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Predictive Analytics to Support Clinical Trials Get Healthier

Predictive Analytics to Support Clinical Trials Get Healthier

Ankit Lodha, Anvita Karara
Copyright: © 2018 |Pages: 18
ISBN13: 9781522529477|ISBN10: 1522529470|EISBN13: 9781522529484
DOI: 10.4018/978-1-5225-2947-7.ch016
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MLA

Lodha, Ankit, and Anvita Karara. "Predictive Analytics to Support Clinical Trials Get Healthier." Exploring the Convergence of Big Data and the Internet of Things, edited by A.V. Krishna Prasad, IGI Global, 2018, pp. 222-239. https://doi.org/10.4018/978-1-5225-2947-7.ch016

APA

Lodha, A. & Karara, A. (2018). Predictive Analytics to Support Clinical Trials Get Healthier. In A. Prasad (Ed.), Exploring the Convergence of Big Data and the Internet of Things (pp. 222-239). IGI Global. https://doi.org/10.4018/978-1-5225-2947-7.ch016

Chicago

Lodha, Ankit, and Anvita Karara. "Predictive Analytics to Support Clinical Trials Get Healthier." In Exploring the Convergence of Big Data and the Internet of Things, edited by A.V. Krishna Prasad, 222-239. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2947-7.ch016

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Abstract

The concept of clinical big data analytics is simply the joining of two or more previously disparate sources of information, structured in such a way that insights are prescribed from examination of the new expanded data set. The combination with Internet of Things (IoT), can provide multivariate data, if healthcare organizations build the infrastructure to accept it. Many providers are able to integrate financial and utilization data to create a portrait of organizational operations, but these sources do not give a clear idea of what patients do on their own time. Embracing the centrality of the IoT would relinquish the idea that provider is the only pillar around which healthcare revolves. This chapter provides deeper insights into the four major challenges: costly protocol amendments, increasing protocol complexity and investigator site burden. It also provides recommendations for streamlining clinical trials by following a two dimension approach-optimization at a program level (clinical development plan) as well as at the individual trial candidate level.

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