Establishment of Requirements and Methodology for the Development and Implementation of GreyMatters, a Memory Clinic Information System

INTRODUCTION: The aim of the paper is to establish the requirements and methodology for the development process of GreyMatters, a memory clinic system, outlining the conceptual, practical, technical and ethical challenges, and the experiences of capturing clinical and research oriented data along with the implementation of the system. METHODS: The methodology for development of the information system involved phases of requirements gathering, modeling and prototype creation, and 'bench testing' the prototype with experts. The standard Institute of Electrical and Electronics Engineers (IEEE) recommended approach for the specifications of software requirements was adopted. An electronic health record (EHR) standard, EN13606 was used, and clinical modelling was done through archetypes and the project complied with data protection and privacy legislation. RESULTS: The requirements for GreyMatters were established. Though the initial development was complex, the requirements, methodology and standards adopted made the construction, deployment, adoption and population of a memory clinic and research database feasible. The electronic patient data including the assessment scales provides a rich source of objective data for audits and research and to establish study feasibility and identify potential participants for the clinical trials. CONCLUSION: The establishment of requirements and methodology, addressing issues of data security and confidentiality, future data compatibility and interoperability and medico-legal aspects such as access controls and audit trails, led to a robust and useful system. The evaluation supports that the system is an acceptable tool for clinical, administrative, and research use and forms a useful part of the wider information architecture

Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement

BACKGROUND:Stroke, like many long-term conditions, tends to be managed in isolation of its associated risk factors and multimorbidity. With increasing access to clinical and research data there is the potential to combine data from a variety of sources to inform interventions to improve healthcare. A 'Learning Health System' (LHS) is an innovative model of care which transforms integrated data into knowledge to improve healthcare. The objective of this study is to develop a process of engaging stakeholders in the use of clinical and research data to co-produce potential solutions, informed by a LHS, to improve long-term care for stroke survivors with multimorbidity. METHODS:We used a stakeholder engagement study design informed by co-production principles to engage stakeholders, including service users, carers, general practitioners and other health and social care professionals, service managers, commissioners of services, policy makers, third sector representatives and researchers. Over a 10 month period we used a range of methods including stakeholder group meetings, focus groups, nominal group techniques (priority setting and consensus building) and interviews. Qualitative data were recorded, transcribed and analysed thematically. RESULTS:37 participants took part in the study. The concept of how data might drive intervention development was difficult to convey and understand. The engagement process led to four priority areas for needs for data and information being identified by stakeholders: 1) improving continuity of care; 2) improving management of mental health consequences; 3) better access to health and social care; and 4) targeting multiple risk factors. These priorities informed preliminary design interventions. The final choice of intervention was agreed by consensus, informed by consideration of the gap in evidence and local service provision, and availability of robust data. This shaped a co-produced decision support tool to improve secondary prevention after stroke for further development. CONCLUSIONS:Stakeholder engagement to identify data-driven solutions is feasible but requires resources. While a number of potential interventions were identified, the final choice rested not just on stakeholder priorities but also on data availability. Further work is required to evaluate the impact and implementation of data-driven interventions for long-term stroke survivors

Electronic case report forms and electronic data capture within clinical trials and pharmacoepidemiology

Aims: Researchers in clinical and pharmacoepidemiology fields have adopted information technology (IT) and electronic data capture, but these remain underutilised despite the benefits. This review discusses electronic case report forms and electronic data capture, specifically within pharmacoepidemiology and clinical research.Methods: The review used PubMed and the Institute of Electrical and Electronic Engineers library (IEEE). Search terms used were agreed by the authors and documented. PubMed is medical and health based, whereas IEEE is technology based. The review focuses on electronic case report forms and electronic data capture, but considers briefly other relevant topics; consent, ethics, and security.Results: One thousand, one hundred and twenty six papers were found using the search terms. Manual filtering and reviewing of abstracts further condensed this number to 136 relevant manuscripts. The papers were further categorised; 17 containing study data, 40 containing observational data, 27 anecdotal data, 47 covering methodology or design of systems, 1 case study, 1 literature review, 2 feasibility studies, and 1 cost analysis.Conclusion: Electronic case report forms, electronic data capture, and IT in general, are viewed with enthusiasm and are seen as a cost effective means of improving research efficiency, educating participants, and improving trial recruitment, provided concerns about how data will be protected from misuse can be addressed. Clear operational guidelines and best practices are key for healthcare providers, and researchers adopting IT, and further work is needed on improving integration of new technologies with current systems. A robust method of evaluation for technical innovation is required

A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools

Several tools exist that are designed to process computer interpretable guidelines (CIGs), each with a distinct purpose, such as detecting interactions or patient personalisation. While it is desirable to use these tools as part of larger decision support systems (DSSs) doing so is often not straightforward, as their design does not often support external interoperability or account for the fact that other CIG tools may be running in parallel, a situation that will become increasingly more prevalent with the increased adoption of CIGs in different parts of the health system. This results in an integration overhead, system redundancy and a lack of flexibility in how these tools can be combined. To address these issues, we define a blueprint architecture to be used in the design of guideline processing tools, based on the conceptualisation of key components as RESTful microservices. In addition, we define the types of data endpoints that each component should expose, for both the communication between internal components and communication with external components that exist as a part of a DSS. To demonstrate the utility of our architecture, we show how an example guideline processing tool can be restructured according to these principles, in order to enable it to be flexibly integrated into the DSS used in the CONSULT project