Alignment of Big Data Perceptions Across Levels in Healthcare: The case of New Zealand

Big data and related technologies have the potential to transform healthcare sectors by facilitating improvements to healthcare planning and delivery. Big data research highlights the importance of aligning big data implementations with business needs to achieve success. In one of the first studies to examine the influence of big data on business-IT alignment in the healthcare sector, this paper addresses the question: how do stakeholders’ perceptions of big data influence alignment between big data technologies and healthcare sector needs across macro, meso, and micro levels in the New Zealand (NZ) healthcare sector? A qualitative inquiry was conducted using semi-structured interviews to understand perceptions of big data across the NZ healthcare sector. An application of a novel theory, Theory of Sociotechnical Representations (TSR), is used to examine people’s perceptions of big data technologies and their applicability in their day-to-day work. These representations are analysed at each level and then across levels to evaluate the degree of alignment. A social dimension lens to alignment was used to explore mutual understanding of big data across the sector. The findings show alignment across the sector through the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of utilising modern and new data in measuring health outcomes. Areas of misalignment include the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. Both practical and theoretical contributions of the study are discussed

A Review Paper on Scope of Big Data Analysis in Heath Informatics

Abstract— The term Health Informatics represent a huge volume of data that is collected from different source of health sector. Because of its’ diversity in nature, quite a big number of attributes, numerous amount data, health informatics can be considered as Big Data. Therefore, different techniques used for analyzing Big Data will also fit for Health Informatics. In recent years, implementation of Data Mining on Health Informatics brings a lot of fruitful outcomes that improve the overall healthcare system both in analyzing disease and improving healthcare services which eventually reduce expenses. This paper will define the term the health informatics with a detail discussion about different source of heath informatics. Finally, some case study will be illustrated as examples where data mining techniques are applied to produce more efficient, in depth outcomes in analyzing disease

Editorial for IEEE access special section on theoretical foundations for big data applications : challenges and opportunities

Big data is one of the hottest research topics in science and technology communities, and it possesses a great application potential in every sector for our society, such as climate, economy, health, social science, and so on. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, and manage. We can conclude that big data is still in its infancy stage, and we will face many unprecedented problems and challenges along the way of this unfolding chapter of human history

Big Data and Its Applications

In times when everything is online, one thing which is common in every application is the use of data. Data is being generated every second, when applications are generating exponentially larger data sets every second it’s the big data which comes into effect. The major objective of this paper is to state the meaning of big data, figure out various ways as how to digest this data. Further this paper will also focus on the applications of Big Data in multiple segments: Finance, Banking and Securities and  Health Care Sector

The Development of Big Data & Artificial Intelligence in the Field of Healthcare——The Case of Ping An Health (Ping An Good Doctor)

This report explores the utilization of big data and artifcial intelligence (AI) in the healthcare sector, focusing on the case of Ping An Health (formerly Ping An Good Doctor) in China. The rapid advancement of Internet technology has propelled the widespread adoption of these technologies across various industries. PingAn Health leverages its platform’s advantages to continuously innovate and enhance user experience, positioning itself at the forefront of the industry. The report delves into Ping An Health’s AI and big data technologies, ofering critical analyses of the ethical, political, and social implications surrounding the company

Leveraging big data tools and technologies: Addressing the challenges of the water quality sector

The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry

A survey of big data and machine learning

This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper

Engaging the public & private sectors in data sharing to improve maternal and newborn health in Uttar Pradesh, India

Background The private for-profit health sector in India delivers around 80% of outpatient treatment and 60% of hospitalisations, and includes more than three quarters of human resources for health. The sector includes solo doctor clinics, small hospitals and big corporate hospital chains, as well as many informal providers. The formal private health sector has grown rapidly without regulatory frameworks and quality assurance. Quality of care is variable and there is lack of adherence to standard treatments, protocols or pricing. Limited information is shared with public health information systems. Aim To develop an engagement strategy with the private for-profit health sector in Uttar Pradesh, India. The broader underlying goal is to develop and pilot a district level Data Informed Platform for Health (DIPH) for improved local health decision-making in maternal and child health including both the public and private health sectors. Methods We reviewed literature, and examined national plans and programme documents to identify lessons from successful public-private engagements for maternal and child health and collate key policies related to the private health sector in India. We sought inputs from 27 national, state and district level stakeholders for developing a strategy to engage with the private sector for a DIPH. Findings In India, public-private partnerships for service delivery and financing represent a key area of engagement with the private sector, especially for maternal and child health. Examples include the Merrygold network, a clinical social franchise, and the Sambhav voucher scheme, in which poor households can exchange vouchers for health services in selected city hospitals in Uttar Pradesh. Engagements related to data recording and reporting from the private health sector have been less successful. There are gaps in reporting even notifiable diseases like Tuberculosis. There is limited data available on the private sector at the national level. Legal provisions can facilitate data exchange and synthesis: a binding legal framework may be available when the Clinical Establishments Act, passed by the Indian Parliament in 2010, is implemented. Proposed engagement strategies Stakeholder consultations suggested that before the Clinical Establishments Act is implemented, the private sector might best be engaged by: 1.Relationship building among key private and public sector stakeholders. 2.Sensitisation of private and public sector groups and individuals with the concept of a DIPH. 3.Inclusion of selected private sector players in the DIPH 4.User-friendly data collection and management. 5.Provision of both financial and non-financial incentives to encourag