Clinicians, patients agree: Include obesity-related information in radiology reports

Clinicians and patients both prefer to have obesity-related information included in radiology reports, according to a new study published in Clinical Radiology.

The authors developed two different surveys, one for patients and one for hospital clinicians who refer patients to radiologists. Both surveys included questions asking if obesity should be described on a radiology report; this was important to ask, the authors explained, due to the “unique social stigma” associated with the subject.Respondents were asked to answer on a scale from one to seven, with one meaning “strongly disagree” and seven meaning “strongly agree.” The clinician mean answer was a 5.9, and the patient mean answer was a 5.8.

In addition, more than 72 percent of clinicians and more than 64 percent of clinicians said they would prefer a quantitative report. More than 13 percent of clinicians and 25 percent of patients said they prefer a qualitative report “simply indicating the presence or absence of obesity.”

“Despite the unique social factors relating to obesity, this study suggests clinicians and patients desire obesity-related information on radiology reports, and would find the information helpful in informing clinical decisions, and in facilitating the discussion of obesity with individual patients,” wrote lead author T.E. Murray, MB, MCh, MRCS, FFR, department of radiology at Beaumont Hospital in Dublin, Ireland, and colleagues.

Murray and colleagues added that it is becoming easier and easier for specialists to measure obesity.

“Using freely available software, levels of adiposity can be quantified using a variety of techniques,” the authors wrote. “Measuring cross-sectional area of abdominal visceral fat at the level of a specific lumbar vertebra at computed tomography (CT) is the most widely employed technique; however, other measurements and methods may be used, and comparison can be made with available gender- and race-specific ranges. As computational power and machine learning progress, it is likely that this will become a widespread and automated feature of future radiology systems, along with a host of other epidemiological data points.”