Despite the recognised benefits to human health from green and blue spaces, socioeconomic inequalities in access to and use of such spaces have been observed. Using a multidisciplinary, multistakeholder systems approach and structural equation modelling, this paper examines the structural and behavioural dynamics of green and blue spaces, people and health and wellbeing outcomes. Systems thinking offers a deeper understanding of the dynamics of collective choices at all levels within the determinants and the circular causality of these processes. The resulting map shows that behavioural and structural dynamics of green and blue spaces reinforce social cohesion, mental and physical benefits and their circular causality. Acknowledging the importance of multiple uses of green and blue spaces, this paper concludes that delivering universal services at a scale and intensity proportionate to the degree of need is vital to ensure services and health and wellbeing benefits are available to all, not only the most advantaged.

In today's healthcare (HC) market there are lots of pressures on HC organizations (Os). Besides, many challenges including; demographic changes and the need to manage vastly increasing data volumes in HC, have motivated these organizations to adopt business intelligence (BI) solutions. Through a systematic review of the literature, this study establishes the patterns of BI adoption in the HC domain by examining the nature of BI solutions in use, expected outcomes from BI use, specific types of BI capabilities deployed, and aspects of HCOs directly impacted. Findings from our study provide a foundation for future research agenda on BI in Healthcare. We conclude by highlighting the shortcomings of current BI practice in the HC domain in the context of the emerging value-based (VB) HC delivery model and the need for research in this direction.

Improving the value of care is one of the essential aspects of Value-Based Healthcare (VBHC) model today. VBHC is a new HC delivery model which is centered on patient health outcomes and improvements. There is anecdotal evidence that the use of decision aid tools like dashboards can play a significant role in the successful implementation of VBHC models. However, there has been little or no systematic studies and reviews to establish the extent to which analytics dashboards are used to support patient care in a VBHC delivery context. This paper bridges this knowledge gap through a systematic review of the existing literature on dashboards in the HC domain. Our study reveals dashboard capabilities as an enabling tool for value improvements and provides insight into the design of dashboards. This study concludes by highlighting a few gaps, question, and need for research in the future.The need for HC services is rising due to growing global demand and population, the burden of increasingly complicated chronic disease [8]. With

The objective of this paper is to investigate existing factors related to the decision to adopt and use of dashboards in the healthcare domain using a systematic literature review approach. The study is part of a larger initiative on how analytics dashboards can support decisions in value-based prostate cancer treatment and care. Although many studies have been undertaken to evaluate the implementation of health information technologies in the healthcare sector, as far as we know, none of these studies provides a framework for dashboards use in the healthcare context. We believe that the resulting model from our study provides the necessary first step in developing empirical evidence for the acceptance and use of the dashboards in the healthcare domain.

Introduction: The National Care Experience Programme (NCEP) conducts national surveys that ask people about their experiences of care in order to improve the quality of health and social care services in Ireland. Each survey contains open-ended questions, which allow respondents to comment on their experiences. While these comments provide important and valuable information about what matters most to service users, there is to date no unified approach to the analysis and integration of this detailed feedback. The objectives of this study are to analyse qualitative responses to NCEP surveys to determine the key care activities, resources and contextual factors related to positive and negative experiences; to identify key areas for improvement, policy development, healthcare regulation and monitoring; and to provide a tool to access the results of qualitative analyses on an ongoing basis to provide actionable insights and drive targeted improvements. Methods: Computational text analytics methods will be used to analyse 93,135 comments received in response to the National Inpatient Experience Survey and National Maternity Experience Survey. A comprehensive analytical framework grounded in both service management literature and the NCEP data will be employed as a coding framework to underpin automated analyses of the data using text analytics and deep learning techniques. Scenario-based designs will be adopted to determine effective ways of presenting insights to knowledge users to address their key information and decision-making needs. Conclusion: This study aims to use the qualitative data collected as part of routine care experience surveys to their full potential, making this information easier to access and use by those involved in developing quality improvement initiatives. The study will include the development of a tool to facilitate more efficient and standardised analysis of care experience data on an ongoing basis, enhancing and accelerating the translation of patient experience data into quality improvement initiatives.