Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations

By Yichuan Wang, LeeAnn Kung, Terry Anthony Byrd. 2018

1,377 citations recorded by the source corpus.

To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.

Subfields: Health Information Management, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/16575f23ff879e6353a55bbfbbcc54e27606bfc5

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Citations in the corpus

Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, background): “Although these questions have not yet been studied, the prevalent belief is that “big data is better data” (Chen, Chiang, & Storey, 2012; McAfee, Brynjolfsson, Davenport, Patil, & Barton, 2012; Wang, Kung, & Byrd, 2018).”
Role of big data analytics in developing sustainable capabilities (2019, background): “The routinization stage is related to a firm's governance system that enables the incorporation of the technologies within the processes (Wang et al., 2018; Gunasekaran et al., 2017; Zmud and Apple, 1992).”
Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, background): “To optimize the potential of AI, very large datasets are ideal (e.g., electronic health records; EHRs) that can be analyzed computationally, revealing trends and associations regarding human behaviors and patterns [21] that are often hard for humans to extract.”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, neutral): “…role of BDAC’s in enhancing incremental innovation capabilities can be discerned in several examples such as alterations to products and services (Y. Wang, Kung, & Byrd, 2018), personalization of offered marketing approaches and services (Buettner, 2017; Xu, Frankwick, & Ramirez, 2016), changes…”
Developing a unified definition of digital transformation (2021, background): “For instance, while some authors view DT as a slight technology-enabled change such as implementing a new ERP System (S. Chanias, 2017), others believe that DT is a more radical and evolutionary process that takes place over time (Janowski, 2015; Loebbecke and Picot, 2015; Wang et al., 2018).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, positive): “Therefore, even though research has broadly discussed characteristics and the impact of BDPA on performance (organizational or operational performance) (see Gupta and George, 2016; Ren et al., 2017; Gunasekaran et al., 2017; Fosso Wamba et al., 2017; Srinivas and Swink, 2018; Wang et al., 2018; Aydi”
Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective (2020, positive): “[120] enumerated a number of capabilities of big data analytics in healthcare sector, particularly for decision support capability, analytical capability for pattern of care, predictive capability, unstructured data analysis capability and traceability.”
Concurrence of big data analytics and healthcare: A systematic review (2018, background): “This is critical for healthcare, because incorrect interpretation of the reports generated could lead to unanticipated consequences [27].”