Assessing the Understandability and Actionability of Online Neurosurgical Patient Education Materials

Ramos, Christian Lopez; Williams, Jonathan; Bababekov, Yanik J.; Chang, David C.; Carter, Bob S.; Jones, Pamela S.

doi:10.1016/j.wneu.2019.06.166

Cited by 30 publications

(35 citation statements)

References 30 publications

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“…Priyanka et al found that PEMAT scores were consistent with poor understandability (median score, 64%; interquartile range, 64–73) (Priyanka et al, 2018). Overall, we detected a low PEMAT score in most studies with a similar design (Doruk, Enver, Çaytemel, Azezli, & Başaran, 2018; Lopez Ramos et al, 2019). However, unlike most other studies, the PEMAT score in our study was high.…”

Section: Discussionsupporting

confidence: 54%

Readability, understandability, and quality of retinopathy of prematurity information on the web

Yılmaz

Tutar

Arslan

et al. 2021

Birth Defects Research

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Background The purpose of this study is to assess the readability, understandability, and quality of information on retinopathy of prematurity presented at websites frequently visited by parents. Methods A total of 220 websites were assessed, which were recruited by searching for “retinopathy of prematurity” at the Google search engine. The readability of each web page was assessed by Flesch Reading Ease Score, Gobbledygook's Gunning Frequency, Flesch Kincaid Grade Level, Coleman Liau score, The Simple Measure of Gobbledygook, Fry Graph Readability Formula, and Automated readability score. The understandability of the web pages included in the study was measured by using the Patient Education Materials Assessment Tool. Quality was evaluated using Health in Net code and JAMA. The ALEXA traffic tool was used to reference the domains' popularity and visibility. Results Sixty‐four websites were included to the study. The average Flesch Reading Ease Score was 50.1 ± 11.4, Gunning Frequency of Gobbledygook level was 13.4 ± 2.5, The Flesch–Kincaid Grade level was 10.7 ± 2.2, Coleman Liau level was 10.8 ± 1.7, Simple Measure of Gobbledygook level was 10.0 ± 1.9, and Fry Graph Readability Formula 11.9 ± 2.7, Automated readability score 10.4 ± 2.5. The average understandability score for all website‐based patient education materials was 76.9 ± 15.2. Total JAMA Benchmark score is 2.27 ± 1.14 (range from 1 to 4). The quality of information at most websites were determined by our chosen assessments to not to be good. Conclusion Websites addressed to parents for retinopathy of prematurity had found to have high understandability. It was concluded based on this study that readability and quality of presented written materials at online sources need to be improved.

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Section: Discussionsupporting

confidence: 54%

Readability, understandability, and quality of retinopathy of prematurity information on the web

Yılmaz

Tutar

Arslan

et al. 2021

Birth Defects Research

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“…Previous studies assessing the readability and actionability of patient education materials on multiple health topics report low scores on readability, actionability or both. [31][32][33] The results of our study, showing low scores on readability and actionability of the current education materials delivered by nurses, are in line with these results. After evaluation using the PEMAT and optimization of the current education materials, the readability and actionability increased largely.…”

Section: Discussionsupporting

confidence: 89%

Health literacy and optimizing education materials in a surgical population

Dijkman¹,

Leenen²,

Koorn³

et al. 2020

JNEP

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Objective: The aim is to examine and compare the level of health literacy (HL) amongst surgical vascular and abdominal patients and measuring the understandability and actionability of current and optimized education materials.Methods: A cross-sectional design was utilized. Patients undergoing abdominal or vascular surgery, were included for measuring HL with the Newest Vital Sign Dutch (NVS-d) tool. The Dutch version of the Patient Education Materials Assessment Tool (PEMAT) was used to measure the understandability and actionability of current and optimized patient education materials.Results: A total of 101 patients were included, of those 54 (53.5%) have limited HL. Patients with limited HL were significantly older (p < .001), lower educated (p < .001), and had a higher ASA status (p = .005) and Charlson Comorbidity Index score (p < .001). The occurrence of limited HL differed significantly (p = .046) between abdominal versus vascular patients. The understandability varied between 24%-59% and the actionability between 40%-67% of the current education materials. The optimized education materials had a understandability score of 86% and a actionability score of 100%.Conclusions: The high prevalence of inadequate HL emphasizes the importance of nursing and medical staff providing clear information to enable shared decision-making. Besides, it is necessary to evaluate current education materials and optimize these materials according to the level of health literacy to provide health information that is understandable.

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“…Typical words of X9 included effective, successful, lose, achieve, gains, go wrong, overcome, solve, cope, and competent. The complexity of actions is another important feature of health education reading [42,43]. In children's health readings, simple actions and verbs describing the direction of movements were prevalent-typical words in M1 were moving, coming, and going, get, follow, step, and steps.…”

Section: Feature Selection Resultsmentioning

confidence: 99%

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Xie¹,

Ji²,

Liu³

et al. 2021

JMIR Med Inform

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Background Medical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education. Objective Using authoritative and highly influential web-based children’s health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children’s health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public. Methods The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline. Results Using five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI −0.016 to 0.1929), mean specificity (P=.02; 95% CI −0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI −0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI −0.1699 to 0.0681), mean specificity (P=.10; 95% CI −0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI −0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets. Conclusions We developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children’s health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

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Assessing the Understandability and Actionability of Online Neurosurgical Patient Education Materials

Cited by 30 publications

References 30 publications

Readability, understandability, and quality of retinopathy of prematurity information on the web

Readability, understandability, and quality of retinopathy of prematurity information on the web

Health literacy and optimizing education materials in a surgical population

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

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