Dr. John Bryden was the executive officer of European Federation for Medical Informatics for a decade between 1998 and 2008. When he retired from active work within the federation, he was awarded an honorary fellowship. In one of his early papers from the 1960s, he described how some relatively novel machines called computers might replace the punched cards that were being used at the time. He saw, before many others, that computers could be used for the care of individual patients and even more so for groups of patients. He implemented a unique patient identifier (community health index) which has enabled Scotland to link electronic medical record data for clinical management of chronic disease deterministically. An example was the development of the Glasgow Coma Scale. One benefit of demonstrating significant value in projects such as this at an early stage of record linkage was that the governance framework for the use of data became relatively permissive. Another major success was diabetes care; it became possible to apply insights from the aggregate data to improve services and make them more efficient. Scotland has developed safe havens for data where not only the physical environment but also the people, mechanisms and projects are all subject to control to ensure safety and confidentiality. Similar moves are under way in Europe. TRANSFoRm (www.transformproject.eu) led by King's college in London is mainly focused on primary care data. Excellence in medical informatics is possible as a result of the work of its pioneers, including John Bryden's first paper suggesting that computers might be useful.

. It is supported as part of the BCS charitable mission which includes promoting new thinking, education, sharing knowledge, promoting professional practice and the setting and implementation of standards. This is very much something that an academic journal can support. The journal is broadening its scope and looking to attract papers about how informatics can impact on frontline health and social care – across the breadth of health informatics. Please consider registering with the journal; we accept a wide range of scientific publications from pure research to articles on policy, and short communications or letters about issues of the day.

Increasing investment in eHealth aims to improve cost effectiveness and safety of care. Data extraction and aggregation can create new data products to improve professional practice and provide feedback to improve the quality of source data. A previous systematic review concluded that locally relevant clinical indicators and use of clinical record systems could support clinical governance. We aimed to extend and update the review with a theoretical framework. We searched PubMed, Medline, Web of Science, ABI Inform (Proquest) and Business Source Premier (EBSCO) using the terms curation, information ecosystem, data quality management (DQM), data governance, information governance (IG) and data stewardship. We focused on and analysed the scope of DQM and IG processes, theoretical frameworks, and determinants of the processing, quality assurance, presentation and sharing of data across the enterprise. There are good theoretical reasons for integrated governance, but there is variable alignment of DQM, IG and health system objectives across the health enterprise. Ethical constraints exist that require health information ecosystems to process data in ways that are aligned with improving health and system efficiency and ensuring patient safety. Despite an increasingly 'big-data' environment, DQM and IG in health services are still fragmented across the data production cycle. We extend current work on DQM and IG with a theoretical framework for integrated IG across the data cycle. The dimensions of this theory-based framework would require testing with qualitative and quantitative studies to examine the applicability and utility, along with an evaluation of its impact on data quality across the health enterprise.

To compare the quality of data collection via electronic data capture (EDC) with voiced questionnaire (QNN) and data image capture features using a tablet versus standard paper-based QNN, to assess the user's perception of using the EDC tool, and to compare user satisfaction with the two methods. Randomised cross-over study. Study sites: This study was conducted in two villages along the Thailand-Myanmar border. This study included 30 community health volunteers (CHVs) and 120 Karen hill tribe villagers. Employing a cross-over study design, the CHVs were allocated randomly to two groups, in which they performed interviews in different sequences using EDC and QNN. Data discrepancies were found between the two data-collection methods, when data from the paper-based and image-capture methods were compared, and when conducting skip pattern questions. More than 90% of the CHVs perceived the EDC to be useful and easy to use. Both interviewers and interviewees were more satisfied with the EDC compared with QNN in terms of format, ease of use, and system speed. The EDC can effectively be used as an alternative method to paper-based QNNs for data collection. It produces more accurate data that can be considered evidence-based.

Change management in health care is a complex and time-consuming endeavour, and no less so in implementing technological systems. In deploying a nationwide programme, the personally controlled electronic health record (PCEHR), the Australian Government employed a number of national and local change management programmes. This article describes the processes undertaken and the experiences of introducing the PCEHR into 74 general practices across a specific area of metropolitan Melbourne. An online survey was developed by an independent evaluator and offered to all participating practices. The response rate was 82%. The deployment and testing of the eHealth infrastructure and the roll-out of the PCEHR were deeply supported through face-to-face, locally contextualised support processes. The area Medicare Local (ML), an organisation that provides support services to general practice and allied health in the community, provided support and programme coordination. This support occurred in the environment of a number of other initiatives to improve adoption. The impact and value of this support in the registration and adoption process was explored in an online survey and found to be the key factor in practice engagement and success. ML support was seen as instrumental in improving adoption and was more effective than other activities. This article highlights the role of local support, in this case, MLs, in the effective implementation of eHealth programmes across a range of stakeholder groups, in particular, general practice, and the potential for the lessons learned from the engagement model of such an entity to be more generally applied.

Even though informatics is a term used commonly in healthcare, it can be a confusing and disengaging one. Many definitions exist in the literature, and attempts have been made to develop a clear taxonomy. Despite this, informatics is still a term that lacks clarity in both its scope and the classification of sub-terms that it encompasses. This paper reviews the importance of an agreed taxonomy and explores the challenges of establishing exactly what is meant by health informatics (HI). It reviews what a taxonomy should do, summarises previous attempts at categorising and organising HI and suggests the elements to consider when seeking to develop a system of classification. The paper does not provide all the answers, but it does clarify the questions. By plotting a path towards a taxonomy of HI, it will be possible to enhance understanding and optimise the benefits of embracing technology in clinical practice.

This issue of the journal starts with a leading article about the tangled taxonomies within our discipline.1 In this issue explores this further, looking at how a clear taxonomy for our discipline might improve our understanding of what is and, perhaps more importantly, is not part of health informatics. Taxonomies, the classification or grouping of things, are well developed for living things. Many of the groupings of plants and animals take a phylogenetic perspective, namely they make the assumption that there was evolution from a common ancestry. Darwin was one of the first known to have sketched out a ‘Tree of life’ to illustrate this common ancestry of many life forms.2 We should, as an informatics community, better define the components of what makes up our discipline, as this would help us define and explain what we do. Creating such a taxonomy should not necessarily constrain us. Darwin recognised that some branches of the tree of life might die and fall away:

This study compares general practitioners (GPs) and general practice trainees (GPTs) on the adaptation to the electronic health records (EHRs) and how they perceive its impact on medical consultations. Cross-sectional, descriptive study. The link for an online questionnaire was sent to mainland Portuguese health care centre groups. A total of 147 physicians (100 females and 47 males). GPs had more learning difficulties in using the EHR (P < 0.05), greater difficulty in keeping regular updated records (P < 0.001) and a more noticeable fear of the possibility of occurring prescription errors (P < 0.05), when compared with GPTs. Most GPs (75%) and GPTs (80.4%) are satisfied with how they use the EHR. Most participants have adhered to the EHR, and are satisfied with its use. A negative impact on medical consultations was not observed. However, it is the group of GPs that is less adapted to the electronic system.

Falls in older Australians are an increasingly costly public health issue, driving the development of novel modes of intervention, especially those that rely on computer-driven technologies. The aim of this paper was to gain an understanding of the state of the art of research on smart homes and computer-based monitoring technologies to prevent and detect falls in the community-dwelling elderly. Cochrane, Medline, Embase and Google databases were searched for articles on fall prevention in the elderly using pre-specified search terms. Additional papers were searched for in the reference lists of relevant reviews and by the process of 'snowballing'. Only studies that investigated outcomes related to falling such as fall prevention and detection, change in participants' fear of falling and attitudes towards monitoring technology were included. Nine papers fulfilled the inclusion criteria. The following outcomes were observed: (1) older adults' attitudes towards fall detectors and smart home technology are generally positive; (2) privacy concerns and intrusiveness of technology were perceived as less important to participants than their perception of health needs and (3) unfriendly and age-inappropriate design of the interface may be one of the deciding factors in not using the technology. So far, there is little evidence that using smart home technology may assist in fall prevention or detection, but there are some indications that it may increase older adults' confidence and sense of security, thus possibly enabling aging in place.

Non-attendance at paediatric urology outpatient appointments results in the patient's failure to receive medical care and wastes health care resources. To determine the utility of using routinely collected electronic health record (EHR) data for multi-centre analysis of variables predictive of patient noshows (NS) to identify areas for future intervention. Data were obtained from Children's Hospital Colorado, Rady Children's Hospital San Diego and University of Virginia Hospital paediatric urology practices, which use the Epic® EHR system. Data were extracted for all urology outpatient appointments scheduled from 1 October 2010 to 30 September 2011 using automated electronic data extraction techniques. Data included appointment type; date; provider type and days from scheduling to appointment. All data were de-identified prior to analysis. Predictor variables identified using χ(2) and analysis of variance were modelled using multivariate logistic regression. A total of 2994 NS patients were identified within a population of 28,715, with a mean NS rate of 10.4%. Multivariate logistic regression determined that an appointment with mid-level provider (odds ratio (OR) 1.70 95% CI (1.56, 1.85)) and an increased number of days between scheduling and appointment (15-28 days OR 1.24 (1.09, 1.41); 29+ days OR 1.70 (1.53, 1.89)) were significantly associated with NS appointments. We demonstrated sufficient interoperability among institutions to obtain data rapidly and efficiently for use in 1) interventions; 2) further study and 3) more complex analysis. Demographic and potentially modifiable clinic characteristics were associated with NS to the outpatient clinic. The analysis also demonstrated that available data are dependent on the clinical data collection systems and practices.