We describe a prototype open source UK-scalable Health Information Exchange (HIE) to support patient-centric care, translational research and other secondary uses. It has been designed by interoperability experts to standardise information flows across the patient path—for example from home, work, mobile, clinical and community care locations. Sintero enables secure information sharing within the patient’s named ‘circle of care’ including family, third sector, social care, community pharmacy, specialist care networks and named statutory (e.g. NHS) carers. Patients may access to their own personal health records and manage consents for their sharing and secondary uses. Sintero’s infrastructure is a low-cost ‘commodity’ approach to delivering scalable DALLAS community solutions. It does this by taking a collaborative approach to capacity development, working with NHS informatics and frontline innovation centres towards efficient use of the technology by the workforce. There is a strong focus on delivery of patient value by continuous measurement and systematic feedback of outcomes. Sintero is being made available to UK DALLAS communities on a collaborative basis.

Adoption of SNOMED CT has not been as quick and easy as many people had hoped or expected. One reason is lack of education and hence understanding of what SNOMED CT does and how it works. We set out to answer the question "who needs to know what?" about SNOMED CT to help establish priorities for UK higher education. We devised an online questionnaire and obtained 177 responses, 51% health IT professionals, 42% clinicians. The sample was self-selecting of those with knowledge of SNOMED CT. The level of reported competence was greater among health IT professionals (33% rated themselves as competent) than among clinicians (5% rated themselves as competent) 92% of those who felt competent had received 3 or more days of training in SNOMED CT. This indicates the need for formal training in SNOMED CT. Most respondents indicated that health IT professionals ought to have a high level of competence in SNOMED CT, such that they are able to explain most if not all aspects of SNOMED CT to others. On the other hand, clinicians only require a fairly basic understanding.

Objectives : To examine the evidence base for telemonitoring designed for patients who have chronic obstructive pulmonary disease and heart failure, and to assess whether telemonitoring fulfils the principles of monitoring and is ready for implementation into routine settings. Design Qualitative data collection using interviews and participation in a multi-path mapping process. Participants : Twenty-six purposively selected informants completed semi-structured interviews and 24 individuals with expertise in the relevant clinical and informatics domains from academia, industry, policy and provider organizations and participated in a multi-path mapping workshop. Results : The evidence base for the effectiveness of telemonitoring is weak and inconsistent, with insufficient cost-effectiveness studies. When considered against an accepted definition of monitoring, telemonitoring is found wanting. Telemonitoring has not been able so far to ensure that the technologies fit into the life world of the patient and into the clinical and organizational milieu of health service delivery systems. Conclusions : To develop effective telemonitoring for patients with chronic disease, more attention needs to be given to agreeing the central aim of early detection and, to ensure potential implementation, engaging a wide range of stakeholders in the design process, especially patients and clinicians.

OBJECTIVE:To propose a research agenda that addresses technological and other knowledge gaps in developing telemonitoring solutions for patients with chronic diseases, with particular focus on detecting deterioration early enough to intervene effectively. DESIGN:A mixed methods approach incorporating literature review, key informant, and focus group interviews to gain an in-depth, multidisciplinary understanding of current approaches, and a roadmapping process to synthesise a research agenda. RESULTS:Counter to intuition, the research agenda for early detection of deterioration in patients with chronic diseases is not only primarily about advances in sensor technology but also much more about the problems of clinical specification, translation, and interfacing. The ultimate aim of telemonitoring is not fully agreed between the actors (patients, clinicians, technologists, and service providers). This leads to unresolved issues such as: (1) How are sensors used by patients as part of daily routines? (2) What are the indicators of early deterioration and how might they be used to trigger alerts? (3) How should alerts lead to appropriate levels of responses across different agencies and sectors? CONCLUSION:Attempts to use telemonitoring to improve the care of patients with chronic diseases over the last two decades have so far failed to lead to systems that are embedded in routine clinical practice. Attempts at implementation have paid insufficient attention to understanding patient and clinical needs and the complex dynamics and accountabilities that arise at the level of service models. A suggested way ahead is to co-design technology and services collaboratively with all stakeholders.

In this paper we discuss a flexible distributed workflow-based approach that enables researchers to study biomedical data for creating decision support pipelines. Specifically we describe the TRIACS platform, which has first been applied to supporting evidence-based decisions for optimum diabetic retinopathy screening intervals In the prioritization mechanism, pseudonymised case data is stratified for screening need by computation of outcome risk or by clustering of dasiaat-riskpsila cases with past cases of actual preventable outcomes. Workflows present a novel approach to this problem by providing an appropriate level of granularity for breaking the domain problem into a collection of reusable service-oriented components that can be applied in different ways. TRIACS is intended to make the creation of new application logic quicker and easier than bespoke development methods. Through the TRIACS workflow interface, modular code is portable and available to solve analogous domain problems including application to trial studies for mining and analysing clinical data.

We are interested in understanding how high quality information management systems can improve disease prevention and screening services, particularly in the prevention of diabetes and its complications. As part of this effort, we are exploiting standards-driven distributed data collation, processing and computational technologies to deduce comparative performance of disease outcomes markers. In the system design, data is sent to an individualized risk management information service (e.g. as part of a home or mobile-based telehealth service). Data originating across the 'patient path' is quality controlled to improve the accuracy of risk models. Delivery of these functions 'at scale' requires the development of new data trend management and decision support tools. Semantic standardization, particularly in representation of disease progression features for data aggregation and distributed search is a key requirement. We discuss some of the current challenges surrounding outcomes marker modeling to support disease prevention and screening services.

Novel sensor-based continuous biomedical monitoring technologies have a major role in chronic disease management for early detection and prevention of known adverse trends. In the future, a diversity of physiological, biochemical and mechanical sensing principles will be available through sensor device 'ecosystems'. In anticipation of these sensor-based ecosystems, we have developed Healthcare@Home (HH) - a research-phase generic intervention-outcome monitoring framework. HH incorporates a closed-loop intervention effect analysis engine to evaluate the relevance of measured (sensor) input variables to system-defined outcomes. HH offers real-world sensor type validation by evaluating the degree to which sensor-derived variables are relevant to the predicted outcome. This 'index of relevance' is essential where clinical decision support applications depend on sensor inputs. HH can help determine system-integrated cost-utility ratios of bespoke sensor families within defined applications - taking into account critical factors like device robustness / reliability / reproducibility, mobility / interoperability, authentication / security and scalability / usability. Through examples of hardware / software technologies incorporated in the HH end-to-end monitoring system, this paper discusses aspects of novel sensor technology integration for outcome-based risk analysis in diabetes.

The research aim underpinning the Healthcare@Home (HH) information system described here was to enable 'near real time' risk analysis for disease early detection and prevention. To this end, we are implementing a family of prototype web services to 'push' or 'pull' individual's health-related data via an system of clinical hubs, mobile communication devices and/or dedicated home-based network computers. We are examining more efficient methods for ethical use of such data in timeline-based (i.e. 'longitudinal') data analysis systems. A consistent data collation infrastructure is being created for use along the 'patient path'--accessible wherever patients happen to be. This 'patient-centred' infrastructure can be applied in the evaluation of disease progression risk (in the light of clinical understanding of disease processes). In this paper we describe the requirements for making multi-data trend management 'scale-up', together with some requirements of an 'end-to-end' functioning data collection system. A Service-Oriented Architecture (SOA) approach is used to maximise benefits from (1) clinical evidence and (2) computational models of disease progression that can be made available elsewhere on the SOA. We discuss the implications of this so-called 'closed loop' approach for improving healthcare intervention outcomes, patient safety, decision support, objective measurement of service quality and in providing inputs for quantitative healthcare (predictive) modelling.

In this paper we apply an alternative search mechanism to biomedical audio/visual and spectral data discovery using distributed peer-to-peer techniques. We describe the underlying architecture, distributed database, along with the complex search algorithms based on multimodal workflows, aggregating index and content information. We exemplify the utilization of such a distributed infrastructure in diabetic retinopathy research and clinical trials, for the early detection and prevention of retinal disease and investigational drug discovery. The peer-to-peer toolkit itself provides a platform that enables users to build search algorithms by combining components into workflows that are executed across the peers on the network using industry standards such as Web services and SOAP. An aspect of this framework is that it contains a mobile implementation capable of allowing the control and monitoring of searches from mobile devices, as well as remote workflow management by the remote control of the enactment engine from a mobile user interface.

Integrated care pathways (ICP) are increasingly used in clinical settings to provide more effective care to patients. ICPs form part of local working agreements to assist co-ordination of multi-disciplinary teams to deliver evidence-based care plans to individual patients. They also document the expected progress of specific patient groups as part of clinical records. To anticipate increased use of ICPs, we have developed Healthcare@Home, a research-phase demonstrator for improving integration of information along the patient path. Healthcare@Home includes support for at-home, in-clinic and mobile wireless sensor devices feeding patient-proximal data hubs, timeline-based physiological trend analysis, data aggregation/dashboarding and individualised risk stratification. These and other decision support tools are embedded in portal designs supporting 'end-to-end' workflows as focused by the composite needs of a National Service Framework (NSF) for patients with diabetes. Healthcare@Home thus represents a scaleable, extensible personalised healthcare information system driven directly from national policy on disease early detection and prevention. Individual portlets have been mapped to stages in the ICP. The portal technologies employed, running on PCs, mobile phones or TVs are capable of highly cost-effective 'end-to-end, anywhere-to-anywhere' information integration.

In this paper we present mechanisms for imaging and spectral data discovery, as applied to the early detection of pathologic mechanisms underlying diabetic retinopathy in research and clinical trial scenarios. We discuss the Alchemist framework, built using a generic peer-to-peer architecture, supporting distributed database queries and complex search algorithms based on workflow. The Alchemist is a domain-independent search mechanism that can be applied to search and data discovery scenarios in many areas. We illustrate Alchemist's ability to perform complex searches composed as a collection of peer-to-peer overlays, Grid-based services and workflows, e.g. applied to image and spectral data discovery, as applied to the early detection and prevention of retinal disease and investigational drug discovery. The Alchemist framework is built on top of decentralised technologies and uses industry standards such as Web services and SOAP for messaging.

Healthcare@Home is a research project implementing a prototype to `push' or `pull' data via mobile devices and/or dedicated home-based network servers to one or more, data analysis engines. This data is then used to evaluate diabetes risk assessment for a particular individual, and also undertake trends analysis across data from multiple individuals. We outline the need for such a personalized health management system, and describe a logical and physical architecture of the system - making use of the service-oriented architecture (SOA) approach. The current status of the implementation is then reported, utilizing Web services and clinical sensor technologies

Publisher Summary The use of the Internet as a device for systematic interrelation of detailed scientific information is now well-established. The medium has shown remarkable development within the last few years, to the point that web-based client-server technologies are dominating the design of new operating systems. These forms of distributed network computing will have major applications in biology, particularly where the computational power can lie at a remote site. Even today, several public domain and commercial databases can perform computation-intensive processes from a simple web browser irrespective of the host computer used. Despite current optimism shown by proponents of “bioinformatics, it should be emphasized that the process of interlinking (federation) of more than a few different datasets will be a complex and labor-intensive process. Present techniques cannot easily make accurate predictions about the biological function of ion channels from raw sequence data. Despite this, databases can efficiently correlate similarities in sequence with respective known functions; this aspect is explored in detail later. Internet databases actually have very limited applications as storage devices—their application in answering scientific questions that makes them interesting and important for the future.

Inwardly rectifying potassium (Kir) channels are expressed in a wide range of excitable and non-excitable cells, where they set the resting membrane potential. Because Kirs are open in the range of the resting potential, their modulation is an important factor in the regulation of cellular excitability. In the noradrenergic neurons of locus coeruleus (LC), the neuropeptide substance P (SP) has been shown to suppress Kir activity.1,2 In this report we demonstrate, by immunohistochemistry with site-directed polyclonal antibodies, the co-localization of Kir2.2 and substance P receptor (SPR) proteins in single LC neurons and associated oligodendroglia. In addition, we show a nuclear localization in LC neurons for Kir2.2 protein. This was supported by a nuclear signal with Kir2.2 antibodies obtained in Chinese hamster ovary (CHO) cells transiently transfected with Kir2.2. Expression of Kir2.2 protein could not be detected in untransfected cells. The Kir2.2 antibody detected a single 62 kD band on Western blots containing rat brain nuclear and plasma membrane protein fractions. This band was blocked by incubation with the 10 μg/ml Kir2.2 antigenic peptide (data not shown). Similarly Kir2.2 immunostaining on rat brain tissue sections was also blocked by incubation with the Kir2.2 antigenic peptide (data not shown). Adjacent coronal cerebellar rat brain tissue sections (10 μm deep, paraffin-embedded) were immunostained with the affinity purified SPR antibody (1:500), or the affinity purified Kir2.2 antibody (12 μg/ml). The large diameter (∼40 μm) of LC neurons made it possible to identify cells present in serial tissue sections. Strong nuclear and plasma membrane signals in neurons were observed for Kir2.2, with a clear nuclear signal also in oligodendroglia (FIG. 1A). SPR was strongly expressed on the plasma membranes of neurons and oligodendroglia, as well as neural processes within the LC (FIG. 1B). In order to establish co-expression of Kir2.2 and SPR, the Macromedia XRES program was used to overlay the two adjacent tissue sections. This overlay demonstrated a co-localization of SPR and Kir2.2 proteins in single neurons and oligodendroglia of the LC (FIG. 1C). This is consistent with previous