Patient Education Materials Research Articles

Building the OAChangeMap to Improve the Service Delivery of the New South Wales Osteoarthritis Chronic Care Program: A Worked Example of Using a Codesign Framework.

The Osteoarthritis Chronic Care Program (OACCP) has been implemented in Australian public hospitals to deliver best evidence osteoarthritis (OA) care. It is important to ensure that the OACCP continues to deliver evidence-based OA care as intended. We aimed to identify barriers and enablers to delivering the OACCP, prioritize the barriers, and generate strategies to address them. This study provides a worked example of a seven-step theory-informed codesign framework. We invited OACCP coordinators to participate in semistructured interviews (analyzed thematically) and complete a questionnaire to identify barriers and enablers to delivery of the OACCP. We then invited a broader group of stakeholders (OACCP coordinators, health managers, policy makers, consumers, and researchers) to prioritize the barriers via a short survey (survey 2). We held five codesign workshops in which we mapped the priority barriers to the Theoretical Domains Framework and developed strategies to address them. Sixteen coordinators were interviewed, and the main barriers identified were as follows: (1) patients often have beliefs that are inconsistent with best evidence care, (2) there are aspects of clinical care that are not delivered optimally, and (3) system-level factors are a barrier to optimal patient care and sustainability of the OACCP. We codesigned a plan for action with patient educational materials, shared decision-making tools, and health professional education and training. Our worked example of codesign used a theory-based, data-driven approach with key stakeholders, identified and prioritized barriers to the delivery of the OACCP, acknowledged enablers, and generated a plan for feasible strategies to improve the program.

Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures.

Patient education materials (PEMs) are crucial for improving patient adherence and outcomes; however, they may not beaccessibledue to high reading levels. Our study used seven readability measures to compare the readability of SpanishPEMsfromthe Society of Pediatric Dermatology (SPD) with those generated by Open-AI's ChatGPT 4.0 and Google Gemini. Our results showed that when prompted to produce material at a 6th grade level, both AI ChatBots generated significantly improved readability scores when compared to the SPD handouts. These findings suggest that AI-generated PEMs couldbetter meet readability standards and potentially improve patient outcomes, although further studies are needed to confirm this.

ChatGPT versus Google Gemini: a comparison to evaluate patient education guide created on common neurological disorders

Abstract Introduction This study assesses the effectiveness of AI tools, ChatGPT and Google Gemini, in educating the public about neurological conditions such as Bell’s palsy, Tetanus, and Headaches, and assess chatbot-generated patient guides based on readability and ease of understanding. Methodology In March 2024, the authors conducted a cross-sectional study to develop patient education brochures for Bell’s palsy, Tetanus vaccination, and Persistent Headache, leveraging AI models ChatGPT and Google Gemini. The brochures’ quality was assessed through readability, similarity, and a modified DISCERN score for reliability. Statistical analysis, performed in R software, compared responses from both AI models using unpaired T-tests. The correlation between ease score and reliability was explored using Pearson’s Coefficient of Correlation. Results The study revealed no significant variances in word count, sentence count, or average words per sentence between the two AI tools. However, materials generated by ChatGPT exhibited significantly higher ease scores, highlighting its proficiency in creating more understandable content (p < 0.05). Conclusions This study finds ChatGPT outperforms Google Gemini in readability despite similar metrics. This suggests ChatGPT's potential superiority in creating more understandable patient education materials. As AI advances, it’s essential to research more tools and medical conditions to ensure they meet diverse patient education needs.

The Importance of Readability: A Guide to Understanding Alopecia Areata through Multilingual Online Resources.

Online resources play a vital role in patient education, yet the readability of alopecia areata-related materials remained understudied. A thorough analysis of online alopecia areata-related materials across 5 languages was conducted using Google search. Search terms "alopecia areata" and "alopecia areata treatment" were translated and queried, generating search result lists. The first 50 articles from each list were evaluated for suitability. The materials were categorized into 2 main groups: those focusing on alopecia areata itself and those addressing its treatment. Treatment materials were further divided into subgroups, including Janus kinase inhibitors and other treatment options. Readability was evaluated using the Lix score. The analysis included 251 articles in English, German, French, Italian, and Spanish. The overall mean Lix score was 52 ± 8, which classified them as very hard to comprehend. Articles on alopecia areata treatment had a mean Lix score of 55 ± 8, which was significantly higher (p < 0.001) than those on alopecia areata itself, 50 ± 8. alopecia areata-treatment articles dedicated to JAK inhibitors had an average Lix score of 57 ± 10 and it was significantly higher (p = 0.043) than those on other treatment, 53 ± 6. Online resources on alopecia areata and its treatments remained challenging to comprehend, particularly regarding JAK inhibitors. Improving clarity in patient education materials is crucial for informed decision-making and therapeutic relationships.

Utilization of Artificial Intelligence in the Creation of Patient Information on Laryngology Topics.

To evaluate and compare the readability and quality of patient information generated by Chat-Generative Pre-Trained Transformer-3.5 (ChatGPT) and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) using validated instruments including Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease, DISCERN, and Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P). ENTHealth.org and ChatGPT-3.5 were queried for patient information on laryngology topics. ChatGPT-3.5 was queried twice for a given topic to evaluate for reliability. This generated three de-identified text documents for each topic: one from AAO-HNS and two from ChatGPT (ChatGPT Output 1, ChatGPT Output 2). Grade level and reading ease were compared between the three sources using a one-way analysis of variance and Tukey's post hoc test. Independent t-tests were used to compare DISCERN and PEMAT understandability and actionability scores between AAO-HNS and ChatGPT Output 1. Material generated from ChatGPT Output 1 and ChatGPT Output 2 were at least two reading grade levels higher than that of material from AAO-HNS (p < 0.001). Regarding reading ease, ChatGPT Output 1 and ChatGPT Output 2 documents had significantly lower mean scores compared to AAO-HNS (p < 0.001). Moreover, ChatGPT Output 1 material on vocal cord paralysis had a lower PEMAT-P understandability compared to that of AAO-HNS material (p > 0.05). Patient information on the ENTHealth.org website for select laryngology topics was, on average, of a lower grade level and higher reading ease compared to that produced by ChatGPT, but interestingly with largely no difference in the quality of information provided. NA Laryngoscope, 2024.

Utility of Generative Artificial Intelligence for Patient Care Counseling for Mandibular Fractures.

To determine the readability and accuracy of information regarding mandible fractures generated by Chat Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4o. Patients are increasingly turning to generative artificial intelligence to answer medical queries. To date, the accuracy and readability of responses regarding mandible fractures have not been assessed. Twenty patient questions regarding mandible fractures were developed by querying AlsoAsked (https://alsoasked.com), SearchResponse (https://searchresponse.io), and Answer the Public (https://answerthepublic.com/). Questions were posed to ChatGPT 3.5 and 4o. Readability was assessed by calculating the Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, number of sentences, and percentage of complex words. Accuracy was assessed by a board-certified facial plastic and reconstructive otolaryngologist using a 5-point Likert Scale. No significant differences were observed between the two versions for readability or accuracy. Readability was above recommended levels for patient education materials. Accuracy was low, and a majority of responses were deemed inappropriate for patient use with multiple inaccuracies and/or missing information. ChatGPT produced responses written at a high level inappropriate for the average patient, in addition to containing several inaccurate statements. Patients and clinicians should be aware of the limitations of generative artificial intelligence when seeking medical information regarding mandible fractures.

Readability Metrics in Patient Education: Where Do We Innovate?

The increasing use of digital applications in healthcare has led to a greater need for patient education materials. These materials, often in the form of pamphlets, booklets, and handouts, are designed to supplement physician–patient communication and aim to improve patient outcomes. However, the effectiveness of these materials can be hindered by variations in patient health literacy. Readability, a measure of text comprehension, is a key factor influencing how well patients understand these educational materials. While there has been growing interest in readability assessment in medicine, many studies have demonstrated that digital texts do not frequently meet the recommended sixth-to-eighth grade reading level. The purpose of this opinion article is to review readability from the perspective of studies in pediatric medicine, internal medicine, preventative medicine, and surgery. This article aims to communicate that while readability is important, it tends to not fully capture the complexity of health literacy or effective patient communication. Moreover, a promising avenue to improve readability may be in generative artificial intelligence, as there are currently limited tools with similar effectiveness.

Comparative Analysis of Artificial Intelligence Platforms in Generating Post-Operative Instructions for Rhinologic Surgery

AbstractRecently, artificial intelligence (AI) platforms such as ChatGPT and Google Gemini have progressed at a rapid pace. To allow for optimal medical outcomes and patient safety, it is crucial that patients have clearly written post-operative instructions. Patients are increasingly turning to AI platforms for medical information. Our objective was to evaluate the ability of ChatGPT versus Gemini to develop accurate and understandable post-operative instructions for patients undergoing endoscopic sinus surgery (ESS). Postoperative instructions on ESS, balloon sinuplasty, Caldwell Luc surgery, and septoplasty were generated using ChatGPT and Gemini. Measures of readability were calculated. The Patient Education Materials Assessment Tool was utilized to determine Understandability and Actionability. ChatGPT and Gemini were capable of generating understandable post-operative instructions but were poor for actionability and readability. While AI has a promising ability to generate accessible medical information, patients should be aware of its drawbacks especially when seeking postoperative instructions.

Assessing the Readability of Online English and Spanish Resources for Polydactyly and Syndactyly.

Online patient education materials (PEMs) that are difficult to read disproportionately affect patients with low health literacy and educational attainment. Patients may not be fully informed or empowered to engage meaningfully with providers and advocate for their goals. We aim to assess the readability of online PEMs regarding polydactyly and syndactyly. Google was used to query "polydactyly" and "syndactyly" in English and Spanish. The first 50 results were categorized into institutional (government, medical school, teaching hospital), noninstitutional (private practice, blog), and academic (journal articles, book chapters). Readability scores were generated using the Simple Measure of Gobbledygook and Spanish Simple Measure of Gobbledygook scales. All polydactyly PEMs and >95% of syndactyly PEMs exceeded the National Institutes of Health recommended 6th-grade reading level. Altogether, English PEMs had an average reading level of a university freshman and Spanish PEMs had an average reading level of nearly a high school sophomore. For both diagnoses, English PEMs were harder to read than Spanish PEMs overall and when compared across the 3 categories between the 2 languages. Generally, noninstitutional PEMs were more difficult to read than their institutional counterparts. To improve patient education, health literacy, and language equity, online resources for polydactyly and syndactyly should be written at the 6th-grade level. Currently, these PEMs are too advanced, which can make accessing, understanding, and pursuing healthcare decisions more challenging. Understanding health conditions and information is crucial to empower patients, regardless of literacy.

Limited Availability and Readability of Spanish Language Patient Educational Material: A Review of the Nation's Top Children's Hospitals.

Cleft lip with or without cleft palate (CL/P) is a common congenital facial pathology that occurs at higher incidences in Hispanic communities. The authors analyzed the availability and readability of Spanish-written patient education materials (PEMs) on CL/P from top-ranking U.S. children's hospitals to determine the presence of health literacy barriers. Availability of PEM was evaluated by 2 methods: (1) Google search and (2) evaluation of the official hospital websites. For each institution, a Google search was conducted using the phrase, "labio leporino y/o paladar hendido (translation: CL/P) + (hospital name)." In addition, each hospital website was assessed for Spanish PEM availability. Spanish PEMs were then categorized by whether they had been generated by an automated translation function or if they were independently written Spanish text. English PEM readability was assessed using the Simple Measure of Gobbledygook (SMOG). Spanish PEM readability was assessed using spanish orthgraphic length (Spanish Orthographic Length), the SMOG converted for the Spanish language. Unpaired 2-tailed t tests were used to compare readability. Of a total of 85 pediatric hospitals, 28 (37.3%) had Spanish PEM. Five (6.7%) hospitals created their own Spanish-language document. The average spanish orthgraphic length reading level was 9.49 compared with an average SMOG of 11.38 (P < 0.001). Institutions that did not provide Spanish PEM in any format had a significantly higher SMOG for English PEM of 12.13 compared with 11.38 of those that had SMOG with English PEM (P = 0.04). Health literacy barriers not only exist for Spanish PEM but also for English PEM, indicating an opportunity to improve communication.

Comments on "Readability Analysis of Patient Education Material on Rotator Cuff Injuries From the Top 25 Ranking Orthopedic Institutions".

Comments on "Readability Analysis of Patient Education Material on Rotator Cuff Injuries From the Top 25 Ranking Orthopedic Institutions".

The effect of the use of artificial intelligence in the preparation of patient education materials by nursing students on the understandability, actionability and quality of the material: A randomized controlled trial

The effect of the use of artificial intelligence in the preparation of patient education materials by nursing students on the understandability, actionability and quality of the material: A randomized controlled trial

Assessing the Role of the Generative Pretrained Transformer (GPT) in Alzheimer's Disease Management: Comparative Study of Neurologist- and Artificial Intelligence-Generated Responses.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder posing challenges to patients, caregivers, and society. Accessible and accurate information is crucial for effective AD management. This study aimed to evaluate the accuracy, comprehensibility, clarity, and usefulness of the Generative Pretrained Transformer's (GPT) answers concerning the management and caregiving of patients with AD. In total, 14 questions related to the prevention, treatment, and care of AD were identified and posed to GPT-3.5 and GPT-4 in Chinese and English, respectively, and 4 respondent neurologists were asked to answer them. We generated 8 sets of responses (total 112) and randomly coded them in answer sheets. Next, 5 evaluator neurologists and 5 family members of patients were asked to rate the 112 responses using separate 5-point Likert scales. We evaluated the quality of the responses using a set of 8 questions rated on a 5-point Likert scale. To gauge comprehensibility and participant satisfaction, we included 3 questions dedicated to each aspect within the same set of 8 questions. As of April 10, 2023, the 5 evaluator neurologists and 5 family members of patients with AD rated the 112 responses: GPT-3.5: n=28, 25%, responses; GPT-4: n=28, 25%, responses; respondent neurologists: 56 (50%) responses. The top 5 (4.5%) responses rated by evaluator neurologists had 4 (80%) GPT (GPT-3.5+GPT-4) responses and 1 (20%) respondent neurologist's response. For the top 5 (4.5%) responses rated by patients' family members, all but the third response were GPT responses. Based on the evaluation by neurologists, the neurologist-generated responses achieved a mean score of 3.9 (SD 0.7), while the GPT-generated responses scored significantly higher (mean 4.4, SD 0.6; P<.001). Language and model analyses revealed no significant differences in response quality between the GPT-3.5 and GPT-4 models (GPT-3.5: mean 4.3, SD 0.7; GPT-4: mean 4.4, SD 0.5; P=.51). However, English responses outperformed Chinese responses in terms of comprehensibility (Chinese responses: mean 4.1, SD 0.7; English responses: mean 4.6, SD 0.5; P=.005) and participant satisfaction (Chinese responses: mean 4.2, SD 0.8; English responses: mean 4.5, SD 0.5; P=.04). According to the evaluator neurologists' review, Chinese responses had a mean score of 4.4 (SD 0.6), whereas English responses had a mean score of 4.5 (SD 0.5; P=.002). As for the family members of patients with AD, no significant differences were observed between GPT and neurologists, GPT-3.5 and GPT-4, or Chinese and English responses. GPT can provide patient education materials on AD for patients, their families and caregivers, nurses, and neurologists. This capability can contribute to the effective health care management of patients with AD, leading to enhanced patient outcomes.

A Performance Evaluation of Large Language Models in Keratoconus: A Comparative Study of ChatGPT-3.5, ChatGPT-4.0, Gemini, Copilot, Chatsonic, and Perplexity.

Background: This study evaluates the ability of six popular chatbots; ChatGPT-3.5, ChatGPT-4.0, Gemini, Copilot, Chatsonic, and Perplexity, to provide reliable answers to questions concerning keratoconus. Methods: Chatbots responses were assessed using mDISCERN (range: 15-75) and Global Quality Score (GQS) (range: 1-5) metrics. Readability was evaluated using nine validated readability assessments. We also addressed the quality and accountability of websites from which the questions originated. Results: We analyzed 20 websites, 65% "Private practice or independent user" and 35% "Official patient education materials". The mean JAMA benchmark score was 1.40 ± 0.91 (0-4 points), indicating low accountability. Reliability, measured using mDISCERN, ranged from 42.91 ± 3.15 (ChatGPT-3.5) to 46.95 ± 3.53 (Copilot). The most frequent question was "What is keratoconus?" with 70% of websites providing relevant information. This received the highest mDISCERN score (49.30 ± 4.91) and a relatively high GQS score (3.40 ± 0.56) with an Automated Readability Level Calculator score of 13.17 ± 2.13. Moderate positive correlations were determined between the website numbers and both mDISCERN (r = 0.265, p = 0.25) and GQS (r = 0.453, p = 0.05) scores. The quality of information, assessed using the GQS, ranged from 3.02 ± 0.55 (ChatGPT-3.5) to 3.31 ± 0.64 (Gemini) (p = 0.34). The differences between the texts were statistically significant. Gemini emerged as the easiest to read, while ChatGPT-3.5 and Perplexity were the most difficult. Based on mDISCERN scores, Gemini and Copilot exhibited the highest percentage of responses in the "good" range (51-62 points). For the GQS, the Gemini model exhibited the highest percentage of responses in the "good" quality range with 40% of its responses scoring 4-5. Conclusions: While all chatbots performed well, Gemini and Copilot showed better reliability and quality. However, their readability often exceeded recommended levels. Continuous improvements are essential to match information with patients' health literacy for effective use in ophthalmology.

Can ChatGPT provide quality information about fever in children?

Artificial intelligence (AI) systems hold great promise in improving medical care and health problems. We aimed to evaluate the answers by asking the most frequently asked questions to ChatGPT for the prediction and treatment of fever, which is a major problem in children. The 50 questions most frequently asked about fever in children were determined, and we asked them to ChatGPT. We evaluated the responses using the quality and readability scales. While ChatGPT demonstrated good quality in its responses, the readability scale and the Patient Education Material Evaluation Tool (PEMAT) scale used with materials appearing on the site were also found to be successful. Among the scales in which we evaluated ChatGPT responses, a weak positive relationship was found between Gunning Fog (GFOG) and Simple Measure of Gobbledygook (SMOG) scores (r = 0.379) and a significant and positive relationship was found between FGL and SMOG scores (r = 0.899). This study sheds light on the quality and readability of information regarding the presentation of AI tools, such as ChatGPT, regarding fever, a common complaint in children. We determined that the answers to the most frequently asked questions about fire were high-quality, reliable, easy to read and understandable.

Assessment of Artificial Intelligence Chatbot Responses to Common Patient Questions on Bone Sarcoma.

The potential impacts of artificial intelligence (AI) chatbots on care for patients with bone sarcoma is poorly understood. Elucidating potential risks and benefits would allow surgeons to define appropriate roles for these tools in clinical care. Eleven questions on bone sarcoma diagnosis, treatment, and recovery were inputted into three AI chatbots. Answers were assessed on a 5-point Likert scale for five clinical accuracy metrics: relevance to the question, balance and lack of bias, basis on established data, factual accuracy, and completeness in scope. Responses were quantitatively assessed for empathy and readability. The Patient Education Materials Assessment Tool (PEMAT) was assessed for understandability and actionability. Chatbots scored highly on relevance (4.24) and balance/lack of bias (4.09) but lower on basing responses on established data (3.77), completeness (3.68), and factual accuracy (3.66). Responses generally scored well on understandability (84.30%), while actionability scores were low for questions on treatment (64.58%) and recovery (60.64%). GPT-4 exhibited the highest empathy (4.12). Readability scores averaged between 10.28 for diagnosis questions to 11.65 for recovery questions. While AI chatbots are promising tools, current limitations in factual accuracy and completeness, as well as concerns of inaccessibility to populations with lower health literacy, may significantly limit their clinical utility.

Videos in Short-Video Sharing Platforms as Sources of Information on Colorectal Polyps: Cross-Sectional Content Analysis Study.

Short videos have demonstrated huge potential in disseminating health information in recent years. However, to our knowledge, no study has examined information about colorectal polyps on short-video sharing platforms. This study aimed to analyze the content and quality of colorectal polyps-related videos on short-video sharing platforms. The terms "" (intestinal polyps) or "" (colonic polyps) or "" (rectal polyps) or "" (colorectal polyps) or "" (polyps of large intestine) were used to search in TikTok (ByteDance), WeChat (Tencent Holdings Limited), and Xiaohongshu (Xingyin Information Technology Limited) between May 26 and June 8, 2024, and then the top 100 videos for each search term on different platforms were included and recorded. The Journal of American Medical Association (JAMA) score, the Global Quality Scale (GQS), the modified DISCERN, and the Patient Education Materials Assessment Tool (PEMAT) were used to evaluate the content and quality of selected videos by 2 independent researchers. SPSS (version 22.0; IBM Corp) and GraphPad Prism (version 9.0; Dotmatics) were used for analyzing the data. Descriptive statistics were generated, and the differences between groups were compared. Spearman correlation analysis was used to evaluate the relationship between quantitative variables. A total of 816 eligible videos were included for further analysis, which mainly conveyed disease-related knowledge (n=635, 77.8%). Most videos were uploaded by physicians (n=709, 86.9%). These videos had an average JAMA score of 2.0 (SD 0.6), GQS score of 2.5 (SD 0.8), modified DISCERN score of 2.5 (SD 0.8), understandability of 80.4% (SD 15.6%), and actionability of 42.2% (SD 36.1%). Videos uploaded by news agencies were of higher quality and received more likes and comments (all P<.05). The number of collections and shares of videos about posttreatment caveats were more than those for other content (P=.03 and P=.006). There was a positive correlation between the number of likes, comments, collections, and shares (all P<.001). The duration and the number of fans were positively correlated with the quality of videos (all P<.05). There are numerous videos about colorectal polyps on short-video sharing platforms, but the reliability and quality of these videos are not good enough and need to be improved.

Large language models in patient education: a scoping review of applications in medicine.

Large Language Models (LLMs) are sophisticated algorithms that analyze and generate vast amounts of textual data, mimicking human communication. Notable LLMs include GPT-4o by Open AI, Claude 3.5 Sonnet by Anthropic, and Gemini by Google. This scoping review aims to synthesize the current applications and potential uses of LLMs in patient education and engagement. Following the PRISMA-ScR checklist and methodologies by Arksey, O'Malley, and Levac, we conducted a scoping review. We searched PubMed in June 2024, using keywords and MeSH terms related to LLMs and patient education. Two authors conducted the initial screening, and discrepancies were resolved by consensus. We employed thematic analysis to address our primary research question. The review identified 201 studies, predominantly from the United States (58.2%). Six themes emerged: generating patient education materials, interpreting medical information, providing lifestyle recommendations, supporting customized medication use, offering perioperative care instructions, and optimizing doctor-patient interaction. LLMs were found to provide accurate responses to patient queries, enhance existing educational materials, and translate medical information into patient-friendly language. However, challenges such as readability, accuracy, and potential biases were noted. LLMs demonstrate significant potential in patient education and engagement by creating accessible educational materials, interpreting complex medical information, and enhancing communication between patients and healthcare providers. Nonetheless, issues related to the accuracy and readability of LLM-generated content, as well as ethical concerns, require further research and development. Future studies should focus on improving LLMs and ensuring content reliability while addressing ethical considerations.

Utilizing Artificial Intelligence to Increase the Readability of Patient Education Materials in Pediatric Otolaryngology.

Objectives: To identify the reading levels of existing patient education materials in pediatric otolaryngology and to utilize natural language processing artificial intelligence (AI) to reduce the reading level of patient education materials. Methods: Patient education materials for pediatric conditions were identified from the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) website. Patient education materials about the same conditions, if available, were identified and selected from the websites of 7 children's hospitals. The readability of the patient materials was scored before and after using AI with the Flesch-Kincaid calculator. ChatGPT version 3.5 was used to convert the materials to a fifth-grade reading level. Results: On average, AAO-HNS pediatric education material was written at a 10.71 ± 0.71 grade level. After requesting the reduction of those materials to a fifth-grade reading level, ChatGPT converted the same materials to an average grade level of 7.9 ± 1.18 (P < .01). When comparing the published materials from AAO-HNS and the 7 institutions, the average grade level was 9.32 ± 1.82, and ChatGPT was able to reduce the average level to 7.68 ± 1.12 (P = .0598). Of the 7 children's hospitals, only 1 institution had an average grade level below the recommended sixth-grade level. Conclusions: Patient education materials in pediatric otolaryngology were consistently above recommended reading levels. In its current state, AI can reduce the reading levels of education materials. However, it did not possess the capability to reduce all materials to be below the recommended reading level.

Using PPI and UDL principles to design a gastrointestinal endoscopy patient information video series

Abstract Introduction Patients undergoing GI endoscopy often experience anxiety and due to limited health literacy. Traditional paper-based information leaflets are often not sufficient to accommodate for the diverse way in which people consume information. Public and patient involvement (PPI) and universal design for learning are two well-described pedagogical principles. We wanted to use these principles to design more effective patient education materials. Objectives Given the heterogeneity of resources available to patients, the authors wanted to develop a high-quality and trustable video resources for patients that would be available on our hospital website. These videos would be co-designed by patients and ancillary staff, through the formation of a key stakeholder panel. Results Using PPI principles, we were able to improve the relevance and quality of our content, through the use of storyboards and the expertise of the stakeholder panel. Through the three key UDL principles of multiple means of representation, engagement and action and expression, we were able to ensure that our videos were accessible and appropriate for all of our patients. Initial feedback from stakeholders and patients provided valuable insights, prompting necessary adjustments for accuracy and patient understanding. This comprehensive approach underscores the commitment to enhancing healthcare delivery. Our project is the first guide in combining both pedagogical principles in the development of patient information and education materials. Conclusions The meticulous steps taken, from stakeholder engagement to video production, reflect a commitment to inclusivity and patient-centred care. Ongoing evaluation and feedback mechanisms ensure continuous improvement, contributing to the overall success and sustainability of the digital service. Key messages • This is the first research/patient education piece designed using both PPI and UDL principles. • The importance of patient-centred educational design is key to improving health literacy and education in our patients.