Logic Model Early Stage Evaluation of a European Public Health Data Analytic Framework (original) (raw)
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Evaluating Impact of an Emerging Big Health Data Platform: A Logic Model and Q-Methodology Approach
SSRN Electronic Journal
Despite advances in technology and medical science, modern health-based projects are open to systemic failure due to many factors. These include I.T. developer's lack of awareness with regard to end-user needs, poor communication amongst all parties concerned and inappropriate or inadequate tests of the emerging system. Other issues may be external (e.g. political and legal) such as sharing of patient data and issues surrounding consent. The goal of this paper is to take a major health-based European model in current development and explore how it addresses the needs of four institutions in four different countries, and how it will meet their respective needs. The evaluation was designed within a Logic Model, and uses the Framework approach, and Q-Methodology to assess both impact and evaluation. Data will be collected through longitudinal semi-structured interviews and Q-scoring with principal stakeholders and developers at each stage of the project. This approach, recurring interviews with the same key players in the project, will help ensure that there is mutual understanding between I.T. developers and endusers of the system. The final system is meant to provide effective health-based decision support systems for policy makers.
Realist Evaluation of a European Data Analytic Framework
HCI 2018
The multinational MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate the utilisation of a wide range of health and social care data. The platform will enable the integration of heterogeneous data sources, providing privacy-preserving analytics, forecasting tools and bespoke visualisations of actionable epidemiological data. An evaluation framework starting with a logic model and using the principles of realist evaluation developed working with users, and software developers. The tools used are a series of parallel case studies to address the requirements of stakeholder groups at critical time points during the project to ensure IT systems development is in line with user's requirements. The process includes longitudinal interviews with stakeholders, regular feedback to users and developers, and measurement of stakeholder's attitudes to the project using Q-methodology.
Q-Method Evaluation of a European Health Data Analytic End User Framework
SSRN Electronic Journal
MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate the utilisation of a wide range of health and social care data to support better policy making. Our aim is to explore the use of Qmethodology as part of the evaluation of the implementation of the MIDAS project. Q-methodology is used to identify perspectives and viewpoints on a particular topic. In our case, we defined a concourse of statements relevant to project implementation and goals, by working from a logic model previously developed for the evaluation, and structured interviews with project participants. A 36-item concourse was delivered to participants, using the HTMLQ system. Analysis was done in the qmethod package. Participants had a range of professional backgrounds, and a range of roles in the project, including developers, end-users, policy staff, and health professionals. The qsort is carried out at 14 months into the project, a few months before the intended first release of the software being developed. Sixteen people took part, 6 developers, 5 managers, 2 health professionals and 3 others. Three factors (distinct perspectives) were identified in the data. These were tentatively labelled 'Technical optimism', 'Enduser focus' and 'End-user optimism'. These loaded well onto individuals, and there were few consensus statements. Analysis of these factors loaded well onto individuals with a significant number of consensus statements identified.
HCI 2018, 2018
This paper outlines the scope and aims of the MIDAS Project, a Horizon 2020-funded initiative to develop a data analytics platform to support better policy-making in the European health sector. It focuses specifically on the engagement of users in the co-design of the platform, and describes a participatory workshop which encouraged stakeholders to share their understanding of the problem to be addressed and insights into potential solutions. The major elements of the workshop are described and the key results are presented. Participant feedback is analysed and the main lessons and insights are highlighted, including the importance of an adopting an iterative approach to user engagement in software design and development.
Impact Evaluation of an Emerging European Health Project -the MIDAS Model
Business Systems Research, 2020
Background: This paper describes the impact evaluation of a large big data platform initiative that is being undertaken in order to increase the probability of its success. The initiative, MIDAS (Meaningful Integration of Data Analytics and Services), is a European health-based Horizon 2020 project comprising a consortium of members from various universities, research institutions, and government agencies. Objectives: The purpose of the paper is to present a pioneering platform that will support healthcare policymakers in their decision-making by enabling greater and more efficient use of their data. The goal is to present and evaluate the results of the MIDAS project across four countries. Methods/Approach: The literature is replete with examples of worthwhile technology projects that have failed due to user resistance. In order to avoid such failure, and ensure the success of the final MIDAS platform, a detailed impact evaluation is being undertaken at timed periods of development. Results: This paper describes the impact evaluation process, outlining the use of Q-methodology and the development of a 36-item concourse using the HTMLQ system for that purpose. Conclusions: This research contributes to the overall understanding of how impact evaluation can be undertaken at timed periods during the development of an innovative technology for organisational purposes.
Guidance on the use of logic models in health technology assessments of complex interventions
2016
The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. The European Commission is not responsible for any use that may be made of the information contained therein. | 6 addition, two subtypes are identified, namely logic models that seek to represent structure (system-based) and those that focus on processes or activities (process-orientated). This guidance offers direction on how to choose between distinct types and sub-types of logic models, describes each logic model type and its application in detail, and provides templates for getting started with the development of an HTA/SR-specific logic model. Development of the guidance This guidance was informed by a combination of (i) systematic searches for published examples of logic models supplemented by purposive sampling of iterative logic modelling approaches; (ii) searches for existing guidance on the use of logic models in primary research, SRs and HTAs; (iii) development of two draft templates for system-based and process-orientated logic models in an iterative process within the research team and in consultation with external methodological experts; and (iv) application of these draft templates in multiple SRs and one HTA of different complex health technologies covering technical, educational and policy interventions in environmental health, e-learning for health professionals and models of palliative care.
Usability Evaluation of a Co-created Big Data Analytics Platform for Health Policy-Making
Human Interface and the Management of Information. Visual Information and Knowledge Management, 2019
The increasingly important role of big data in organisational decisionmaking brings with it significant challenges in terms of designing usable software interfaces. Specifically, such interfaces must allow users to explore, analyse, and visualise complex data from heterogeneous sources and derive insights to support management decisions. This paper describes a usability evaluation of the MIDAS Project, a big data platform for health policy-making, developed by an EUfunded Horizon 2020 project involving a number of international partners and pilot sites. We describe how a combination of heuristic and formative user-centred evaluation methods were employed, and give a summary of the key findings. We discuss key insights from the evaluation, including the importance of having diverse users, the role played by users' prior expectations, and the logistical challenge of coordinating user testing across multiple sites. Finally, we explore the relative value of each of the evaluation methods, and outline how our approach to usability testing will evolve for future iterations of the MIDAS platform.
International Journal of Case Studies in Business, IT, and Education (IJCSBE), 2020
Big Data Analytics (BDA) has brought revolutionary changes in many fields. The areas of application such as banking, education, manufacturing, farming, government, transport, media, and entertainment are the ones that make extensive application of BDA. Healthcare is the one that has experienced drastic changes because of BDA. Due to the positive effects of big data, risky jobs like diagnosis, reporting, CRM, predicting the deceases, tracking medical records has become much easier these days. ScienceSoft is an IT company providing information technology services in emerging areas such as CRM, Data Analytics, Collaboration, Knowledge Management, Information Security, etc. ScienceSoft's headquarters is located in McKinney, USA. Organizations such as IBM, Microsoft, Oracle, etc. are collaborating with ScienceSoft due to the reliable and high-quality services provided. This paper attempts to give a broader outlook of Big Data, analyzes the company's business models for handling Big Data Analytics related projects, particularly in the healthcare sector. This paper also contains information related to the challenges of analyzing Big Data, the Company's technologies and tools that are needed in the development of BDA projects and how Big Data is converted into useful knowledge to deliver better results in healthcare allied sectors.
Creating and Using Logic Models: Four Perspectives
2015
SUMMARY. The use of logic models in program development, evalu ation, and dissemination is becoming more commonly accepted as a means of facilitating communication, replication, quality improvement, and assessment. Each of the following chapters in this book includes a logic model of the program being described. The purpose of this chap ter is to describe what logic models are, and to convey to a diverse field the role and functioning of logic models in the conceptualization, deliv ery, management, and evaluation of programs. Since this volume is in tended for a wide audience including service providers, program ad ministrators, and researchers, we will attempt to provide information on logic models that is broadly useful. Therefore, this chapter is structured so that each of the four authors presents her/his unique perspective based principally on their own experience using logic models. [Article copies available for a fee from The Haworth Document Delivery Service: