Readability Analysis of the American Society of Ophthalmic Plastic & Reconstructive Surgery Patient Educational Brochures

ABSTRACT

Introduction: Previous studies have shown patient education material (PEM) in ophthalmology has been written at levels exceeding appropriate reading levels. However, information for readability in the field of oculoplastics remains limited. The aim of this study was to evaluate the readability of patient educational brochures from the American Society of Ophthalmic Plastic & Reconstructive Surgery (ASOPRS).

Methods: Patient educational brochures from ASOPRS were analyzed for readability. The body of text from all 18 ASOPRS patient brochures was analyzed by ten validated tests for English readability assessment: Flesch Reading Ease Test (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), Coleman-Liau Index (CLI), Gunning Fog Index (GFI), New Dale-Chall Readability (NDC), FORCAST, Fry Graph Readability (FG), Raygor Readability Estimate (RRE), and New Fog Count (NFC).

Results: The mean (± SD) readability scores from the 18 ASOPRS patient brochures were 48 (4.3), 11.0 (0.8), 13.0 (0.7), 11.7 (0.8), 13.6 (0.9), 11.3 (0.8), 11.1 (0.5), 12.1 (1.5), 12.2 (1.0), and 10.6 (1.3) for FRE, FKGL, SMOG, CLI, GFI, NDC, FORCAST, FG, RRE, and NFC, respectively. All ten of the mean readability scores were above the recommended reading levels.

Conclusions: These findings show that the average patient may have difficulty understanding educational information provided by ASOPRS patient brochures, thereby hindering their ability to make informed decisions on their healthcare. Revision with readability as a primary goal, with input from patients and caregivers, may be necessary to improve health literacy among patients who seek oculoplastic care.

KEYWORDS:

• Patient education
• readability
• Health literacy
• oculoplastic

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

AUTHOR CONTRIBUTIONS

Haig Pakhchanian: Conceptualization, data curation, data analysis, writing.

Melissa Yuan: Data curation, data analysis, writing-review & editing.

Rahul Raiker: Conceptualization, data analysis, writing-review & editing.

Shanawar Waris: Data curation, data analysis, writing.

Craig Geist: Conceptualization, supervision, data analysis, writing-review & editing

Additional information

Funding

The authors received no financial support or grants for the research, authorship, and publication of this article.