Legal issues related to Accountable-eHealth systems in Australia

Information privacy requirements of patients and information requirements of healthcare providers (HCP) are competing concerns. Reaching a balance between these requirements have proven difficult but is crucial for the success of eHealth systems. The traditional approaches to information management have been preventive measures which either allow or deny access to information. We believe that this approach is inappropriate for a domain such as healthcare. We contend that introducing information accountability (IA) to eHealth systems can reach the aforementioned balance without the need for rigid information control. IA is a fairly new concept to computer science, hence; there are no unambiguously accepted principles as yet. But the concept delivers promising advantages to information management in a robust manner. Accountable-eHealth (AeH) systems are eHealth systems which use IA principles as the measure for privacy and information management. AeH systems face three main impediments; technological, social and ethical and legal. In this paper, we present the AeH model and focus on the legal aspects of AeH systems in Australia. We investigate current legislation available in Australia regarding health information management and identify future legal requirements if AeH systems are to be implemented in Australia.

Access to Cardiac Rehabilitation does not Equate to Attendance. (On Behalf of the Cardiac ARIA Project. Winner Nursing/Allied Health Professional Investigator Award)

Background/Aims Timely access to appropriate cardiac care is critical for optimizing positive outcomes after a cardiac event. Attendance at cardiac rehabilitation (CR) remains less than optimal (10%–30%). Our aim was to derive an objective, comparable, geographic measure reflecting access to cardiac services after a cardiac event in Australia. Methods An expert panel defined a single patient care pathway and a hierarchy of the minimum health services for CR and secondary prevention. Using geographic information systems a numeric/alpha index was modelled to describe access before and after a cardiac event. The aftercare phase was modelled into five alphabetical categories: from category A (access to medical service, pharmacy, CR, pathology within 1 h) to category E (no services available within 1 h). Results Approximately 96% or 19 million people lived within 1 h of the four basic services to support CR and secondary prevention, including 96% of older Australians and 75% of the indigenous population. Conversely, 14% (64,000) indigenous people resided in population locations that had poor access to health services that support CR after a cardiac event. Conclusion Results demonstrated that the majority of Australians had excellent ‘geographic’ access to services to support CR and secondary prevention. Therefore, it appears that it is not the distance to services that affects attendance. Our ‘geographic’ lens has identified that more research on socioeconomic, sociological or psychological aspects to attendance is needed.

Transforming mobile personal life log into autobiographical multimedia eChronicles

Mobile devices are becoming indispensable personal assistants in people's daily life as these devices support work, study, play and socializing activities. The multi-modal sensors and rich features of smartphones can capture abundant information about users' life experience, such as taking photos or videos on what they see and hear, and organizing their tasks and activities using calendar, to-do lists, and notes. Such vast information can become useful to help users recalling episodic memories and reminisce about meaningful experiences. In this paper, we propose to apply autobiographical memory framework to provide an effective mechanism to structure mobile life-log data. The proposed model is an attempt towards a more complete personal life-log indexing model, which will support long term capture, organization, and retrieval. To demonstrate the benefits of the proposed model, we propose some design solutions for enabling users-driven capture, annotation, and retrieval of autobiographical multimedia chronicles tools.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

A viable and sustainable key management approach for a national e-health environment

To protect the health information security, cryptography plays an important role to establish confidentiality, authentication, integrity and non-repudiation. Keys used for encryption/decryption and digital signing must be managed in a safe, secure, effective and efficient fashion. The certificate-based Public Key Infrastructure (PKI) scheme may seem to be a common way to support information security; however, so far, there is still a lack of successful large-scale certificate-based PKI deployment in the world. In addressing the limitations of the certificate-based PKI scheme, this paper proposes a non-certificate-based key management scheme for a national e-health implementation. The proposed scheme eliminates certificate management and complex certificate validation procedures while still maintaining security. It is also believed that this study will create a new dimension to the provision of security for the protection of health information in a national e-health environment.

Designing an information accountability framework for eHealth

Information privacy is a crucial aspect of eHealth. Appropriate privacy management measures are therefore essential for its success. However, traditional measures for privacy preservation such as rigid access controls (i.e., preventive measures) are not suitable to eHealth because of the specialised and information - intensive nature of healthcare itself, and the nature of the information. Healthcare professionals (HCP) require easy, unrestricted access to as much information as possible towards making well - informed decisions. On the other end of the scale however, consumers (i.e., patients) demand control over their health information and raise concerns for privacy arising from internal activities (i.e., information use by HCPs). A proper balance of these competing concerns is vital for the implementation of successful eHealth systems. Towards reaching this balance, we propose an information accountability framework (IAF) for eHealth systems.

‘The health-location connection’: using GIS and social media on smartphones to track engagement with the Local urban environment for work, recreation, and physical activity

A technologically innovative study was undertaken across two suburbs in Brisbane, Australia, to assess socioeconomic differences in women's use of the local environment for work, recreation, and physical activity. Mothers from high and low socioeconomic suburbs were instructed to continue with usual daily routines, and to use mobile phone applications (Facebook Places, Twitter, and Foursquare) on their mobile phones to ‘check-in’ at each location and destination they reached during a one-week period. These smartphone applications are able to track travel logistics via built-in geographical information systems (GIS), which record participants’ points of latitude and longitude at each destination they reach. Location data were downloaded to Google Earth and excel for analysis. Women provided additional qualitative data via text regarding the reasons and social contexts of their travel. We analysed 2183 ‘check-ins’ for 54 women in this pilot study to gain quantitative, qualitative, and spatial data on human-environment interactions. Data was gathered on distances travelled, mode of transport, reason for travel, social context of travel, and categorised in terms of physical activity type – walking, running, sports, gym, cycling, or playing in the park. We found that the women in both suburbs had similar daily routines with the exception of physical activity. We identified 15% of ‘check-ins’ in the lower socioeconomic group as qualifying for the physical activity category, compared with 23% in the higher socioeconomic group. This was explained by more daily walking for transport (1.7kms to 0.2kms) and less car travel each week (28.km to 48.4kms) in the higher socioeconomic suburb. We ascertained insights regarding the socio-cultural influences on these differences via additional qualitative data. We discuss the benefits and limitations of using new technologies and Google Earth with implications for informing future physical and social aspects of urban design, and health promotion in socioeconomically diverse cities.

Personal processes interface mock up

Situated on Youtube, and shown in various locations. In this video we show a 3D mock up of a personal house purchasing process. A path traversal metaphor is used to give a sense of progression along the process stages. The intention is to be able to use console devices like an Xbox to consume business processes. This is so businesses can expose their internal processes to consumers using sophisticated user interfaces. The demonstrator was developed using Microsoft XNA, with assistance from the Suncorp Bank and the Smart Services CRC. More information at: www.bpmve.org

Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management [Volume 120]

We are pleased to present the papers from the Australasian Health Informatics and Knowledge Management (HIKM) conference stream held on 20 January 2011 in Perth as a session of the Australasian Computer Science Week (ASCW) 2011. Formerly HIKM was named Health Data and Knowledge Management, however the inclusion of the health informatics term is timely given the current health reform. The submissions to HIKM 2011 demonstrated that Australasian researchers lead with many research and development innovations coming to fruition. Some of these innovations can be seen here, and we believe further recognition will accomplish by continuation to HIKM in the future. The HIKM conference is a review of health informatics related research, development and education opportunities. The conference papers were written to communicate with other researchers and share research findings, capturing each and every aspect of the health informatics field. They are namely: conceptual models and architectures, privacy and quality of health data, health workflow management patient journey analysis, health information retrieval, analysis and visualisation, data integration/linking, systems for integrated or coordinated care, electronic health records (EHRs) and personally controlled electronic health records (PCEHRs), health data ontologies, and standardisation in health data and clinical applications.

Information accountability and usability : are there any connections?

Availability of health information is rapidly increasing and the expansion and proliferation of health information is inevitable. The Electronic Healthcare Record, Electronic Medical Record and Personal Health Record are at the core of this trend and are required for appropriate and practicable exchange and sharing of health information. However, it is becoming increasingly recognized that it is essential to preserve patient privacy and information security when utilising sensitive information for clinical, management and administrative processes. Furthermore, the usability of emerging healthcare applications is also becoming a growing concern. This paper proposes a novel approach for integrating consideration of information accountability with a perspective from usability engineering that can be applied when developing healthcare information technology applications. A social networking user case in the healthcare information exchange will be presented in the context of our approach.

Reorganizing a technical services division using collaborative evidence based information practice at Auraria Library

The article focuses on the evidence-based information practice (EBIP) applied at the Auraria Library in Denver, Colorado during the reorganization of its technical services division. Collaboration processes were established for the technical services division through the reorganization and redefinition of workflows. There are several factors that form part of the redefinition of roles including personal interests, department needs, and library needs. A collaborative EBIP environment was created in the division by addressing issues of workplace hierarchies, by the distribution of problem solving, and by the encouragement of reflective dialogue.

'Information in context' : co-designing workplace structures and systems for organizational learning

With the aim of advancing professional practice through better understanding how to create workplace contexts that cultivate individual and collective learning through situated 'information in context' experiences, this paper presents insights gained from three North American collaborative design (co-design) implementations. In the current project at the Auraria Library in Denver, Colorado, USA, participants use collaborative information practices to redesign face-to-face and technology-enabled communication, decision making, and planning systems. Design processes are described and results-to-date described, within an appreciative framework which values information sharing and enables knowledge creation through shared leadership.

Systems thinking : an approach for advancing workplace information literacy

As the importance of information literacy has gained increased recognition, so too have academic library professionals intensified their efforts to champion, activate, and advance these capabilities in others. To date, however, little attention has focused on advancing these essential competencies amongst practitioner advocates.This paper helps redress the paucity of professional literature on the topic of workplace information literacy among library professionals.