Language matters, especially in health care. Clinicians rely on patient characteristics to construct a compelling and accurate picture, such as the age, sex, or symptoms in a 25-year-old woman with lower abdominal pain.
But derogatory expressions are also sometimes used to describe patients. Terms like “poor historian,” “noncompliant,” or “no-show” routinely appear in clinical medicine, reflecting and reinforcing negative stereotypes. Recent research found that stigmatizing language is surprisingly common, across a wide swath of clinical settings, including outpatient clinics, emergency rooms, and inpatient hospital stays. The question is, how common is stigmatizing language in home health care, one of the fastest-growing outpatient settings? And how does it affect patient care?
In a new Journal of Medical Internet Research (JMIR) Nursing study, LDI senior fellow Kathryn Bowles and colleagues, including Maxim Topaz, a Penn Nursing Ph.D. graduate, used machine learning to detect patterns in “judgment language,” one metric of stigmatizing words, in the notes of urban home health care clinicians. Drawing from data collected by a private company, the authors analyzed clinicians’ perceptions of a patient’s reliability (e.g., the patient “states,” “claims,” “admits”).
In a cohort of 45,384 patients, researchers examined over 260,000 patient notes. Applying their algorithm, Bowles and colleagues found that 10% of all notes included judgment language. More specifically, this language was most common among Hispanic and Black patients, followed by white and then Asian patients. In fact, Black and Hispanic patients were 14% more likely to have judgment language present in their notes than white patients.
Crucially, the authors observed that the “length of a home health care visit was reduced by 21 minutes when judgment language was used.”
“This is concerning,” the authors write, “since shorter home health care visits are associated with a higher risk for poor outcomes,” such as a higher risk of hospitalizations.
Recent scholarship found that negative language was often correlated with a patient’s race and ethnicity. Substantive evidence demonstrated that Black patients were 25-50% more likely to have stigmatizing language used than white patients in electronic health records across many settings.
This story is by Sam Schotland. Read more at Penn LDI.
