Flesch-Kincaid Research Articles

Decoding medical jargon: The use of AI language models (ChatGPT-4, BARD, microsoft copilot) in radiology reports

ObjectiveEvaluate Artificial Intelligence (AI) language models (ChatGPT-4, BARD, Microsoft Copilot) in simplifying radiology reports, assessing readability, understandability, actionability, and urgency classification. MethodsThis study evaluated the effectiveness of these AI models in translating radiology reports into patient-friendly language and providing understandable and actionable suggestions and urgency classifications. Thirty radiology reports were processed using AI tools, and their outputs were assessed for readability (Flesch Reading Ease, Flesch-Kincaid Grade Level), understandability (PEMAT), and the accuracy of urgency classification. ANOVA and Chi-Square tests were performed to compare the models' performances. ResultsAll three AI models successfully transformed medical jargon into more accessible language, with BARD showing superior readability scores. In terms of understandability, all models achieved scores above 70%, with ChatGPT-4 and BARD leading (p < 0.001, both). However, the AI models varied in accuracy of urgency recommendations, with no significant statistical difference (p = 0.284). ConclusionAI language models have proven effective in simplifying radiology reports, thereby potentially improving patient comprehension and engagement in their health decisions. However, their accuracy in assessing the urgency of medical conditions based on radiology reports suggests a need for further refinement. Practice implicationsIncorporating AI in radiology communication can empower patients, but further development is crucial for comprehensive and actionable patient support.

Identifying ChatGPT-written Patient Education Materials Using Text Analysis and Readability.

Artificial intelligence (AI)-based text generators such as Chat Generative Pre-Trained Transformer (ChatGPT) have come into the forefront of modern medicine. Given the similarity between AI-generated and human-composed text, tools need to be developed to quickly differentiate the two. Previous work has shown that simple grammatical analysis can reliably differentiate AI-generated text from human-written text. In this study, ChatGPT was used to generate 25 articles related to obstetric topics similar to those made by the American College of Obstetrics and Gynecology (ACOG). All articles were geared towards patient education. These AI-generated articles were then analyzed for their readability and grammar using validated scoring systems and compared to real articles from ACOG. Characteristics of the 25 AI-generated articles included fewer overall characters than original articles (mean 3,066 vs. 7,426; p < 0.0001), a greater average word length (mean 5.3 vs. 4.8; p < 0.0001), and a lower Flesch-Kincaid score (mean 46 vs. 59; p < 0.0001). With this knowledge, a new scoring system was develop to score articles based on their Flesch-Kincaid readability score, number of total characters, and average word length. This novel scoring system was tested on 17 new AI-generated articles related to obstetrics and 7 articles from ACOG, and was able to differentiate between AI-generated articles and human-written articles with a sensitivity of 94.1% and specificity of 100% (Area Under the Curve [AUC] 0.99). As ChatGPT is more widely integrated into medicine, it will be important for health care stakeholders to have tools to separate originally written documents from those generated by AI. While more robust analyses may be required to determine the authenticity of articles written by complex AI technology in the future, simple grammatical analysis can accurately characterize current AI-generated texts with a high degree of sensitivity and specificity. · More tools are needed to identify AI-generated text in obstetrics, for both doctors and patients.. · Grammatical analysis is quick and easily done.. · Grammatical analysis is a feasible and accurate way to identify AI-generated text..

Pregnancy Loss: A Comparative Analysis of Information Delivery Between ACOG FAQs and ChatGPT [ID 2683594

INTRODUCTION: Pregnancy loss is a sensitive topic, and clear, reliable information is crucial. This study compares the readability and accuracy of ChatGPT's responses to ACOG's frequently asked questions (FAQ) sheets on the subject. METHODS: Within the five FAQ sheets under “Pregnancy Loss,” 66 questions were assessed using ChatGPT-3.5. Readability scores, ie, Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, Gunning Fog Score, Smog Index, Coleman Liau Index, and Automated Readability Index were computed for each response. The quality of responses were also graded by two maternal–fetal medicine specialists using a 1–4 scale, where 1 represents a comprehensive response and 4 indicates an incorrect response. Statistical analysis utilized a two-tailed t-test. A weighted Cohen’s kappa coefficient evaluated interrater reliability. RESULTS: ACOG attained better readability scores over ChatGPT across all six metrics with statistical significance (P&lt;.001). Among grading, ACOG also outperformed ChatGPT in quality of information with a summative mean of 1.53 (SD 0.60) versus 1.85 (SD 0.90) with statistical significance (P&lt;.001). Grading for ACOG possessed a Cohen's kappa coefficient of 0.493, implying moderate agreement between graders. Grading for ChatGPT possessed a Cohen's kappa coefficient of 0.744, implying substantial agreement between graders. CONCLUSION: The responses by ACOG to frequently asked questions on pregnancy loss were both clearer and more comprehensive than those of ChatGPT with consistent agreement between graders. Although ChatGPT can be a convenient source of medical queries, further improvements ought to be made to these large-language models to provide clear and accurate information regarding pregnancy loss to patients.

Comparative Readability Assessment of Four Large Language Models in Answers to Common Contraception Questions [ID 2683638

INTRODUCTION: Large language models (LLMs) have the potential to revolutionize reproductive health education by offering accessible contraception counseling. The CDC, NIH, and AMA recommend presenting patient educational materials at or below a sixth-grade reading level. In this study, we present a comparative analysis of the readability of responses generated by LLMs, including OpenAI's ChatGPT 4.0, OpenAI's ChatGPT 3.5, Google Bard, and Microsoft Bing, to common contraception questions. METHODS: Referencing common contraception questions outlined in a recent review, we presented GPT-4.0, GPT-3.5, Google Bard, and Microsoft Bing with six common contraception questions on June 10, 2023, ensuring no prior chat bias. The readability of LLM outputs was evaluated using Flesch–Kincaid Grade Level (FK), Gunning Fog (GF), Automated Readability Index (ARI), and Coleman–Liau (CL) indices. All index score outputs correspond to a reading grade level (RGL), eg, RGL of 6 represents a sixth-grade level. RESULTS: Of the four LLMs, Google Bard demonstrated the highest readability with an average RGL of 10.6 (GF 10.1, FK 9.6, ARI 11.4, CL 11.4), followed by GPT-3.5 at an average RGL of 13.6 (GF 13.3, FK 12.6, ARI 15.2, CL 16.5). Microsoft Bing and GPT-4.0 yielded responses with higher average RGLs of 14.2 and 15.4, respectively. CONCLUSION: Google Bard and GPT-3.5 offered the most readable responses to contraception questions; however, these models still provided responses significantly above the recommended sixth-grade reading level (RGL of 6). Large language models should undergo evaluation to ensure alignment with established readability standards to optimize patient comprehension in reproductive counseling applications.

Unlocking the future of patient Education: ChatGPT vs. LexiComp® as sources of patient education materials

BackgroundChatGPT is a conversational artificial intelligence technology that has shown application in various facets of healthcare. With the increased use of AI, it is imperative to assess the accuracy and comprehensibility of AI platforms. ObjectiveThis pilot project aimed to assess the understandability, readability, and accuracy of ChatGPT as a source of medication-related patient education as compared with an evidence-based medicine tertiary reference resource, LexiComp®. MethodsPatient education materials (PEMs) were obtained from ChatGPT and LexiComp® for 8 common medications (albuterol, apixaban, atorvastatin, hydrocodone/acetaminophen, insulin glargine, levofloxacin, omeprazole, and sacubitril/valsartan). PEMs were extracted, blinded, and assessed by 2 investigators independently. The primary outcome was a comparison of the Patient Education Materials Assessment Tool-printable (PEMAT-P). Secondary outcomes included Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, word count, and accuracy. A 7-item accuracy checklist for each medication was generated by expert consensus among pharmacist investigators, with LexiComp® PEMs serving as the control. PEMAT-P interrater reliability was determined via intraclass correlation coefficient (ICC). Flesch reading ease, Flesch Kincaid grade level, percent passive sentences, and word count were calculated by Microsoft® Word®. Continuous data were assessed using the Student’s t-test via SPSS (version 20.0). ResultsNo difference was found in the PEMAT-P understandability score of PEMs produced by ChatGPT versus LexiComp® [77.9% (11.0) vs. 72.5% (2.4), P=0.193]. Reading level was higher with ChatGPT [8.6 (1.2) vs. 5.6 (0.3), P < 0.001). ChatGPT PEMs had a lower percentage of passive sentences and lower word count. The average accuracy score of ChatGPT PEMs was 4.25/7 (61%), with scores ranging from 29% to 86%. ConclusionDespite comparable PEMAT-P scores, ChatGPT PEMs did not meet grade level targets. Lower word count and passive text with ChatGPT PEMs could benefit patients, but the variable accuracy scores prevent routine use of ChatGPT to produce medication-related PEMs at this time.

ChatGPT-3.5 Responses Require Advanced Readability for the General Population and May Not Effectively Supplement Patient-Related Information Provided by the Treating Surgeon Regarding Common Questions About Rotator Cuff Repair

The purpose of this study was to investigate the accuracy of ChatGPT's responses to frequently asked questions (FAQ) prior to rotator cuff repair surgery. The ten most common FAQs related to rotator cuff repair were compiled from four institution websites. Questions were then input into ChatGPT-3.5 in one session. The provided ChatGPT-3.5 responses were analyzed by two orthopedic surgeons for reliability, quality, and readability using The Journal of the American Medical Association (JAMA) Benchmark criteria, the DISCERN score, and the Flesch-Kincaid Grade Level. The JAMA Benchmark criteria score was 0, indicating the absence of reliable source material citations. The mean Flesch-Kincaid Grade Level was 13.4 (range, 11.2-15.0). The mean DISCERN score was 43.4 (range, 36-51) indicating that the quality of the responses overall was considered fair. All responses cited making final decision-making to be made with the treating physician. ChatGPT-3.5 provided substandard patient-related information in alignment with recommendations from the treating surgeon regarding common questions around rotator cuff repair surgery. Additionally, the responses lacked reliable source material citations and the readability of the responses was relatively advanced with a complex language style. The findings of this study suggest that ChatGPT-3.5 may not effectively supplement patient-related information in the context of recommendations provided by the treating surgeon prior to rotator cuff repair surgery.

Examining Accessibility of Freestanding Abortion Clinics for People With Disabilities [ID 2683401

INTRODUCTION: People with disabilities face numerous barriers to equitable health care but have a fundamental right to safe abortion care. This study reviews the accessibility of freestanding abortion clinics in the United States for those with disabilities. METHODS: A quantitative data analysis was performed. All freestanding abortion clinics registered with the Abortion Care Network were included except those directly affiliated with a university or general gynecology clinic and those exclusively operating online. Two independent reviewers analyzed clinic websites after an interrater reliability test. Independent variables analyzed included Flesch Reading Ease score, presence of a statement affirming dignity for all persons, online appointment scheduling, transparent pricing, and financial assistance information. RESULTS: Seventy-two clinics were included. No clinic explicitly stated that their facilities were accessible to individuals with disabilities. Fifty-four percent had a statement affirming dignity for all persons, most of which were in the Northeast or West Coast. 6% had an accessibility widget, and 63% had online appointment scheduling. Mean Flesch Reading Ease score was 55.37 correlating to a 10th- to 12th-grade reading level. 33% had transparent pricing information, and 71% offered information on financial assistance. CONCLUSION: Several accessibility barriers were identified on these websites. Freestanding abortion clinics should focus on enhancing accessibility by explicitly affirming dignity for all patients, providing clear information on facility accessibility, and implementing an accessibility widget, online scheduling, and transparent pricing. Furthermore, these websites should be written at an easily comprehensible reading level. These simple changes would make these clinics more accessible for those with disabilities.

The complexity of online patient education materials for wound care strategies: A readability analysis

BackgroundIn the United States, over 6 million people are affected by chronic wounds. Patients often rely on the Internet for treatment information; however, these educational materials typically exceed the average eighth-grade health literacy level. This study aimed to assess the readability and language accessibility of online patient education materials on wound care strategies. MethodsA search was conducted on Google for articles related to wound care strategies. The first 12 unique websites from each search strategy were selected for further analysis. Readability was assessed using 11 tests, with the mean scores calculated for each. ResultsA total of 66 articles pertaining to wound care strategies were retrieved from 43 websites. All articles had an average reading grade level of 13.5 ± 2.5 and an average reading age of 18.7 ± 2.5 years. Websites were categorized by the following sources: academic (34.9%), reagent/biologic manufacturers (27.9%), wound care (18.6%), news media organizations (14%), and other (4.7%). Flesch Reading Ease Score, graded from 0 for most difficult to 100 for least difficult, was found to be highest for academic websites (44.2, P = .01) and lowest for news media websites (24.9, P = .01). Academic websites were available in the more languages compared to all other website categories (P < .01). ConclusionOnline materials related to wound care strategies often exceed the National Institute of Health recommended eighth-grade reading level. This study emphasizes the need for healthcare providers to create more accessible educational materials to address the gap in health literacy and optimize patient care.

Evaluating Chat Generative Pre-trained Transformer Responses to Common Pediatric In-toeing Questions.

Chat generative pre-trained transformer (ChatGPT) has garnered attention in health care for its potential to reshape patient interactions. As patients increasingly rely on artificial intelligence platforms, concerns about information accuracy arise. In-toeing, a common lower extremity variation, often leads to pediatric orthopaedic referrals despite observation being the primary treatment. Our study aims to assess ChatGPT's responses to pediatric in-toeing questions, contributing to discussions on health care innovation and technology in patient education. We compiled a list of 34 common in-toeing questions from the "Frequently Asked Questions" sections of 9 health care-affiliated websites, identifying 25 as the most encountered. On January 17, 2024, we queried ChatGPT 3.5 in separate sessions and recorded the responses. These 25 questions were posed again on January 21, 2024, to assess its reproducibility. Two pediatric orthopaedic surgeons evaluated responses using a scale of "excellent (no clarification)" to "unsatisfactory (substantial clarification)." Average ratings were used when evaluators' grades were within one level of each other. In discordant cases, the senior author provided a decisive rating. We found 46% of ChatGPT responses were "excellent" and 44% "satisfactory (minimal clarification)." In addition, 8% of cases were "satisfactory (moderate clarification)" and 2% were "unsatisfactory." Questions had appropriate readability, with an average Flesch-Kincaid Grade Level of 4.9 (±2.1). However, ChatGPT's responses were at a collegiate level, averaging 12.7 (±1.4). No significant differences in ratings were observed between question topics. Furthermore, ChatGPT exhibited moderate consistency after repeated queries, evidenced by a Spearman rho coefficient of 0.55 ( P = 0.005). The chatbot appropriately described in-toeing as normal or spontaneously resolving in 62% of responses and consistently recommended evaluation by a health care provider in 100%. The chatbot presented a serviceable, though not perfect, representation of the diagnosis and management of pediatric in-toeing while demonstrating a moderate level of reproducibility in its responses. ChatGPT's utility could be enhanced by improving readability and consistency and incorporating evidence-based guidelines. Level IV-diagnostic.

Changing Health Information on COVID-19 Vaccination in Asia

The informational domain related to COVID-19 reflects the degree of uncertainty and pace of evolution of the pandemic. This places a burden on peoples’ searches for information to guide their choices, importantly including for COVID-19 vaccines. Thus, it is important for health communications that support vaccination campaigns to attenuate vaccine hesitancy to be accessible, including in terms of readability, and adapted to the evolving pandemic. This paper aims to understand internet searches on COVID-19 vaccination, specifically the mix of sources and readability of the sources over a two-year period (2021–2023) in Singapore, Hong Kong, and the Philippines, for search results in English, as English is a main language for each of these locations. The sources accessed through online searches in June 2021 and May 2023 were categorized by type of source and whether they were from one of the focal locations or elsewhere. The readability of information from web-search results was assessed using a set of readability tests (Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, Gunning Fog Index, Coleman–Liau Index, and Simple Measure of Gobbledygook Grade level). Over the two-year period there was an increase in government sources and reduction in mass media sources with distinct local patterns. Local government sources increased in Singapore whereas foreign government and multi-lateral organization sources increased in Hong Kong, with the Philippines being an intermediate pattern. In contrast to the changing mix of sources, the readability tests indicate a low proportion of URLs scoring within recommended readability thresholds across locations and types of sources over the two years. Information on COVID-19 vaccine development and deployment is an important part of health communications that includes internet search. The paper contributes to understanding health communications during a pandemic, including mix of local and non-local sources and contingency on local social and health context.

Mixed methods assessment of the influence of demographics on medical advice of ChatGPT.

To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.

Evaluation of online text-based information resources of gynaecological cancer symptoms.

Gynaecological cancer symptoms are often vague and non-specific. Quality health information is central to timely cancer diagnosis and treatment. The aim of this study was to identify and evaluate the quality of online text-based patient information resources regarding gynaecological cancer symptoms. A targeted website search and Google search were conducted to identify health information resources published by the Australian government and non-government health organisations. Resources were classified by topic (gynaecological health, gynaecological cancers, cancer, general health); assessed for reading level (Simple Measure of Gobbledygook, SMOG) and difficulty (Flesch Reading Ease, FRE); understandability and actionability (Patient Education Materials Assessment Tool, PEMAT, 0-100), whereby higher scores indicate better understandability/actionability. Seven criteria were used to assess cultural inclusivity specific for Aboriginal and Torres Strait Islander people; resources which met 3-5 items were deemed to be moderately inclusive and 6+ items as inclusive. A total of 109 resources were identified and 76% provided information on symptoms in the context of gynaecological cancers. The average readability was equivalent to a grade 10 reading level on the SMOG and classified as 'difficult to read' on the FRE. The mean PEMAT scores were 95% (range 58-100) for understandability and 13% (range 0-80) for actionability. Five resources were evaluated as being moderately culturally inclusive. No resource met all the benchmarks. This study highlights the inadequate quality of online resources available on pre-diagnosis gynaecological cancer symptom information. Resources should be revised in line with the recommended standards for readability, understandability and actionability and to meet the needs of a culturally diverse population.

Readability of Trauma-related Patient Education Materials From the American Academy of Orthopaedic Surgeons and Orthopaedic Trauma Association Websites.

Web-based resources serve as a fundamental educational platform for orthopaedic trauma patients; however, they are frequently written above the recommended sixth-grade reading level, and previous studies have demonstrated this for the American Academy of Orthopaedic Surgeons (AAOS) web-based articles. In this study, we perform an updated assessment of the readability of AAOS trauma-related educational articles as compared with injury-matched education materials developed by the Orthopaedic Trauma Association (OTA). All 46 AAOS trauma-related web-based ( https://www.orthoinfo.org/ ) patient education articles were analyzed for readability. Two independent reviewers used (1) the Flesch-Kincaid Grade Level (FKGL) and (2) the Flesch Reading Ease (FRE) algorithms to calculate the readability level. Mean readability scores were compared across body part categories. A one-sample t -test was done to compare mean FKGL with the recommended sixth-grade readability level and the average American adult reading level. A two-sample t -test was used to compare the readability scores of the AAOS trauma-related articles with those of the OTA. The average (SD) FKGL and FRE for the AAOS articles were 8.9 (0.74) and 57.2 (5.8), respectively. All articles were written above the sixth-grade reading level. The average readability of the AAOS articles was significantly greater than the recommended sixth-grade reading level ( P < 0.001). The average FKGL and FRE for all AAOS articles were significantly higher compared with those of the OTA articles (8.9 ± 0.74 versus 8.1 ± 1.14, P < 0.001 and 57.2 ± 5.8 versus 65.6 ± 6.6, P < 0.001, respectively). Excellent agreement was observed between raters for the FKGL 0.956 (95% confidence interval, 0.922 to 0.975) and FRE 0.993 (95% confidence interval, 0.987 to 0.996). Our findings suggest that after almost a decade, the readability of the AAOS trauma-related articles remains unchanged. The AAOS and OTA trauma patient education materials have high readability levels and may be too difficult for patient comprehension. A need remains to improve the readability of these commonly used trauma education materials.

The Performance of Chatbots and the AAPOS Website as a Tool for Amblyopia Education.

To evaluate the understandability, actionability, and readability of responses provided by the website of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS), ChatGPT-3.5, Bard, and Bing Chat about amblyopia and the appropriateness of the responses generated by the chatbots. Twenty-five questions provided by the AAPOS website were directed three times to fresh ChatGPT-3.5, Bard, and Bing Chat interfaces. Two experienced pediatric ophthalmologists categorized the responses of the chatbots in terms of their appropriateness. Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL), and Coleman-Liau Index (CLI) were used to evaluate the readability of the responses of the AAPOS website and chatbots. Furthermore, the understandability scores were evaluated using the Patient Education Materials Assessment Tool (PEMAT). The appropriateness of the chatbots' responses was 84.0% for ChatGPT-3.5 and Bard and 80% for Bing Chat (P > .05). For understandability (mean PEMAT-U score AAPOS website: 81.5%, Bard: 77.6%, ChatGPT-3.5: 76.1%, and Bing Chat: 71.5%, P < .05) and actionability (mean PEMAT-A score AAPOS website: 74.6%, Bard: 69.2%, ChatGPT-3.5: 67.8%, and Bing Chat: 64.8%, P < .05), the AAPOs website scored better than the chat-bots. Three readability analyses showed that Bard had the highest mean score, followed by the AAPOS website, Bing Chat, and ChatGPT-3.5, and these scores were more challenging than the recommended level. Chatbots have the potential to provide detailed and appropriate responses at acceptable levels. The AAPOS website has the advantage of providing information that is more understandable and actionable. The AAPOS website and chatbots, especially Chat-GPT, provided difficult-to-read data for patient education regarding amblyopia. [J Pediatr Ophthalmol Strabismus. 20XX;X(X):XXX-XXX.].

An Analysis of the Readability of Online Sarcoidosis Resources.

Introduction Sarcoidosis is an inflammatory disease characterized by the formation of noncaseating granulomas in multiple organ systems. The presentation can vary widely; although some patients with sarcoidosis can be asymptomatic, sarcoidosis can also present in others with symptomatic multiorgan system involvement. Considering the potential severity of the disease, patients need to be well-informed about sarcoidosis to better manage their health. This study aims to assess the readability levels of online resources about sarcoidosis. Methods We conducted a retrospective cross-sectional study. The term "sarcoidosis" was searched online using both Google and Bing to find websites written in English. Each website was categorized by type: academic, commercial, government, nonprofit, and physician. The readability scores for each website were calculated using six different readability tests: the Flesch-Kincaid reading ease (FKRE), Flesch-Kincaid grade level (FKGL), Gunning fog score (GFS), Simple Measure of Gobbledygook (SMOG), automated readability index (ARI), and Coleman-Liau index (CLI). FKRE gives a score that corresponds to the difficulty of the text, while the remaining tests give a score that corresponds to a grade level in terms of reading ability. A one-sample t-test was used to compare all test scores with the national recommended standard of a sixth-grade reading level. Our null hypothesis was that the readability scores of the websites searched would not differ statistically significantly from the sixth-grade reading level and that there would be no significant differences across website categories. To evaluate the difference between the categories of websites, ANOVA testing was used. Results Thirty-four websites were analyzed. Each of the six readability tests for the websites had an average score, which corresponded to being significantly harder to read than the nationally recommended sixth-grade reading level (p<0.001). None of the mean readability scores showed a statistically significant difference across the five different website categories. Conclusions This is the first study, to our knowledge, to examine the readability of online English resources on sarcoidosis and calculate standardized readability scores for them. It implies that the online English material for sarcoidosis is above the health literacy recommended reading levels for patients. There is a need to simplify the material to be easier to read for patients.

Navigating Scar Care: An Evaluation of Scar Treatment Patient Education Materials.

While patient education materials (PEMs) across various specialties have been reported as being too difficult to read, the quality and understandability of PEMs related to scar management have not been assessed. In this study, we report the breadth of scar management interventions and readability of online PEMs authored by academic societies and university hospitals. Websites of academic medical societies and university hospitals with scar revision PEMs were assessed for relevance. PEM readability was assessed via Flesch Reading Ease, Flesch-Kincaid Grade Level, and Gunning-Fox Index scores. Understandability and actionability were evaluated using the Patient Education Material Assessment Tool (PEMAT). A total of 26 scar revision PEMs met the inclusion criteria. The most commonly mentioned scar management interventions were scar revision surgery (73%) and laser scar revision (70%), with minimal emphasis on noninvasive methods like scar massage or sun protection. Readability analysis yielded a mean Flesch reading level of 8.8. Overall, PEMAT understandability of online scar treatment PEMs was moderate, with a median of 76.0% (IQR 71.5%-80.5%). PEMs from all specialties and institution types were lacking in actionability, with median actionability of 40.8% (IQR 38.1%-60.0%). Online scar revision PEMs included a wide breadth of scar management interventions; however, the least costly interventions, such as sun protection and scar massage, were not commonly included. PEMs for scar management could be improved by simplifying language, including visual aids, and including checklists or specific steps, patients can take to take action on scar management interventions.

Readability of Neurosurgical Patient Education Resources by the American Association of Neurological Surgeons

The importance of patient education has become increasingly apparent in recent years. A prominent patient education tool in neurosurgery is the Neurosurgical Conditions and Treatments page provided by the American Association of Neurological Surgeons (AANS). This study aimed to investigate the readability of this resource page as many new articles have been incorporated in the past decade. Flesch-Kincaid Grade Level and Flesch-Kincaid Reading Ease scores were calculated for each article. One-way analysis of variance and Scheffé, Tukey-Kramer, Bonferroni, Fisher least significant difference, and Dunnett test post hoc analyses were conducted to determine differences between each category with respect to their mean grade level and reading ease scores. Overall mean (SD) Flesch-Kincaid Reading Ease score was 40.2 (12.24), and overall mean (SD) Flesch-Kincaid Grade Level score was 7.48 (1.26). Significant differences were found between mean reading ease scores between each categorization by the AANS (P= 0.014). No significant differences were found between mean grade level score for each categorization (analysis of variance, P= 0.154). As compared to a single previous investigation conducted 10 years ago, the readability of articles has changed modestly, and the reading grade level remains well above the recommendations by the American Medical Association and National Institutes of Health. The 6 new articles introduced in the past decade have demonstrated similar readability, presenting a persistent challenge in the realm of patient education in neurosurgery.

Empowering inclusivity: improving readability of living kidney donation information with ChatGPT.

Addressing disparities in living kidney donation requires making information accessible across literacy levels, especially important given that the average American adult reads at an 8th-grade level. This study evaluated the effectiveness of ChatGPT, an advanced AI language model, in simplifying living kidney donation information to an 8th-grade reading level or below. We used ChatGPT versions 3.5 and 4.0 to modify 27 questions and answers from Donate Life America, a key resource on living kidney donation. We measured the readability of both original and modified texts using the Flesch-Kincaid formula. A paired t-test was conducted to assess changes in readability levels, and a statistical comparison between the two ChatGPT versions was performed. Originally, the FAQs had an average reading level of 9.6 ± 1.9. Post-modification, ChatGPT 3.5 achieved an average readability level of 7.72 ± 1.85, while ChatGPT 4.0 reached 4.30 ± 1.71, both with a p-value <0.001 indicating significant reduction. ChatGPT 3.5 made 59.26% of answers readable below 8th-grade level, whereas ChatGPT 4.0 did so for 96.30% of the texts. The grade level range for modified answers was 3.4-11.3 for ChatGPT 3.5 and 1-8.1 for ChatGPT 4.0. Both ChatGPT 3.5 and 4.0 effectively lowered the readability grade levels of complex medical information, with ChatGPT 4.0 being more effective. This suggests ChatGPT's potential role in promoting diversity and equity in living kidney donation, indicating scope for further refinement in making medical information more accessible.

ChatGPT-4 Generates More Accurate and Complete Responses to Common Patient Questions About Anterior Cruciate Ligament Reconstruction Than Google’s Search Engine

To replicate a patient's internet search to evaluate ChatGPT's appropriateness in answering common patient questions about anterior cruciate ligament reconstruction compared with a Google web search. A Google web search was performed by searching the term "anterior cruciate ligament reconstruction." The top 20 frequently asked questions and responses were recorded. The prompt "What are the 20 most popular patient questions related to 'anterior cruciate ligament reconstruction?'" was input into ChatGPT and questions and responses were recorded. Questions were classified based on the Rothwell system and responses assessed via Flesch-Kincaid Grade Level, correctness, and completeness were for both Google web search and ChatGPT. Three of 20 (15%) questions were similar between Google web search and ChatGPT. The most common question types among the Google web search were value (8/20, 40%), fact (7/20, 35%), and policy (5/20, 25%). The most common question types amongst the ChatGPT search were fact (12/20, 60%), policy (6/20, 30%), and value (2/20, 10%). Mean Flesch-Kincaid Grade Level for Google web search responses was significantly lower (11.8 ± 3.8 vs 14.3 ± 2.2; P= .003) than for ChatGPT responses. The mean correctness for Google web search question answers was 1.47 ± 0.5, and mean completeness was 1.36 ± 0.5. Mean correctness for ChatGPT answers was 1.8 ± 0.4 and mean completeness was 1.9 ± 0.3, which were both significantly greater than Google web search answers (P= .03 and P= .0003). ChatGPT-4 generated more accurate and complete responses to common patient questions about anterior cruciate ligament reconstruction than Google's search engine. The use of artificial intelligence such as ChatGPT is expanding. It is important to understand the quality of information as well as how the results of ChatGPT queries compare with those from Google web searches.

Analysis of informed consent documents for compliance with ICMR guidelines for biomedical and health research

Abstract Background: Ethical conduct of research depends on the voluntary expression of consent and adequate disclosure of information about the research in informed consent documents (ICDs). Objectives: The objective of this study was to analyze ICDs of academic studies for compliance with National Ethical Guidelines for Biomedical and Health Research laid down by the Indian Council of Medical Research (ICMR) and to determine the readability of ICDs using the Flesch–Kincaid Grade Level scale and Flesch reading-ease (FRE) score. Methodology: ICDs of academic research projects submitted during 2020–22 were retrieved from the IEC office and analyzed for compliance with ICMR 2017 guidelines. The readability of the documents was assessed by the Flesch–Kincaid Grade Level Scale and FRE score. Results: Among 177 protocols analyzed, the most common were epidemiological studies (36.72%), followed by diagnostic studies (28.81%). Vernacular translations of ICDs were present in significantly more studies in 2022 (χ 2 = 7.18, P = 0.02) as compared to 2020 and 2021. FREs score was 45.75 ± 10.76, and Flesch–Kincaid Grade Level was 8.67 ± 1.44. Content analysis of participant information sheet (PIS) revealed that significantly more PIS submitted in 2022 mentioned expected duration of participation (χ 2 = 6.95, P &lt; 0.001), benefit to patient/community (χ 2 = 26.63, P &lt; 0.001), disclosure of foreseeable risk or discomfort (χ 2 = 21.72, P &lt; 0.001), payment for participation (χ 2 = 21.72, P &lt; 0.001), and identity of research team and contact details (χ 2 = 18.58, P &lt; 0.001). Compliance score was significantly better in 2022 as compared to 2020 and 2021. Conclusion: Gradually, ICDs became more compliant with ICMR guidelines. Still, there is scope for improvement in ICDs regarding content and readability so that patients can comprehend facts easily to make informed decisions in a real sense.