New book highlights the role of data in improving healthcare

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RTI-HS researcher co-authors chapter on estimating uncertainty of test results

RESEARCH TRIANGLE PARK, N.C. — A new book, with a chapter co-authored by a senior researcher at RTI Health Solutions, brings together well-known international experts to shed light on the use of cutting-edge approaches to improve the healthcare industry. Emphasizing healthcare data from a statistical and operational management perspective, the book, titled Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, details how advancements in data have improved the delivery of healthcare, and ways analytical methods and tools can help providers increase efficiency and promote quality. 

Varun Ramamohan, Ph.D., Senior Research Health Economist at RTI Health Solutions, a business unit of RTI International, co-authored a chapter on modeling and potentially improving clinical measurement processes titled "Modeling and Simulation of Measurement Uncertainty in Clinical Laboratories." 

Estimating the uncertainty of the results of a lab test is important because it provides quantitative information about the quality of the measurement process. This in turn is vital because clinical laboratory test results inform every stage of the medical decision making process. In their chapter, Ramamohan and his coauthors describe the methods for developing mathematical models and using computer simulation to estimate, analyze, and minimize the uncertainty associated with the clinical laboratory measurement process.

"We have presented our methodology for the modeling, estimation, and analysis of the uncertainty associated with clinical measurement processes in previously published work in this area," Ramamohan said. "So now we are excited to get the opportunity to summarize and communicate that research to a wider audience with this book chapter."

The chapter begins with background information regarding methods for estimation of uncertainty associated with clinical laboratory measurement processes and provides  broad guidelines for the development of measurement uncertainty models. As an example, the authors illustrate these guidelines by developing a model of the uncertainty associated with the clinical measurement of a particular enzyme. The authors reason that such models can serve as an alternative or aid to conducting controlled experiments in the laboratory.

"We hope that this chapter can provide interested readers with an introduction to the field, and that instrument manufacturers and clinicians will consider our method in their efforts to improve measurement processes in the clinical laboratory," said Ramamohan.

Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, published by Wiley, is now available on leading commercial bookseller websites.

ISBN: 978-1-118-91939-2

 

 

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