1010 - Development of a Program Utilizing the Simple Measure of Gobbledygook (SMOG) Algorithm for Comprehending Patient-Generated Educational Content in the Field of Radiation Oncology

W. G. Chin¹, and A. B. Patel Jr²; ¹Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, ²Winship Cancer Institute at Emory University, Atlanta, GA

Purpose/Objective(s): Radiation therapy is administered to more than half of all patients diagnosed with malignancies. Patient education holds a pivotal position in the care plan, as a well-informed and engaged patient demonstrates improved adherence to therapy and instructions. Our institution decided to redesign the education material provided at consultation and discontinue use of open-source materials from the NCCN. To further enhance patient education, we employed a user-developed algorithm that utilizes a well-established readability index score, the Simple Measure of Gobbledygook (SMOG). This algorithm aids in creating patient education materials that are both informative and comprehensible. Materials/

Methods: We used the R statistical software program to develop a simple algorithm that could be invoked within the POSIT(RStudio) program. R-Markdown code was employed to assign a working directory where a corpus of text data was loaded. The algorithm employed Quanteda: Quantitative Analysis of Textual Data, to calculate the readability index score using SMOG (McLaughlin 1969). The algorithm was used to assess the readability index scores for three open source content (NCI Radiation Therapy and You, ASCO Answers Radiation Therapy, and ASTRO Radiation Therapy For Cancer). Subsequently, we tailored patient education materials to suit the needs of individuals navigating their cancer journey within our institution. This endeavor involved a collaborative effort, drawing on the expertise of nurses, radiation therapists, medical physicists, and attending providers to shape the content. Finally, the algorithm was applied to refine the readability index of the synthesized educational materials, aiming for a reading level equivalent to ninth grade. The results of the text analysis were saved as a data object.
Results:
Conclusion: Often, we write content assuming that each patient can comprehend complex processes and technical terms. Moreover, this text is rarely, if ever, put to the test. Countless hours are invested in producing dense material that is probably destined to remain unused and unread. This effort delineates a way to potentially address one of these challenges by employing a mechanism that can effortlessly analyze text and provide a readability index score.

Document: Title	SMOG Reading Grade Level	Tokens	Sentences	Pages
EMORY: Radiation Therapy	8.38	2738	207	35
NCI: Radiation Therapy and You	11.17	6360	340	54
ASCO: answers Radiation Therapy	13.28	1331	83	2
ASTRO: Radiation Therapy for Cancer	14.75	5594	272	36