The Impact of Big Data on Health Economics: Opportunities and Applications

Big Data has transformed the field of health economics, providing researchers with an unprecedented level of data and insights that can inform healthcare policy and practice. In this study, we explored the opportunities and applications of Big Data in health economics, examining its potential to improve healthcare delivery, reduce costs, and promote better health outcomes. Our findings suggest that Big Data has significant potential to transform thne field of health economics. By using predictive analytics, health economists can identify patterns and trends in healthcare utilization, cost, and outcomes, which can inform the design and implementation of more effective and cost-efficient interventions. Additionally, Big Data can be used to develop personalized treatment plans that are tailored to an individual's specific needs, reducing healthcare costs and improving patient outcomes. Furthermore, Big Data can be used to monitor and manage population health by identifying high-risk individuals, predicting disease outbreaks, and developing strategies to prevent and manage chronic conditions. Health economists can also use Big Data to evaluate the impact of health policy interventions, such as Medicaid expansion and value-based care, and inform future policy decisions. Our study demonstrates that Big Data presents numerous opportunities for health economists to improve healthcare delivery, reduce costs, and promote better health outcomes. By leveraging the power of Big Data, health economists can develop new insights and strategies that can transform the field of health economics and benefit patients, providers, and policymakers alike

A (digital) finger on the pulse

Complex Event Processing (CEP) is a computer-based technique used to track, analyse and process data in real-time (as an event happens). It establishes correlations between streams of information and matches to defined behaviour

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Deriving Value from Big Data Analytics in Healthcare: A Value-focused Thinking Approach

With the potential to generate more insights from data than ever before, big data analytics has become highly valuable to many industries, especially healthcare. Big data analytics can make important contributions to many areas, such as enhancements in the quality of patient care and improvements in operational efficiencies. Big data analytics provides opportunities to address concerns such as disease diagnoses and prevention. However, it has posed challenges such as data security and privacy issues. Also, healthcare institutions have concerns about deriving the greatest benefit from their big data analytics endeavors. Therefore, identifying actionable objectives that can help healthcare organizations derive the maximum value from big data analytics is needed. Using the value-focused thinking (VFT) approach, we interviewed individuals associated with data analytics in healthcare to identify actionable objectives that one needs to consider to derive value from big data analytics, which practitioners can use for their own endeavors and provide opportunities for future research

Developing a Big Data-Enabled Transformation Model in Healthcare: A Practice Based View

Healthcare organizations are looking for opportunities to create new business model and value that can be implemented through information technology (IT) enabled transformation. Big data, an overwhelming phenomenon which has been addressed through various new and old data management technologies, hold the key to healthcare transformation. To address this, we developed a big-data-enabled transformation model based on practice-based view showing that the relationships among big data capability, big-data-enabled transformation practice, benefit dimensions, and firm performance. We tested this model by analyzing secondary data regarding big data in the healthcare context. Our results not only conceptually defined four big data capabilities but also found two significant path-to-performance chains. The contributions of this study are twofold. For management research, we establish a big-data-enabled transformation model to explain how big data leads to firm performance. For practitioners, we identify potential patterns that will help understanding big data\u27s potentials and capabilities

Big data analytics in healthcare: promise and potential

Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome

From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived