Journal Article

Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study

Jordan, Harmon S, Lalor, John P, Smirnova, Jenni Kim, Hu, Wen, Yu, Hong, Levy, David A

2024

Overview

Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients. This study aims to determine whether experts and laypeople agree on what constitutes medical jargon. Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon. The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms. To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

• Publisher: Journal of Medical Internet Research,Gunther Eysenbach MD MPH, Associate Professor,JMIR Publications
• Subject: Adult / Agreements / Analysis / Attitudes / Averages / Communication / Comparative analysis / Computational linguistics / Data collection / Demography / Dictionaries / Electronic health records / Electronic Health Records - statistics & numerical data / Electronic records / Ethnicity / Experts / Female / Gastrointestinal surgery / Grammatical agreement / Health education / Health literacy / Health Literacy - statistics & numerical data / Humans / Jargon / Language processing / Laypersons / Literacy / Male / Medical dictionaries / Medical informatics / Medical records / Medical research / Medicine, Experimental / Methods / Middle Aged / Native language / Native languages / Natural language interfaces / Observational studies / Original Paper / Patients / Race / Rankings / Sex education / Terminology / Terminology as Topic / United Kingdom / Web portals / Workers