Big Data and Predictive Analytics: Recalibrating Expectations

By N. Shah, Ewout W. Steyerberg, D. Kent. 2018

228 citations recorded by the source corpus.

With the routine use of electronic health records (EHRs) in hospitals, health systems, and physician practices, there has been rapid growth in the availability of health care data over the last decade. In addition to the structured data in EHRs, new methods such as natural language processing can derive meaning from unstructured data, permitting the capture of substantial clinical information embedded in clinical notes. Furthermore, the growth in the availability of registries and claims data and the linkages between all these data sources have created a big data platform in health care, vast in both size and scope. Concurrently, new computational machine learning approaches promise ever-more-accurate prediction. The marvel of Google and of Watson, the inexorability of Moore’s law (ie, computing power doubles every 2 years for the same cost), suggest a future in which medicine will be transformed into an information science, and each clinical decision may be optimized based on a forecasting of outcomes under alternative treatment options, beyond the knowledge and understanding of the individual physician. Yet despite these innovations and those to come, quantitative risk prediction in medicine has been available for several decades, based on more classical

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

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Citations in the corpus

Learning Optimized Risk Scores (2016, background): “…the predicted risk to choose between multiple treatment options (i.e., patients may be prescribed different medication based on their predicted risk; see also Van Calster and Vickers, 2015; Shah et al., 2018, for a discussion on how miscalibrated risk predictions can result in harmful decisions).”
Calibration: the Achilles heel of predictive analytics (2019, background): “Calibration has therefore been labeled the ‘ Achilles heel ’ of predictive analytics [11].”