Health information literacy and ageing Australians : applications for practice

This practice framework is designed for health practitioners and allied health care workers. The framework provides empirically-based descriptions of ageing Australians’ experiences of health information literacy and suggests how these may provide a foundation for helping ageing Australians enhance their health information literacy. Health information literacy is understood here to be people’s use of relevant information to learn about health.

Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Information accountability and health big data analytics: A consent-based model

With the ever increasing amount of eHealth data available from various eHealth systems and sources, Health Big Data Analytics promises enticing benefits such as enabling the discovery of new treatment options and improved decision making. However, concerns over the privacy of information have hindered the aggregation of this information. To address these concerns, we propose the use of Information Accountability protocols to provide patients with the ability to decide how and when their data can be shared and aggregated for use in big data research. In this paper, we discuss the issues surrounding Health Big Data Analytics and propose a consent-based model to address privacy concerns to aid in achieving the promised benefits of Big Data in eHealth.

Integration of clinical information systems in Saudi Arabian health care contexts: A grounded theory exploration

This research examined the implementation of clinical information system technology in a large Saudi Arabian health care organisation. The research was underpinned by symbolic interactionism and grounded theory methods informed data collection and analysis. Observations, a review of policy documents and 38 interviews with registered nurses produced in-depth data. Analysis generated three abstracted concepts that explained how imported technology increased practice and health care complexity rather than enhance quality patient care. The core category, Disseminating Change, also depicted a hierarchical and patriarchal culture that shaped the implementation process at the levels of government, organisation and the individual.

Attention and memory bias for body image and health related information using an Emotional Stroop task in a non-clinical sample

It has been proposed that body image disturbance is a form of cognitive bias wherein schemas for self-relevant information guide the selective processing of appearancerelated information in the environment. This threatening information receives disproportionately more attention and memory, as measured by an Emotional Stroop and incidental recall task. The aim of this thesis was to expand the literature on cognitive processing biases in non-clinical males and females by incorporating a number of significant methodological refinements. To achieve this aim, three phases of research were conducted. The initial two phases of research provided preliminary data to inform the development of the main study. Phase One was a qualitative exploration of body image concerns amongst males and females recruited through the general community and from a university. Seventeen participants (eight male; nine female) provided information on their body image and what factors they saw as positively and negatively impacting on their self evaluations. The importance of self esteem, mood, health and fitness, and recognition of the social ideal were identified as key themes. These themes were incorporated as psycho-social measures and Stroop word stimuli in subsequent phases of the research. Phase Two involved the selection and testing of stimuli to be used in the Emotional Stroop task. Six experimental categories of words were developed that reflected a broad range of health and body image concerns for males and females. These categories were high and low calorie food words, positive and negative appearance words, negative emotion words, and physical activity words. Phase Three addressed the central aim of the project by examining cognitive biases for body image information in empirically defined sub-groups. A National sample of males (N = 55) and females (N = 144), recruited from the general community and universities, completed an Emotional Stroop task, incidental memory test, and a collection of psycho-social questionnaires. Sub-groups of body image disturbance were sought using a cluster analysis, which identified three sub-groups in males (Normal, Dissatisfied, and Athletic) and four sub-groups in females (Normal, Health Conscious, Dissatisfied, and Symptomatic). No differences were noted between the groups in selective attention, although time taken to colour name the words was associated with some of the psycho-social variables. Memory biases found across the whole sample for negative emotion, low calorie food, and negative appearance words were interpreted as reflecting the current focus on health and stigma against being unattractive. Collectively these results have expanded our understanding of processing biases in the general community by demonstrating that the processing biases are found within non-clinical samples and that not all processing biases are associated with negative functionality

Formative and reflective measurement and validation mismatch in survey research : an archival analysis of information systems success constructs 1985-2007

The generic IS-success constructs first identified by DeLone and McLean (1992) continue to be widely employed in research. Yet, recent work by Petter et al (2007) has cast doubt on the validity of many mainstream constructs employed in IS research over the past 3 decades; critiquing the almost universal conceptualization and validation of these constructs as reflective when in many studies the measures appear to have been implicitly operationalized as formative. Cited examples of proper specification of the Delone and McLean constructs are few, particularly in light of their extensive employment in IS research. This paper introduces a four-stage formative construct development framework: Conceive > Operationalize > Respond > Validate (CORV). Employing the CORV framework in an archival analysis of research published in top outlets 1985-2007, the paper explores the extent of possible problems with past IS research due to potential misspecification of the four application-related success dimensions: Individual-Impact, Organizational-Impact, System-Quality and Information-Quality. Results suggest major concerns where there is a mismatch of the Respond and Validate stages. A general dearth of attention to the Operationalize and Respond stages in methodological writings is also observed.

The utility and challenges of using ICD codes in child maltreatment research : a review of existing literature

Objective: The objectives of this article are to explore the extent to which the International Statistical Classification of Diseases and Related Health Problems (ICD) has been used in child abuse research, to describe how the ICD system has been applied and to assess factors affecting the reliability of ICD coded data in child abuse research.----- Methods: PubMed, CINAHL, PsychInfo and Google Scholar were searched for peer reviewed articles written since 1989 that used ICD as the classification system to identify cases and research child abuse using health databases. Snowballing strategies were also employed by searching the bibliographies of retrieved references to identify relevant associated articles. The papers identified through the search were independently screened by two authors for inclusion, resulting in 47 studies selected for the review. Due to heterogeneity of studies metaanalysis was not performed.----- Results: This paper highlights both utility and limitations of ICD coded data. ICD codes have been widely used to conduct research into child maltreatment in health data systems. The codes appear to be used primarily to determine child maltreatment patterns within identified diagnoses or to identify child maltreatment cases for research.----- Conclusions: A significant impediment to the use of ICD codes in child maltreatment research is the under-ascertainment of child maltreatment by using coded data alone. This is most clearly identified and, to some degree, quantified, in research where data linkage is used. Practice Implications: The importance of improved child maltreatment identification will assist in identifying risk factors and creating programs that can prevent and treat child maltreatment and assist in meeting reporting obligations under the CRC.

Validation of the impact of Health Information Technology (I-HIT) scale : an international collaborative

In 2005, the Healthcare Information Management Systems Society (HIMSS) Nursing Informatics Community developed a survey to measure the impact of health information technology (HIT), the IHIT Scale, on the role of nurses and interdisciplinary communication in hospital settings. In 2007, nursing informatics colleagues from Australia, England, Finland, Ireland, New Zealand, Scotland and the United States formed a research collaborative to validate the IHIT across countries. All teams have completed construct and face validation in their countries. Five out of six teams have initiated reliability testing by practicing nurses. This paper reports the international collaborative’s validation of the IHIT Scale completed to date.

Rethinking health planning : a framework for organising information to underpin collaborative health planning

The field of collaborative health planning faces significant challenges created by the narrow focus of the available information, the absence of a framework to organise that information and the lack of systems to make information accessible and guide decision-making. These challenges have been magnified by the rise of the ‘healthy communities movement’, as a result of which, there have been more frequent calls for localised, collaborative and evidence-driven health related decision-making. This paper discusses the role of decision support systems as a mechanism to facilitate collaborative health decision-making. The paper presents a potential information management framework to underpin a health decision support system and describes the participatory process that is currently being used to create an online tool for health planners using geographic information systems. The need for a comprehensive information management framework to guide the process of planning for healthy communities has been emphasised. The paper also underlines the critical importance of the proposed framework not only in forcing planners to engage with the entire range of health determinants, but also in providing sufficient flexibility to allow exploration of the local setting-based determinants of health.

Collaborative research to develop a specialist, post-graduate competency-based education curriculum and continuing professional development structure for health librarians in Australia

Through a grant received from the Australian Library and Information Association (ALIA), members of Health Libraries Australia (HLA) are collaborating with a researcher/educator to conduct a twelve month research project with the goal of developing an educational framework for the Australian health librarianship workforce of the future. The collaboration comprises the principal researcher and a representative group of practitioners from different sectors of the health industry who are affiliated with ALIA in various committees, advisory groups and roles. The research has two main aims: to determine the future skills requirements for the health librarian workforce in Australia; and to develop a structured, modular education framework for specialist post-graduate qualifications together with a structure for ongoing continuing professional development. The paper highlights some of the major trends in the health sector and some of the main environmental influences that may act as drivers for change for health librarianship as a profession, and particularly for educating the future workforce. The research methodology is outlined and the main results are described; the findings are discussed with regard to their implications for the development of a structured, competency-based education framework.

Semantic search and inferencing in health informatics

Consider a person searching electronic health records, a search for the term ‘cracked skull’ should return documents that contain the term ‘cranium fracture’. A information retrieval systems is required that matches concepts, not just keywords. Further more, determining relevance of a query to a document requires inference – its not simply matching concepts. For example a document containing ‘dialysis machine’ should align with a query for ‘kidney disease’. Collectively we describe this problem as the ‘semantic gap’ – the difference between the raw medical data and the way a human interprets it. This paper presents an approach to semantic search of health records by combining two previous approaches: an ontological approach using the SNOMED CT medical ontology; and a distributional approach using semantic space vector space models. Our approach will be applied to a specific problem in health informatics: the matching of electronic patient records to clinical trials.