The Role of YouTube as an Information Platform for Ovarian Cancer: the Educational Quality and Reliability.

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Learn more: PMC Disclaimer | PMC Copyright Notice Yonsei Med J . 2026 Apr 10;67(6):502–507. doi: 10.3349/ymj.2025.0374 The Role of YouTube as an Information Platform for Ovarian Cancer: the Educational Quality and Reliability Soo Young Jeong 1 , Jiye Kim 1 , Jisong Hwang 1 , Sung Taek Park 1 , Sung Ho Park 1, ✉ Author information Article notes Copyright and License information 1 Department of Obstetrics and Gynecology, Kangnam Sacred-Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, Seoul, Korea. ✉ Corresponding author: Sung Ho Park, MD, PhD, Department of Obstetrics and Gynecology, Kangnam Sacred-Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, 1 Singil-ro, Yeongdeungpo-gu, Seoul 07441, Korea. vth2000@hallym.or.kr ✉ Corresponding author. Received 2025 Oct 1; Revised 2025 Dec 24; Accepted 2026 Jan 14; Issue date 2026 Jun. © Copyright: Yonsei University College of Medicine 2026 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0 ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13213268  PMID: 42198861 Abstract Purpose Ovarian cancer is the eighth most common malignancy among women worldwide and remains associated with poor survival. Patients and caregivers often experience unmet informational needs and turn to online resources such as YouTube; however, the quality and reliability of ovarian cancer-related content have not been evaluated. Materials and Methods On October 4, 2022, the 100 most-viewed YouTube videos retrieved using the keyword “ovarian cancer” were screened, and 86 English-language videos were included. Data collected included uploader type, views, length, likes, dislikes, comments, and content category. Educational quality and informational reliability were assessed using the Global Quality Score (GQS) and modified DISCERN criteria. Three obstetrics and gynecology residents independently rated all videos. Group comparisons were performed using the Mann–Whitney U and Kruskal–Wallis tests, and inter-rater reliability was assessed by intraclass correlation coefficients (ICC). Results Of the 86 videos, 56.98% were uploaded by medical sources and 43.02% by non-medical sources. Informational content comprised 49%, followed by patient experience (43%). The videos collectively accumulated over 16 million views. Non-medical videos were longer and received more likes and comments, with a higher like ratio. The mean GQS was 3.5±1.1 (moderate quality), and the mean modified DISCERN was 2.2±1.1 (low reliability). Modified DISCERN scores were higher in medical than non-medical videos ( p =0.001). Inter-rater agreement was excellent (ICC>0.9). Conclusion YouTube provides broad access to ovarian cancer-related information, but a substantial gap exists between educational quality and informational reliability. Professional involvement and platform-level strategies may improve evidence attribution and the educational value of popular videos. Keywords: Ovarian neoplasms, social media, information dissemination, health education, patient education as topic Graphical Abstract INTRODUCTION Ovarian cancer is the eighth most common malignancy among women worldwide, ranking high in both incidence and mortality. 1 Although global incidence rates have slightly declined in recent decades, survival outcomes remain poor, with a 5-year survival rate of only 49%. In the United States, 19880 new diagnoses and 12810 deaths were estimated in 2022. 2 These figures underscore the substantial disease burden and highlight the urgent need for improved strategies to support patients and their families. Beyond medical treatment, unmet supportive care needs remain significant in oncology. A review of advanced cancer patients and their informal caregivers reported that patients required psychological, physical, and informational support, while caregivers most frequently identified a lack of information—particularly concerning disease processes, treatment options, and caregiving responsibilities. 3 In ovarian cancer, newly diagnosed patients often experience considerable anxiety and uncertainty due to limited understanding of their condition. Reliable and comprehensible information is therefore critical to empower patients and caregivers, reduce distress, and promote shared decision-making. 4 Despite this recognized need, barriers persist in clinical practice. Time constraints during outpatient visits and the scarcity of standardized educational materials frequently limit the amount of information that can be delivered. Consequently, many patients turn to online platforms to obtain supplementary information. Previous research has emphasized the importance of complementing clinical encounters with alternative modes of communication. For instance, Fitch and Steele 5 proposed the use of computer- and video-based interventions to enhance information delivery and provide patients with accessible resources beyond the clinical setting. Among the available platforms, YouTube has emerged as the most widely used video-sharing service worldwide. With over 2 billion unique monthly users and more than 1 billion hours of content viewed daily, it provides unparalleled opportunities for disseminating health-related information to diverse populations. 6 However, the open-access nature of YouTube raises concerns regarding the accuracy, quality, and reliability of the information presented. Misinformation or low-quality educational content may not only fail to meet patients’ needs but also make it easier to increase confusion and distress. This underscores the necessity of systematically evaluating YouTube as a potential source of medical information. Therefore, this study aims to evaluate the educational quality and informational reliability of YouTube videos related to ovarian cancer. It seeks to clarify whether this platform can serve as a credible supplementary resource for patient education, thereby contributing to efforts to improve supportive care and health literacy in ovarian cancer patients. MATERIALS AND METHODS Video selection and data collection A search of YouTube was performed on October 4, 2022, using the keyword “ovarian cancer.” The search was conducted in incognito mode, with cookies cleared and without logging into a user account. The search was performed from the Republic of Korea. After sorting search results by view count, videos that had enabled comments and visible like/dislike counts were considered eligible, and the 100 most-viewed eligible videos were selected. Subsequently, 14 non-English videos were excluded, leaving 86 eligible videos for final analysis ( Fig. 1 ). For each video, the following parameters were collected: number of subscribers of the uploader, total views, days since initial upload, video length, number of likes, number of dislikes, and number of comments. Fig. 1. Video selection for the keyword “ovarian cancer.”. Open in a new tab Video classification Videos were categorized according to uploader and content. Uploaders were classified as medical (hospital channels, doctors, and health-related channels) or non-medical (patients, news, magazines, talk shows, commercials, and other unspecified sources). Content categories included: 1) disease information related to ovarian cancer, 2) patient experiences, 3) surgical techniques, 4) advertisements, and 5) unclassified. Indices and scoring systems The like ratio was defined as the number of likes divided by the sum of likes and dislikes, multiplied by 100. The view ratio was defined as the number of views per day since upload. A video power index was then derived by multiplying the like ratio by the view ratio and dividing the product by 100. 7 Educational quality was assessed using the 5-point Global Quality Score (GQS), in which a score of 1 indicates poor quality and a score of 5 indicates excellent quality ( Table 1 ). Informational reliability was assessed using modified DISCERN criteria, consisting of five items scored as 0 (no) or 1 (yes), with higher scores indicating greater reliability ( Table 2 ). 8 Table 1. Global Quality Score (1 to 5). Score 1 Poor quality, poor flow of the site, most information missing, not at all useful for patients 2 Generally poor quality and poor flow, some information is listed but many important topics are missing, of very limited use to patients 3 Moderate quality, suboptimal flow, some important information is adequately discussed but others are poorly discussed, somewhat useful for patients 4 Good quality and generally good flow, most of the relevant information is listed, but some topics are not covered, useful for patients 5 Excellent quality and excellent flow, very useful for patients Open in a new tab Table 2. Modified DISCERN Criteria (0 to 5, 5 Questions: 0 for No, 1 for Yes). Question 1 Are the aims clear and achieved? 2 Are reliable sources of information used? (i.e., publications are cited, the speaker is a certified physician) 3 Is the information presented balanced and unbiased? 4 Are additional sources of information listed for patient reference? 5 Are areas of uncertainty mentioned? Open in a new tab Evaluation process Three second-year residents in obstetrics and gynecology independently evaluated all 86 videos using both the GQS and modified DISCERN criteria. The mean score across the three evaluators was used for subsequent analyses. Statistical analysis All statistical analyses were conducted using IBM SPSS Statistics, version 27.0 (IBM Corp., Armonk, NY, USA). For each variable, the median values and ranges were calculated. Comparisons between medical and non-medical uploaders were performed using the Mann–Whitney U test, while comparisons across video content categories were performed using the Kruskal–Wallis test. These analyses were intended to be exploratory and descriptive, aiming to identify general patterns in video characteristics, educational quality, and informational reliability. Accordingly, p -values of <0.05 were considered statistically significant. Inter-rater reliability for the GQS and modified DISCERN scores was evaluated using the intraclass correlation coefficient (ICC). RESULTS Video characteristics Among the 86 videos analyzed, 49 (56.98%) were uploaded by medical sources and 37 (43.02%) by non-medical sources. The largest proportion of uploaders were health channels and news/magazine channels, each accounting for 23.26%, followed by hospital channels (22.09%). By content, informational videos comprised 48.84% (n=42), followed by patient experience videos (43.02%, n=37). Collectively, more than 90% of the videos were categorized as informational or patient experience videos ( Table 3 ). Informational content accounted for 61.22% of medical uploaders, whereas patient experience content accounted for 64.86% of non-medical uploaders, indicating distinct content tendencies across uploader groups. Table 3. Video Classification. Information about disease Patient experience Surgical technique Advertisement Un-classified Total Medical Hospital channel 10 7 2 0 0 19 (22.09) Doctor 6 1 3 0 0 10 (11.63) Health channel 14 5 0 1 0 20 (23.26) Non-medical Patient 2 7 0 0 0 9 (10.47) News, magazine, talk show 5 15 0 0 0 20 (23.26) Commercial 0 0 0 0 0 0 (0.0) Unclassified 5 2 0 0 1 8 (9.30) Total 42 (48.84) 37 (43.02) 5 (5.81) 1 (1.16) 1 (1.16) 86 (100) Open in a new tab The 86 videos accumulated a total of 16240535 views (median 52018; range 21361–3456074) and 13433 comments (median 30; range 0–2042). The single most-viewed video (3456074 views), uploaded by a health channel, addressed risk factors, symptoms, diagnosis, and treatment of ovarian cancer, and received the highest numbers of likes (27593) and dislikes (1333). Comparison by uploader Non-medical videos were significantly longer in duration (median 474 seconds vs. 225 seconds, p =0.012), received significantly more likes (median 768 vs. 283, p =0.001), and had a higher number of comments (median 41 vs. 20, p =0.046). The like ratio was also significantly higher in non-medical videos (98.06 vs. 96.07, p =0.017). No significant differences were observed in subscribers, view counts, dislike counts, or view ratios ( Table 4 ). Table 4. Video Characteristics by Uploader. Total Medical (n=49) Non-medical (n=37) p Subscribers 97900 (78–14500000) 26700 (200–8930000) 122000 (78–14500000) 0.194 Views 52018 (21361–3456074) 50501 (21361–3456074) 54840 (21746–1325594) 0.913 Days from upload 1550.50 (44–5297) 1648 (224–5297) 1323 (44–4843) 0.189 Video length (sec) 308 (30–4438) 225 (30–2583) 474 (69–4438) 0.012 Likes 418 (11–27593) 283 (11–27593) 768 (0–1224) 0.001 Dislikes 15 (0–1333) 15 (0–1333) 15 (0–1224) 0.650 Comments 30 (0–2042) 20 (0–938) 41 (0–2042) 0.046 Like ratio 96.86 (68.89–100.00) 96.07 (83.75–100.00) 98.06 (68.89–100.00) 0.017 View ratio 44.76 (6.99–4262.69) 43.50 (7.48–2304.05) 73.57 (7.36–4276.59) 0.335 Video power index 44.58 (6.99–4262.69) 40.28 (6.99–2197.87) 40.28 (6.99–2197.87) 0.172 Open in a new tab Like ratio: number of likes divided by the total number of likes and dislikes, multiplied by 100. View ratio: number of views per day since upload. Video power index: multiplying the like ratio by the view ratio and dividing the product by 100. Comparison by content For content-based comparisons, analyses were restricted to the major content categories—informational videos and patient experience videos—because other categories had extremely small sample sizes. Between these two content types, no significant differences were observed in view counts or comments. However, the like ratio was significantly higher in patient experience compared with informational videos (median 97.63% vs. 96.53%, p =0.015). Other indices, including view ratio and video power index, did not differ significantly between the two groups ( Table 5 ). Table 5. Video Characteristics by Content. Information about disease (n=42) Patient experience (n=37) Surgical technique (n=5) Advertisement (n=1) Un-classified (n=1) p * Subscribers 86700 (200–8930000) 114000 (78–14500000) 71730 (5090–176000) 1000 7080 0.375 Views 52670.50 (23663–3456074) 53535 (21361–1325594) 90402 (31401–243906) 56974 40606 0.945 Days from upload 1479 (53–5297) 1533 (44–5140) 2531 (1409–4294) 2751 1568 0.516 Video length (sec) 260.50 (44–4438) 310 (30–3041) 1472 (597–2030) 124 104 0.049 Likes 404 (34–27593) 446.50 (15–18457) 331 (150–1100) 11 0 0.285 Dislikes 17 (0–1333) 14 (0–1224) 40.40 (10–88) 0 0 0.209 Comments 22 (0–938) 22 (0–2042) 27 (16–67) 0 0 0.129 Like ratio 96.53 (68.89–100.00) 97.63 (83.75–100.00) 92.08 (85.71–97.07) 100 0 0.015 View ratio 62.44 (7.36–2304.05) 33.31 (7.44–4276.59) 19.57 (9.37–96.37) 20.71 25.9 0.457 Video power index 60.73 (6.99–2197.87) 34.59 (7.21–4262.69) 18.99 (8.03–89.23) 20.71 - 0.376 Open in a new tab Like ratio: number of likes divided by the sum of likes and dislikes, multiplied by 100. View ratio: number of views per day since upload. Video power index: multiplying the like ratio by the view ratio and dividing the product by 100. * Statistical comparisons were performed only between informational and patient experience videos. Categories with extremely small sample sizes (surgical technique, advertisement, and unclassified) were excluded from inferential analyses and are presented for descriptive purposes only. Reliability and quality assessment Inter-rater reliability among the three reviewers was excellent, with an ICC of 0.923 [95% confidence interval (CI), 0.889–0.947; p <0.001] for GQS and 0.990 (95% CI, 0.985–0.993; p <0.001) for modified DISCERN scores ( Table 6 ). The mean GQS across all videos was 3.50±1.06, corresponding to moderate-to-good quality. The mean modified DISCERN score was 2.24±1.12, indicating relatively low informational reliability. Most videos scored highly for clarity of aims (mean 0.97), but much lower for reliable sources (0.47), listing of additional sources (0.19), and mention of uncertainties (0.05). Table 6. ICCs of GQS and Modified DISCERN Score. GQS Modified DISCERN score ICC 95% CI p ICC 95% CI p 0.923 0.889–0.947 <0.001 0.990 0.985–0.993 <0.001 Open in a new tab GQS, Global Quality Score; ICC, intraclass correlation coefficient; CI, confidence interval. Group comparisons The modified DISCERN score was significantly higher in the medical group than in the non-medical group (median 3.00 vs. 1.00, p =0.001), whereas no significant difference in GQS was observed between the two groups ( Table 7 ). Content-based comparisons were limited to informational and patient experience videos, and informational videos achieved significantly higher GQS than patient experience videos (median 4.50 vs. 3.00, p <0.001). Modified DISCERN scores were also significantly higher in informational videos compared with patient experience videos (median 3.00 vs. 1.00, p <0.001) ( Table 8 ). Table 7. Averaged GQS and Modified DISCERN Score by Uploader. Medical (n=49) Non-medical (n=37) p GQS 4.00 (1.00–5.00) 3.18 (1.67–5.00) 0.120 Modified DISCERN score 3.00 (0.00–4.00) 1.00 (1.00–4.00) 0.001 Open in a new tab GQS, Global Quality Score. Table 8. Averaged GQS and Modified DISCERN Score by Content. Information about disease (n=42) Patient experience (n=37) Surgical technique (n=5) Advertisement (n=1) Un-classified (n=1) p * GQS 4.50 (1.67–5.00) 3.00 (1.00–4.33) 3.00 (2.67–4.67) 1.33 3.33 <0.001 Modified DISCERN score 3.00 (1.00–4.00) 1.00 (0.00–2.67) 3.00 (3.00–4.00) 2.00 3.67 <0.001 Open in a new tab GQS, Global Quality Score. * Statistical comparisons were performed only between informational and patient experience videos. Content categories with extremely small sample sizes (surgical technique, advertisement, and unclassified) were excluded from inferential analyses and are presented for descriptive purposes only. DISCUSSION This study systematically evaluated the educational quality and informational reliability of YouTube videos related to ovarian cancer using the GQS and modified DISCERN criteria. Overall quality was moderate-to-good, whereas informational reliability was comparatively low. These findings align with prior analyses of cancer-related content on YouTube, which have reported variable educational quality and frequent deficits in source attribution and evidence citation across disease areas. 9 , 10 , 11 , 12 , 13 , 14 , 15 Uploader type and content category were strongly associated with performance on the reliability metric. Videos from medical sources achieved higher modified DISCERN scores than those from non-medical sources, consistent with the expectation that professionally produced content more often references guideline-based or peer-reviewed information. In contrast, non-medical videos tended to be longer and achieved greater engagement (likes and comments), echoing prior observations that narrative-driven media may attract attention without necessarily improving factual accuracy. 9 , 11 , 12 Engagement metrics on YouTube do not directly reflect informational quality, as they are influenced by factors such as narrative appeal and uploader popularity. Accordingly, we focused on descriptive group-level patterns rather than correlation analyses between engagement and quality scores. A notable observation in this study was the popularity of patient-experience videos featuring public figures (e.g., actors, athletes, journalists). Celebrity disclosures are known to shape health information–seeking behavior and screening interest—the so-called “Angelina Jolie effect” in breast cancer is a well-described example. 14 Such narratives may provide psychosocial validation and foster community support for patients and caregivers; however, they can also transmit anecdotal recommendations that are not evidence-based. Despite these concerns, lower reliability scores do not necessarily indicate a lack of patient value. Given the absence of effective population-based screening and the high likelihood of advanced-stage diagnosis and recurrence, video content with experiential or narrative elements may offer emotional reassurance, thereby complementing clinic-based education even when formal evidence citation is limited. Therefore, these findings suggest that professional engagement in the creation of educational content may enhance the dissemination of accurate, guideline-concordant information, particularly when videos are supplemented with references to established patient guidelines and clinical trial resources. Conversely, videos addressing recent therapeutic advances (e.g., immunotherapy, novel fluorescent markers, hyperthermic intraperitoneal chemotherapy) demonstrated high scores on both GQS and modified DISCERN scores in our analysis. This pattern supports the premise that professionally curated videos—particularly those produced by academic centers, hospitals, or specialty organizations—can effectively disseminate up-to-date, evidence-based information when structured with clear aims, balanced benefits/harms, and explicit citations. 9 , 10 , 11 , 12 Surgical procedure videos (robotic, laparoscopic, or open hysterectomy) appeared more clinician-oriented than patient-oriented, underscoring the need to tailor content to its intended audience and to clearly label target users. Domain-level weaknesses were concentrated in specific DISCERN items: reliable sources, additional resources, and acknowledgement of uncertainty were seldom provided. Similar gaps have been reported across diverse medical topics on YouTube, including spine disorders, rheumatoid arthritis, and the human gut microbiome. 6 , 7 , 8 , 16 Addressing these deficits is feasible with lightweight, standards-based practices for health video production. At minimum, creators should: 1) articulate explicit aims; 2) cite up-to-date, authoritative sources (e.g., NCCN/ESMO guidelines, peer-reviewed studies); 3) present balanced information on benefits, risks, and alternatives; 4) provide links to additional high-quality resources; and 5) identify uncertainties or areas with limited evidence. Adopting such a checklist would directly improve performance on the modified DISCERN domains and enhance trustworthiness. The implications of these findings extend beyond individual content creators. Platform-level interventions, such as incorporating health-context labels, verified institutional attribution, and standardized reference fields, could improve transparency and reliability. In addition, recommendation algorithms that integrate evidence provenance alongside user engagement metrics may further enhance the visibility of high-quality content. Collaboration with professional societies could enable the development of curated playlists addressing common patient concerns, including diagnosis, staging, treatment options, and survivorship. Furthermore, the use of analytic tools to iteratively refine clarity and accessibility would support continuous improvement. Collectively, these strategies align with previous recommendations to ensure that the broad digital reach of online platforms is matched by improvements in the informational quality of patient education resources. 11 , 12 , 13 This study has limitations. First, the analysis was limited to English-language YouTube videos and to the most-viewed content identified at a single time point; temporal drift in rankings and metadata is possible. Second, the search strategy was restricted to a single disease-specific keyword (“ovarian cancer”). This strategy was chosen to prioritize relevance and specificity; however, it may have excluded videos with alternative search terms such as tumor or mass. Future studies using multi-keyword strategies may better explore the wider spectrum of patient search behaviors. Third, video quality and informational reliability were evaluated using the GQS and modified DISCERN criteria applied from a clinician-oriented perspective. Although these instruments are validated and widely used to assess transparency, structure, and reliability of health information, they do not capture patient-perceived usefulness, emotional support, or understandability. Consequently, the real-world educational impact of these videos from the patient perspective could not be directly assessed. Finally, although inter-rater reliability among evaluators was excellent, assessments were conducted by obstetrics and gynecology residents rather than subspecialty experts or patients. While residents possess foundational clinical knowledge relevant to ovarian cancer education, their level of experience may differ from that of board-certified specialists. Future research incorporating multidisciplinary expert panels and patient-reported evaluations may provide a more comprehensive understanding of the educational value of online video content. Future studies should incorporate multilingual sampling, longitudinal surveillance, and mixed-methods designs that link video characteristics to patient-centered outcomes (knowledge gain, decisional conflict, anxiety) and behavioral endpoints (consultation preparedness, adherence). Randomized studies comparing professionally produced videos with narrative content could clarify causal effects on learning and engagement. In summary, YouTube provides substantial reach for disseminating information on ovarian cancer; however, a gap remains between content quality and reliability. Greater professional involvement and the application of standardized production practices, together with platform-level measures to enhance source transparency, may contribute to improving the overall quality of content while maintaining accessibility. Until such measures are widely implemented, it remains important for healthcare providers to guide patients toward credible, evidence-based video resources and to emphasize the need for critical appraisal when utilizing open-access platforms for health information. ACKNOWLEDGEMENTS We would like to express our gratitude to all those who helped us during the writing of this manuscript. Footnotes The authors have no potential conflicts of interest to disclose. AUTHOR CONTRIBUTIONS: Conceptualization: Sung Ho Park. Data curation: Jisong Hwang. Formal analysis: Jiye Kim. Investigation: Jiye Kim and Jisong Hwang. Methodology: Sung Taek Park. Project administration: Soo Young Jeong. Resources: Sung Ho Park. Software: Jiye Kim. 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