Figure Interpretation Assessment Tool (FIAT)

Last modified: September 20, 2018

What’s in a number?

In healthcare, numerous reports are generated with the aim of enhancing care. However, often these numbers are the result of a long process of interpretations of summaries, press- and news messages. By summarizing information into numbers, nuances may get lost, leading to a possible misinterpretation of these numbers. For instance, which effect does a choice in life style really have on your health (e.g. physical activity, diet, use of alcohol)?

Figure Interpretation Assessment Tool

As a solution, the Dutch National Institute for Public Health (RIVM) has asked researchers from the Amsterdam Medical Center (AMC) to develop a tool that supports healthcare workers in the interpretation of numbers prevalent in healthcare reports. The resulting FIAT-Health tool is a questionnaire that aspires to make the user aware of all the points of attention that need to be evaluated in order to verify whether or not a number is trustworthy and to clarify what the number means. As a result, the tool will allow the public, policy advisors and researchers to critically assess statistical information presented by research institutes and media, and develop a better understanding of a published figure. The questionnaire can be found online on the FIAT-Health website and consists of 15 questions. Currently the tool is only available in Dutch.

The research behind the tool

“Ideally, people base their decisions on the best available evidence, retrieving the figures which support their thinking directly from the source in which the figures are initially published; however, this is often not the case.” Key characteristics of figures on health(care) were identified through systematic expert consultations in the Netherlands on four topic categories: morbidity, healthcare expenditure, healthcare outcomes and lifestyle. They were used for the development of the FIAT. Researchers identified characteristics that are relevant for the interpretation: e.g. figures’ origin, credibility, subject matter, population and geographical focus, time period, and underlying data collection methods. (source: Health Research Policy and Systems)

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