380 research outputs found
Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement.
Get PDFBackgroundStroke, 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.MethodsWe 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.Results37 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.ConclusionsStakeholder 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
Collaborative design of a decision aid for stroke survivors with multimorbidity: a qualitative study in the UK engaging key stakeholders
Get PDFObjectives: Effective secondary stroke prevention strategies are sub-optimally used. Novel development of interventions to enable healthcare professionals and stroke survivors to manage risk factors for stroke recurrence are required. We sought to engage key stakeholders in the design and evaluation of an intervention informed by a Learning Health System approach, to improve risk factor management and secondary prevention for stroke survivors with multimorbidity.Design: Qualitative, including focus groups, semi-structured interviews and usability evaluations. Data was audio-recorded, transcribed and coded thematically.Participants: Stroke survivors, carers, health and social care professionals, commissioners, policy makers and researchers.Setting: Stroke survivors were recruited from the South London Stroke Register; health and social care professionals through South London general practices and King’s College London (KCL) networks; carers, commissioners, policy-makers and researchers through KCL networks.Results: 53 stakeholders in total participated in focus groups, interviews and usability evaluations. Thirty-seven participated in focus groups and interviews, including stroke survivors and carers (N=11), health and social care professionals (N=16), commissioners and policy-makers (N=6) and researchers (N=4). Sixteen participated in usability evaluations, including stroke survivors (N=8) and general practitioners (GPs; N=8). Eight themes informed the collaborative design of DOTT (Deciding on Treatments Together), a decision aid integrated with the electronic health record system, to be used in primary care during clinical consultations between the healthcare professional and stroke survivor. DOTT aims to facilitate shared decision making on personalised treatments leading to improved treatment adherence and risk control. DOTT was found acceptable and usable among stroke survivors and GPs during a series of evaluations.Conclusions: Adopting a user-centred data-driven design approach informed an intervention that is acceptable to users and has the potential to improve patient outcomes. A future feasibility study and subsequent clinical trial will provide evidence of the effectiveness of DOTT in reducing risk of stroke recurrence
Why does human phenomics matter today?
No full textHuman phonemics responds to an urgent need in the medical research community; namely, reproducibility.</p
Embedding data provenance into the Learning Health System to facilitate reproducible research
Get PDFOur world is increasingly driven by data. Medical, economic and political decisions are made based on automated analysis of ever-growing volumes of data, be they patient treatment decisions generated from rule models or stock trading decisions made by micro-trading tools. Scientific discovery is now all but impossible without data-intensive infrastructures, which have transformed both how science is done and what science is done. The Learning Health System (LHS) community has taken up the challenge of bringing the complex relationship between clinical research and practice into this brave new world.At the heart of the LHS vision is the notion of routine capture, transformation and dissemination of data and knowledge, with various use cases, such as clinical studies, quality improvement initiatives and decision support, constructed on top of specific routes that the data is taking through the system. In order to stop this increased data volume and analytical complexity from obfuscating the research process, it is essential to establish trust in the system through implementing reproducibility and auditability throughout the workflow.Data provenance technologies can automatically capture the trace of the research task and resulting data, thereby facilitating reproducible research. While some computational domains, such as bioinformatics, have embraced the technology through provenance-enabled execution middlewares, disciplines based on distributed, heterogeneous software, such as medical research, are only starting on the road to adoption, motivated by the institutional pressures to improve transparency and reproducibility.Guided by the experiences of the TRANSFoRm project, we present the opportunities that data provenance offers to the Learning Health System community. We illustrate how provenance can facilitate documenting 21 CFR part 11 compliance for FDA submissions and provide auditability for decisions made by the decision support tools and discuss the transformational effect of routine provenance capture on data privacy, study reporting and publishing medical research
Workflow modelling for scientific processes
No full textEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Templates as a method for implementing data provenance in decision support systems
Get PDFAbstractDecision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates – abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project
Managing and exploiting routinely collected NHS data for research
Get PDFIntroduction Health research using routinely collected National Health Service (NHS) data derived from electronic health records (EHRs) and health service information systems has been growing in both importance and quantity. Wide population coverage and detailed patient-level information allow this data to be applied to a variety of research questions. However, the sensitivity, complexity and scale of such data also hamper researchers from fully exploiting this potential.Objective Here, we establish the current challenges preventing researchers from making optimal use of the data sets at their disposal, on both the legislative and practical levels, and give recommendations as to how these challenges can be overcome.Method A number of projects has recently been launched in the UK to address poor research data management practices. Rapid Organisation of Healthcare Research Data (ROHRD) at Imperial College, London produced a useful prototype that provides local researchers with a one-stop index of available data sets together with relevant metadata.Findings Increased transparency of data sets’ availability and their provenance leads to better utilisation and facilitates compliance with regulatory requirements.Discussion Research data resulting from NHS data is often not utilised fully, or is handled in a haphazard manner that prevents full auditability of the research. Furthermore, lack of informatics and data management skills in research teams act as a barrier to implementing more advanced practices, such as provenance capture and detailed, regularly updated, data management strategies. Only by a concerted effort at the levels of research organisations, funding bodies and publishers, can we achieve full transparency and reproducibility of the research.</p
Analysing scientific workflows with computational tree logic
No full textMotivated by the widespread use of workflow systems in e-Science applications, this article introduces a formal analysis framework for the verification and profiling of the control flow aspects of scientific workflows. The framework relies on process algebras that characterise each workflow component with a process behaviour, which is then used to build a CTL state model that can be reasoned about. We demonstrate the benefits of the approach by modelling the control flow behaviour of the Discovery Net system, one of the earliest workflow-based e-Science systems, and present how some key properties of workflows and individual service utilisation can be queried at design time. Our approach is generic and can be applied easily to modelling workflows developed in any other system. It also provides a formal basis for the comparison of control aspects of e-Science workflow systems and a design method for future systems.</p
UPAYA-UPAYA ROOM ATTENDANT DALAM MEMELIHARA KENYAMANAN TAMU DI VASA HOTEL SURABAYA
Get PDFThe goal to be achieved in this research is to find out the efforts made by the room
attendant in maintaining guest comfort at Vasa hotel Surabaya so that guests feel
happy, satisfied and want to visit again at Vasa Hotel Surabaya. The author makes
observations of the room attendant's efforts in maintaining guest comfort. The
author concludes that the efforts made by the room attendant in maintaining guest
comfort at Vasa hotel Surabaya are as follows: Maintain cleanliness, greet guests
and offer assistance, pause and say hello when meeting guests in the corridor area
or other places other than the room. , and provides towel folding services (towel
art) and has also carried out standard operating room attendant procedures
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