How to work with local communities to improve population health: big data and small data

2017 ◽  
Vol 71 (7) ◽  
pp. 657-659 ◽  
Author(s):  
Rafael Cofiño ◽  
Sonia Lopez-Villar ◽  
Oscar Suárez
2019 ◽  
Vol 32 (2) ◽  
pp. 425-430
Author(s):  
Ahmed Otokiti

Purpose The purpose of this paper is to provide insights into contemporary challenges associated with applying informatics and big data to healthcare quality improvement. Design/methodology/approach This paper is a narrative literature review. Findings Informatics serve as a bridge between big data and its applications, which include artificial intelligence, predictive analytics and point-of-care clinical decision making. Healthcare investment returns, measured by overall population health, healthcare operation efficiency and quality, are currently considered to be suboptimal. The challenges posed by informatics/big data span a wide spectrum from individual patients to government/regulatory agencies and healthcare providers. Practical implications The paper utilizes informatics and big data to improve population health and healthcare quality improvement. Originality/value Informatics and big data utilization have the potential to improve population health and service quality. This paper discusses the challenges posed by these methods as the author strives to achieve the aims.


2019 ◽  
Vol 5 (3) ◽  
pp. 26-29
Author(s):  
Michael Willis ◽  
Cheryl Neslusan ◽  
Silas Martin ◽  
Pierre Johansen ◽  
Christian Asseburg ◽  
...  

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.


2018 ◽  
Vol 98 ◽  
pp. 367-383 ◽  
Author(s):  
Zahir Irani ◽  
Amir M. Sharif ◽  
Habin Lee ◽  
Emel Aktas ◽  
Zeynep Topaloğlu ◽  
...  
Keyword(s):  
Big Data ◽  

2015 ◽  
Vol 198 ◽  
pp. 339-343 ◽  
Author(s):  
Antonio Moreno-Sandoval ◽  
Esteban Moro
Keyword(s):  
Big Data ◽  

2014 ◽  
Vol 23 (01) ◽  
pp. 21-26 ◽  
Author(s):  
T. Miron-Shatz ◽  
A. Y. S. Lau ◽  
C. Paton ◽  
M. M. Hansen

Summary Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.


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