amyloid-β-specific radioligands for positron emission tomography, aided by magnetic resonance imaging techniques, allow detection of AD years before symptoms of dementia develop. Longitudinal prospective studies, such as the Australian Imaging Biomarkers and Lifestyle (AIBL) study of ageing, will determine the sensitivity and specificity of these analysis techniques for diagnosing AD and predicting cognitive decline.

Kathryn A Ellis BAppSc(Hons), PhD · Christopher C Rowe MB BS, FRACP, MD · Cassandra E I Szoeke MB BS, PhD, FRACP · Victor L Villemagne MD · David Ames MD, FRCPsych, FRANZCP · Gaël Chételat PhD · Ralph N Martins BSc(Hons), PhD · Colin L Masters MD, FRCPath, HonDLitt · Jurgen Fripp PhD · Oscar Acosta PhD · Parnesh Raniga BSc(Hons) · Pierrick T Bourgeat PhD · Olivier Salvado PhD

Dose planning for prostate radiation therapy is performed using computed tomography (CT) scans that provide the electron density information needed for individual patients’ radiation dose calculations. For visualising the prostate and determining the target volume for radiation treatment, magnetic resonance imaging (MRI) gives vastly superior soft-tissue contrast. However, currently, MRI scans cannot be used for dose planning, as they do not provide the electron density information. We aimed to develop an alternative and efficient MRI-only image-based workflow, enabling both organ delineation and dose planning to be performed using MRI, with “pseudo-CT scans” generated from MRI scans supplying the information for dose planning. The feasibility of implementing MRI-based prostate radiation therapy planning is being investigated through collaboration between the clinical and medical physics group at the Calvary Mater Newcastle Hospital/University of Newcastle and the biomedical imaging processing group at the CSIRO (Commonwealth Scientific and Industrial Research Organisation) Australian e-Health Research Centre. Results comparing Hounsfield units calculated from CT scans and from MRI-based pseudo-CT scans for 39 patients showed very similar average values for the prostate, bladder, bones and rectum, confirming that pseudo-CT scans can replace CT scans for accurate radiation dose calculations. MRI-based radiotherapy planning can also be used for tumours in other locations, such as head and neck, and breast cancers.

Peter B Greer PhD · Jason A Dowling PhD · Jonathon A Lambert BSc(Hons) · Jurgen Fripp PhD · Joel Parker BAppSc(MedRadScRadTherap) · James W Denham MD, FRCR, FRANZCR · Chris Wratten MB BS, FRANZCR · Anne Capp FRANZCR, FRCR, MMed(ClinEpi) · Olivier Salvado PhD

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. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population.Results: All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available.Conclusion: This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.

Justin R Boyle BEng(Hons), PhD · Ross S Sparks MSc, PhD · Gerben B Keijzers MB BS, MSc(ClinEpi), FACEM · Julia L Crilly MN(Hons), PhD · James F Lind BS BM, BMedSci, FACEM · Louise M Ryan MA, PhD

Objective: To assess the accuracy of data linkage across the spectrum of emergency care in the absence of a unique patient identifier, and to use the linked data to examine service delivery outcomes in an emergency department (ED) setting.Design: Automated data linkage and manual data linkage were compared to determine their relative accuracy. Data were extracted from three separate health information systems: ambulance, ED and hospital inpatients, then linked to provide information about the emergency journey of each patient. The linking was done manually through physical review of records and automatically using a data linking tool (Health Data Integration) developed by the CSIRO (Commonwealth Scientific and Industrial Research Organisation). Match rate and quality of the linking were compared.Setting: 10 835 patient presentations to a large, regional teaching hospital ED over a 2-month period (August – September 2007).Results: Comparison of the manual and automated linkage outcomes for each pair of linked datasets demonstrated a sensitivity of between 95% and 99%; a specificity of between 75% and 99%; and a positive predictive value of between 88% and 95%.Conclusions: Our results indicate that automated linking provides a sound basis for health service analysis, even in the absence of a unique patient identifier. The use of an automated linking tool yields accurate data suitable for planning and service delivery purposes and enables the data to be linked regularly to examine service delivery outcomes.

Julia L Crilly BNurs, MN(Hons), PhD · John A O’Dwyer BCompScInfEng(Hons) · Marilla A O’Dwyer BEng(Hons) · James F Lind BS BM, BMedSci, FACEM · Julia A L Peters MB BS · Vivienne C Tippett BA, GradDipPsych, MPH(BiostatEpi) · Marianne C Wallis RN, BSc(Hons), PhD · Nerolie F Bost BNurs, MN · Gerben B Keijzers MB BS, MClinEpi, FACEM

There is increasing evidence that educating trainee surgeons by simulation is preferable to traditional operating-room training methods with actual patients. Apart from reducing costs and risks to patients, training by simulation can provide some unique benefits, such as greater control over the training procedure and more easily defined metrics for assessing proficiency. Virtual reality (VR) simulators are now playing an increasing role in surgical training. However, currently available VR simulators lack the fidelity to teach trainees past the novice-to-intermediate skills level. Recent technological developments in other industries using simulation, such as the games and entertainment and aviation industries, suggest that the next generation of VR simulators should be suitable for training, maintenance and certification of advanced surgical skills. To be effective as an advanced surgical training and assessment tool, VR simulation needs to provide adequate and relevant levels of physical realism, case complexity and performance assessment. Proper validation of VR simulators and an increased appreciation of their value by the medical profession are crucial for them to be accepted into surgical training curricula.

Hans de Visser MSc, PhD · Marcus O Watson GradDipCS, MSc, PhD · Olivier Salvado PhD · Joshua D Passenger BSc

The Australian e-Health Research Centre,1 in collaboration with the Queensland University of Technology’s Paediatric Spine Research Group,2 is developing software for visualisation and manipulation of large, three-dimensional (3D) medical image datasets. The software allows the extraction of anatomical data from individual patients for use in preoperative planning. State-of-the-art computer technology makes it possible to slice through the image dataset at any angle, or manipulate 3D representations of the data instantly. Although the software was initially developed to support planning for scoliosis surgery, it can be applied to any dataset whether obtained from computed tomography, magnetic resonance imaging or any other imaging modality. Extraction of anatomical data from three-dimensional image datasets Screenshot of the software visualising a three-dimensional (3D) computed tomography dataset from a patient with scoliosis. The left window shows coronal, sagittal and transverse cross-sections of the dataset, as well as an arbitrary transverse cross-section defined by the green lines on the coronal and sagittal cross-sections. The right window shows a 3D reconstruction of the skeletal information derived from the dataset.

Hans de Visser MSc, PhD · Clayton J Adam PhD · Olivier Salvado PhD · Joshua D Passenger BSc

Objective: To demonstrate a method of describing and analysing the interactions between people engaged in decision making in health care network meetings.Method: Analysis of the meeting interaction involved three steps: recording the meeting, annotating the verbal interactions of the meeting, and calculating various metrics from the annotations. Each annotation represented one utterance by one person. Annotations were assigned start and end times and an associated behaviour from four mutually exclusive behaviour categories. We used software and algorithms developed for this process at the Australian e-Health Research Centre.Results: A meeting interaction fingerprint was produced from one meeting, which consisted of a set of metrics describing different aspects of the interaction between participants in the meeting.Conclusions: Creating meeting fingerprints and analysing the interactions between meeting participants has the potential to provide feedback to improve decision making in health care network teams.

Hazel E Harden BSc(Dietetics) · Simon E Locke PhD