Although scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID-19 pandemic In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study 'black swan' events such as the COVID-19-related global crisis and its aftermath's implications for managers and policymakers

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-8551.12441

COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1978&context=jais

Research Perspectives: The Anatomy of a Design Principle

The current study performs a systematic literature review (SLR) to synthesise prior research on the  applicability of big data analytics (BDA) in healthcare. The SLR examines the outcomes of 41 stu...

https://www.tandfonline.com/doi/pdf/10.1080/17517575.2020.1812005

Big data analytics in healthcare: a systematic literature review

Data analytics and mining for biomedical decision support; New computational platforms and models for biomedicine; Intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks; Intelligent devices and instruments; Automated reasoning and meta-reasoning in medicine; Machine learning in medicine, medically-oriented human biology, and healthcare; AI and data science in medicine, medically-oriented human biology, and healthcare; AI-based modeling and management of healthcare pathways and clinical guidelines; Models and systems for AI-based population health; AI in medical and healthcare education; Methodological, philosophical, ethical, and social issues of AI in healthcare, medically-oriented human biology, and medicine.

https://www.nejm.org/doi/pdf/10.1056/NEJMe2206291?articleTools=true

Artificial Intelligence in Medicine

Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness

Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models—one for each event—and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.

Healthcare predictive analytics for risk profiling in chronic care: a Bayesian multitask learning approach

While recent research has suggested the tremendous potential of electronic health records (EHR) to transform healthcare, there remains a limited understanding of the best ways to utilize EHR data to improve clinical decision-making. Healthcare analytics based on EHR data may be able to offer a solution to the challenging goal of providing effective clinical decision support in chronic care. This paper takes a first step towards data-driven, evidence-based healthcare analytics in information systems research. Following the paradigms of design science and predictive analytics research, we propose, demonstrate and evaluate a design framework of risk prediction in the context of chronic disease management. Our framework draws on a large longitudinal real-world EHR dataset and evidence based guidelines to support data- and science-driven clinical decision making. We choose diabetes and coronary heart disease as our experimental cases, each with thousands of patients in their respective cohorts. The results of the experiments suggest that our design can achieve an accurate and reliable predictive performance and that the design is generalizable across chronic diseases. The design artifact and the experimental results contribute to the IS knowledge base and provide important theoretical and practical implications for design science, predictive analytics, and health IT research.

Healthcare Analytics and Clinical Intelligence: A Risk Prediction Framework for Chronic Care

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Healthcare predictive analytics for risk profiling in chronic care: a Bayesian multitask learning approach

Healthcare Analytics and Clinical Intelligence: A Risk Prediction Framework for Chronic Care