Using the Wrong Tools in Health Research

‘Although it is inappropriate, and potentially inaccurate, researchers frequently use linear regression on nonlinear phenomena, calculus on discontinuous functions, or χ2 when data points are interdependent.’—Eric Dent PhD, 1999
The principles of systems science are clear: We’re using research tools designed for static, isolated, linear and mechanical systems, but human beings are nonlinear, adaptive, biologic and heavily influenced by interactions with the rest of our constantly changing world.
In his book “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die,” Eric Siegel describes the story of IBM Watson’s victory in the game Jeopardy against its two greatest human champions at the time.4 He said it is one of the best examples of machine learning and the potential for wonderful benefits if applied appropriately to health care. But he is clear in the need for an accurate context that produces the data and the need for multiple collaborations to share what is learned from the data.
In health care, we have a very poor understanding of the context that produces the data available that we see in fragmented care, often primarily from coding and billing data. This data is incredibly inaccurate. Some estimate that nearly 50% of billing data is wrong or omitted, with the great majority from human and systems error, not fraud.5
But when the application of the principles of systems science is done well, and Siegel gives many examples in his book, then the use of a well-defined context and meaningful collaboration can prevent the law of diminishing returns, or as Siegel calls it in his book, “overlearning,” which can allow for continuous improvement of value over time.