<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "journalpublishing.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="2.0" xml:lang="en" article-type="research-article"><front><journal-meta><journal-id journal-id-type="nlm-ta">JMIR Med Inform</journal-id><journal-id journal-id-type="publisher-id">medinform</journal-id><journal-id journal-id-type="index">7</journal-id><journal-title>JMIR Medical Informatics</journal-title><abbrev-journal-title>JMIR Med Inform</abbrev-journal-title><issn pub-type="epub">2291-9694</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">v14i1e95994</article-id><article-id pub-id-type="doi">10.2196/95994</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Accessibility and Decision-Supportive Quality of Benign Breast Disease Articles on Chinese Hospital WeChat Public Accounts: Nationwide Cross-Sectional Study</article-title></title-group><contrib-group><contrib contrib-type="author"><name name-style="western"><surname>Li</surname><given-names>Jing</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>He</surname><given-names>Chenxi</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff3">3</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Wang</surname><given-names>Shi</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff3">3</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Ke</surname><given-names>Lichi</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Yu</surname><given-names>Hongfan</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff5">5</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Wei</surname><given-names>Xing</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff6">6</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Lei</surname><given-names>Cheng</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff6">6</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Hu</surname><given-names>Dehua</given-names></name><degrees>BS</degrees><xref ref-type="aff" rid="aff7">7</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Zhang</surname><given-names>Zhiliang</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff8">8</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Zhou</surname><given-names>Wen</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Li</surname><given-names>Min</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Wu</surname><given-names>Wenlin</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff3">3</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Xu</surname><given-names>Wei</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff3">3</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Bai</surname><given-names>Jin</given-names></name><degrees>BS</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><name name-style="western"><surname>Shi</surname><given-names>Qiuling</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff3">3</xref></contrib></contrib-group><aff id="aff1"><institution>State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University</institution><addr-line>No. 1, Medical College Road, Yuzhong District</addr-line><addr-line>Chongqing</addr-line><country>China</country></aff><aff id="aff2"><institution>School of Clinical Medicine, Chongqing Medical and Pharmaceutical College</institution><addr-line>Chongqing</addr-line><country>China</country></aff><aff id="aff3"><institution>School of Public Health, Chongqing Medical University</institution><addr-line>Chongqing</addr-line><country>China</country></aff><aff id="aff4"><institution>Department of Breast and Thyroid Surgery, People's Hospital of Jinniu District</institution><addr-line>Chengdu</addr-line><country>China</country></aff><aff id="aff5"><institution>Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University)</institution><addr-line>Chongqing</addr-line><country>China</country></aff><aff id="aff6"><institution>Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital &#x0026; Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China</institution><addr-line>Chengdu</addr-line><country>China</country></aff><aff id="aff7"><institution>Lueyang County Maternal and Child Health and Family Planning Service Center, Lueyang County</institution><addr-line>Shanxi</addr-line><country>China</country></aff><aff id="aff8"><institution>Breast Center, Three Gorges Hospital of Chongqing University</institution><addr-line>Chongqing</addr-line><country>China</country></aff><contrib-group><contrib contrib-type="editor"><name name-style="western"><surname>Coristine</surname><given-names>Andrew</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name name-style="western"><surname>Urquhart</surname><given-names>Christine</given-names></name></contrib><contrib contrib-type="reviewer"><name name-style="western"><surname>Kidholm</surname><given-names>Kristian</given-names></name></contrib></contrib-group><author-notes><corresp>Correspondence to Qiuling Shi, PhD, State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, No. 1, Medical College Road, Yuzhong District, Chongqing, 400016, China, 86 18290585397; <email>qshi@cqmu.edu.cn</email></corresp></author-notes><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>9</day><month>10</month><year>2026</year></pub-date><volume>14</volume><elocation-id>e95994</elocation-id><history><date date-type="received"><day>24</day><month>03</month><year>2026</year></date><date date-type="rev-recd"><day>04</day><month>09</month><year>2026</year></date><date date-type="accepted"><day>09</day><month>09</month><year>2026</year></date></history><copyright-statement>&#x00A9; Jing Li, Chenxi He, Shi Wang, Lichi Ke, Hongfan Yu, Xing Wei, Cheng Lei, Dehua Hu, Zhiliang Zhang, Wen Zhou, Min Li, Wenlin Wu, Wei Xu, Jin Bai, Qiuling Shi. Originally published in JMIR Medical Informatics (<ext-link ext-link-type="uri" xlink:href="https://medinform.jmir.org">https://medinform.jmir.org</ext-link>), 9.10.2026. </copyright-statement><copyright-year>2026</copyright-year><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="https://medinform.jmir.org/">https://medinform.jmir.org/</ext-link>, as well as this copyright and license information must be included.</p></license><self-uri xlink:type="simple" xlink:href="https://medinform.jmir.org/2026/1/e95994"/><abstract><sec><title>Background</title><p>Shared decision-making (SDM) is critical for managing clinically ambiguous conditions such as benign breast disease (BBD), requiring reliable health information. In China, WeChat public accounts (WPAs) run by hospitals serve as a major health information channel, yet their quality remains unevaluated.</p></sec><sec><title>Objective</title><p>This study aimed to evaluate the quality of BBD-related articles on hospital WPAs in China.</p></sec><sec sec-type="methods"><title>Methods</title><p>Using a multistage stratified random sampling approach, we conducted a nationwide survey examining the quality of articles published from January 2020 to November 2024. The articles were evaluated for accessibility (readability, image quality, and user-friendliness) and SDM readiness (credibility, balance, and decision relevance) using validated instruments and content analysis.</p></sec><sec sec-type="results"><title>Results</title><p>Among 276,617 articles from 158 sampled hospitals, 464 were related to BBD. Accessibility was generally good: 72.4% (336/464) were written at a junior high school reading level. Visual presentation was acceptable, although only 10.2% (42/410) had complete image labeling. The Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) scores showed moderate understandability (mean 73.6%, SD 17.3%) but limited actionability (mean 50%, SD 23.2%). For SDM readiness, the mean Quality Evaluation Scoring Tool (QUEST) score was 14.9 (SD 4.7) out of 28, with treatment-related articles scoring lower than prevention-related articles (<italic>P</italic>&#x003C;.001). In the balance evaluation, using Brief DISCERN, 84.3% (182/216) of articles were below the high-quality threshold, often lacking information on treatment risks and quality-of-life impacts. Decision relevance revealed variability in surgical recommendations, especially regarding excision criteria and pregnancy-related guidance.</p></sec><sec sec-type="conclusions"><title>Conclusions</title><p>WPA articles on BBD showed good accessibility but substantial gaps in SDM support, particularly in information balance and decision relevance. Standardized guidelines and quality improvements are needed to enhance patient-centered online health services.</p></sec></abstract><kwd-group><kwd>telemedicine</kwd><kwd>benign breast disease</kwd><kwd>social media</kwd><kwd>shared decision-making</kwd><kwd>patient participation</kwd><kwd>WeChat</kwd></kwd-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Benign breast disease (BBD) poses a major clinical challenge, accounting for 55% to 90% of all breast lesions [<xref ref-type="bibr" rid="ref1">1</xref>-<xref ref-type="bibr" rid="ref3">3</xref>]. The risk of subsequent breast cancer varies considerably, depending on the degree of epithelial proliferation and atypia [<xref ref-type="bibr" rid="ref4">4</xref>-<xref ref-type="bibr" rid="ref6">6</xref>]. Unlike malignancy, BBD lacks evidence-based management protocols and often requires individualized strategies such as surveillance, biopsy, surgery, or follow-up [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref8">8</xref>]. This clinical uncertainty necessitates preference-sensitive care, where comparable treatment options require decisions tailored to patient-specific risks and informed preferences [<xref ref-type="bibr" rid="ref9">9</xref>-<xref ref-type="bibr" rid="ref11">11</xref>]. Shared decision-making (SDM), recommended by the American Society of Breast Surgeons and European guidelines [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref12">12</xref>], is particularly relevant for younger female patients, who constitute most BBD cases [<xref ref-type="bibr" rid="ref13">13</xref>-<xref ref-type="bibr" rid="ref15">15</xref>]. Evidence shows that SDM in surgical care reduces decisional conflict, anxiety, and unnecessary procedures while improving the overall quality of care and reducing costs [<xref ref-type="bibr" rid="ref16">16</xref>-<xref ref-type="bibr" rid="ref19">19</xref>]. Despite these well-documented benefits, the routine adoption of SDM in clinical practice remains suboptimal [<xref ref-type="bibr" rid="ref20">20</xref>]. A critical barrier worldwide is the difficulty of integrating patient decision aids into clinical workflows, particularly in resource-constrained settings [<xref ref-type="bibr" rid="ref21">21</xref>,<xref ref-type="bibr" rid="ref22">22</xref>].</p><p>Telemedicine plays a supportive and interactive role in facilitating decision-making [<xref ref-type="bibr" rid="ref23">23</xref>]. In China, the national &#x201C;Internet + Health Care&#x201D; initiative has rapidly expanded the model of internet hospitals, with 3340 internet hospitals nationwide as of September 2024 [<xref ref-type="bibr" rid="ref24">24</xref>]. WeChat public accounts (WPAs), China&#x2019;s dominant social media platform with 1.385 billion active users, have been adopted by most tertiary hospitals to integrate professional expertise with broad digital reach [<xref ref-type="bibr" rid="ref25">25</xref>,<xref ref-type="bibr" rid="ref26">26</xref>]. Notably, more than 60% of Chinese health consumers prefer WeChat as their primary source of medical information, highlighting the potential of WPAs as tools for patient education and decision support [<xref ref-type="bibr" rid="ref27">27</xref>-<xref ref-type="bibr" rid="ref29">29</xref>]. Among the vast and varied health information available online, hospital-operated WPAs carry unique weight as official institutional voices and tend to be regarded by patients as more credible channels for medical guidance. As time-constrained clinical consultations increasingly drive patients to seek health information online, the BBD-related content patients encounter on WPAs before visits may shape the knowledge, expectations, and preferences they bring into clinical encounters, with potential implications for the starting point and quality of SDM [<xref ref-type="bibr" rid="ref30">30</xref>,<xref ref-type="bibr" rid="ref31">31</xref>].</p><p>Despite this promise, concerns persist regarding the quality and relevance of WPA content for decision-making [<xref ref-type="bibr" rid="ref25">25</xref>,<xref ref-type="bibr" rid="ref32">32</xref>]. Although prior research has evaluated breast cancer information on public platforms such as Twitter and YouTube, a systematic assessment of BBD-related content on hospital-based social media is lacking [<xref ref-type="bibr" rid="ref33">33</xref>,<xref ref-type="bibr" rid="ref34">34</xref>]. To address this gap, our nationwide study evaluates whether hospital-based WPAs provide accessible, decision-supportive content that enables informed patient participation in SDM, offering novel insights into digital health communication within China&#x2019;s emerging internet hospital ecosystem.</p></sec><sec id="s2" sec-type="methods"><title>Methods</title><sec id="s2-1"><title>Study Design and Sampling</title><p>A nationwide, multistage random sampling approach was used across 7 geographic regions in China. In each region, 3 provinces were randomly selected, and 2 municipal-level cities were randomly chosen per province (Table S1 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). Following the Hospital Classification Management Measures [<xref ref-type="bibr" rid="ref35">35</xref>], 4 hospitals were sampled per city: 1 maternal and child health (MCH) hospital, 1 traditional Chinese medicine (TCM) hospital, and 2 general hospitals (including 1 tertiary and 1 secondary).</p><p>Articles published between January 1, 2020, and November 30, 2024, were collected via automated web scraping with manual verification. Chinese keyword searches identified BBD-related content (Table S2 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). Articles explicitly addressing BBD were included, while those addressing malignancy only, administrative or promotional content, or unrelated topics were excluded (Table S3 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>).</p></sec><sec id="s2-2"><title>Data Extraction and Screening</title><p>Data were extracted at both institutional and article levels. Briefly, institutional data&#x2014;including hospital characteristics, geographic location, bed capacity, and digital engagement&#x2014;were sourced from official websites and the National Health Commission database. Article-level data&#x2014;including publication date, view count, and article content&#x2014;were obtained directly from the sampled WeChat (Tencent Holdings) public accounts. All article view counts were captured on March 16, 2025; views per day were calculated as cumulative views divided by the number of calendar days since publication.</p></sec><sec id="s2-3"><title>Evaluation Framework and Assessment Tools</title><p>Following established criteria for evaluating online health information [<xref ref-type="bibr" rid="ref36">36</xref>] and the International Patient Decision Aid Standards (IPDAS) [<xref ref-type="bibr" rid="ref37">37</xref>], we developed a structured framework encompassing 2 major domains: accessibility and readiness (<xref ref-type="fig" rid="figure1">Figure 1</xref>).</p><fig position="float" id="figure1"><label>Figure 1.</label><caption><p>Conceptual framework for evaluating benign breast disease articles on Chinese hospital WeChat public accounts (2020&#x2010;2024). PEMAT-P: Patient Education Materials Assessment Tool for Printable Materials; QUEST: Quality Evaluation Scoring Tool.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="medinform_v14i1e95994_fig01.png"/></fig><sec id="s2-3-1"><title>Accessibility</title><p>Accessibility assessed how clearly and effectively health information was delivered and the extent to which the materials supported patient engagement. It comprised 3 dimensions: readability, image quality, and user-friendliness.</p><p>First, readability was assessed using the Chinese Text Reading Difficulty Grading Tool, which applies natural language processing techniques to estimate text difficulty based on linguistic features from the Chinese textbook corpus [<xref ref-type="bibr" rid="ref38">38</xref>,<xref ref-type="bibr" rid="ref39">39</xref>]. Readability scores were automatically computed for each article without requiring human raters.</p><p>Second, image quality was assessed using the 5C Image Checklist, adapted from the Centers for Disease Control and Prevention&#x2019;s (CDC) Simply Put guide [<xref ref-type="bibr" rid="ref40">40</xref>,<xref ref-type="bibr" rid="ref41">41</xref>]. The checklist evaluates 5 domains: clarity, contribution, contradiction, caption, and cultural appropriateness. Each article was independently rated by 2 trained reviewers (SW and CH), with discrepancies resolved through discussion.</p><p>Finally, user-friendliness was evaluated with the Chinese version of the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P), including understandability and actionability, with scores reported as continuous percentages (higher values indicate better quality) [<xref ref-type="bibr" rid="ref42">42</xref>]. To incorporate patient-centered perspectives, 20% (43/216) of treatment-related articles were randomly selected and independently evaluated by a panel of 14 adult patients using the PEMAT-P tool. Evaluators were recruited by convenience sampling from hospitals and had no prior experience in evaluating patient education materials. Their ages ranged from 23 to 42 years; all held bachelor&#x2019;s or master&#x2019;s degrees, and 6 had a personal history of benign breast nodules. Each article was assessed by 2 independent evaluators, with each evaluator reviewing approximately 6 articles.</p></sec><sec id="s2-3-2"><title>Readiness</title><p>Readiness reflected the extent to which article content could support patients&#x2019; participation in SDM. It was assessed across 3 dimensions: credibility, balance, and decision relevance.</p><p>First, credibility was measured using the Quality Evaluation Scoring Tool (QUEST) [<xref ref-type="bibr" rid="ref43">43</xref>], covering 6 domains (authorship, attribution, conflict of interest, currency, complementarity, and tone; score range 0-28). Assessments were performed by 2 doctoral students in biomedical engineering with clinical training (JL and WZ).</p><p>Second, balance was evaluated using the Brief DISCERN, a validated short version of the DISCERN tool designed for treatment information [<xref ref-type="bibr" rid="ref44">44</xref>,<xref ref-type="bibr" rid="ref45">45</xref>]. Six items were assessed: information source, information currency, treatment mechanism, treatment benefits, treatment risks, and impact on quality of life. A score of &#x2265;16 indicated high quality [<xref ref-type="bibr" rid="ref45">45</xref>]. Two surgeons (LK and ZZ) independently assessed all treatment-related articles.</p><p>Finally, decision relevance was examined through directed content analysis of (1) clinical contexts for which treatment was advised and (2) treatment modalities suggested [<xref ref-type="bibr" rid="ref46">46</xref>]. A coding guide was iteratively developed from 10% (22/216) of treatment-related articles and refined through consensus discussion between 2 reviewers (JL and XW). The final coding guide is presented in Table S4 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>. All 216 treatment-related articles were then independently double-coded, and discrepancies were resolved through discussion.</p></sec></sec><sec id="s2-4"><title>Statistical Analysis</title><p>Descriptive statistics summarized article and hospital characteristics. Categorical variables were presented as frequencies and percentages. Continuous variables were reported as means (SDs) for normally distributed data or medians (IQR) for skewed data. Chi-square tests were used for categorical variables. Skewed continuous variables were compared using the Kruskal-Wallis test and Bonferroni-corrected Dunn post hoc tests. Interrater reliability for all continuous quality scores (QUEST, Brief DISCERN, and PEMAT-P) was assessed using 2-way random-effects intraclass correlation coefficients for single-rating, absolute agreement (ICC<sub>2,1</sub>). For the categorical variables (5C Image Checklist), Cohen kappa was calculated for each domain. For decision relevance content analysis, percentage agreement was used (with an &#x2265;85% threshold).</p><p>Linear regression was used to assess temporal trends in publication volume, with year as the independent variable. Given the nested structure of the data (articles clustered within hospitals), linear mixed-effects models with hospital as a random intercept were fitted using the <italic>lme4</italic> package in R (version 4.5.0; R Foundation for Statistical Computing). <italic>P</italic> values were obtained by likelihood ratio tests. A 2-sided <italic>P</italic> value &#x003C;.05 was considered statistically significant. NVivo (version 20; QSR International) was used for qualitative data analysis. Datawrapper (Datawrapper GmbH) was used to visualize geographic distribution. Origin 2025 (OriginLab) was used for data visualization.</p></sec><sec id="s2-5"><title>Ethical Considerations</title><p>This study was approved by the ethics committee of Chongqing University Three Gorges Hospital (KLS2025122). As all WPA articles assessed were publicly available and contained no identifying information, the ethics committee granted a waiver of informed consent. Adult patients participated in article evaluation with oral consent. All data were anonymized, and no compensation was provided.</p></sec></sec><sec id="s3" sec-type="results"><title>Results</title><sec id="s3-1"><title>Study Sample and Article Selection</title><p>Multistage random sampling yielded 40 city-level units after excluding 2 units: Turpan (Xinjiang) had no independent TCM hospital, and Haibei Prefecture (Qinghai) had no tertiary general hospital at the time of sampling. Of the 160 hospitals expected under the sampling protocol, 158 were included (<xref ref-type="fig" rid="figure2">Figure 2</xref>). From these hospitals, 265 WPAs were identified (<xref ref-type="fig" rid="figure2">Figure 2</xref>), from which 276,617 articles published between January 2020 and November 2024 were retrieved. After keyword screening, 5526 potentially relevant articles were identified, and 464 BBD-related articles met the inclusion criteria.</p><fig position="float" id="figure2"><label>Figure 2.</label><caption><p>Flowchart of article screening for the nationwide cross-sectional survey of benign breast disease content on Chinese hospital WeChat public accounts. MCH: maternal and child health; TCM: traditional Chinese medicine.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="medinform_v14i1e95994_fig02.png"/></fig></sec><sec id="s3-2"><title>Hospital Characteristics and Digital Engagement</title><p>The sample comprised 39 tertiary general hospitals, 40 secondary general hospitals, 39 TCM hospitals, and 40 MCH hospitals (<xref ref-type="table" rid="table1">Table 1</xref>). Overall, 94 (59.5%) hospitals were tertiary-level, and 64 (40.5%) were secondary-level.</p><p>In terms of digital engagement, 155 (98.1%) hospitals operated WPAs, achieving full coverage among tertiary general and TCM hospitals. In contrast, only 65.2% (103/158) maintained official websites, ranging from 89.7% (35/39) of tertiary hospitals to 27.5% (11/40) of secondary general hospitals. Certification for internet hospitals was limited (40/158, 25.3%), with the highest proportion among tertiary general hospitals (22/39, 56.4%).</p><p>Over the past 5 years, publication volumes on WPAs showed no significant regional differences across China (<italic>P</italic>&#x003E;.05; Figure S1 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). By hospital type, the median 5-year publication volume ranged from 271 (IQR 83-759) in secondary general hospitals to 743 (IQR 188-1250) in MCH hospitals; secondary general hospitals had significantly lower publication volumes than the other 3 hospital types (<xref ref-type="table" rid="table1">Table 1</xref>).</p><table-wrap id="t1" position="float"><label>Table 1.</label><caption><p>Baseline characteristics of Chinese public hospitals sampled across 7 geographic regions (N=158).</p></caption><table id="table1" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Characteristics</td><td align="left" valign="bottom">Total hospitals</td><td align="left" valign="bottom">Tertiary general hospitals (n=39)</td><td align="left" valign="bottom">Secondary general hospitals (n=40)</td><td align="left" valign="bottom">Traditional Chinese medicine hospitals (n=39)</td><td align="left" valign="bottom">Maternal and child health hospitals (n=40)</td></tr></thead><tbody><tr><td align="left" valign="top" colspan="6">Hospital profile</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Tertiary hospital, n (%)</td><td align="left" valign="top">94 (59.5)</td><td align="left" valign="top">39 (100)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">30 (76.9)</td><td align="left" valign="top">25 (62.5)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Beds available, mean (SD)</td><td align="left" valign="top">1058 (980)</td><td align="left" valign="top">2132 (1015)</td><td align="left" valign="top">388 (214)<sup><xref ref-type="table-fn" rid="table1fn1">a</xref></sup></td><td align="left" valign="top">1046 (751)</td><td align="left" valign="top">589 (563)<sup><xref ref-type="table-fn" rid="table1fn2">b</xref></sup></td></tr><tr><td align="left" valign="top" colspan="6">Digital engagement, n (%)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Internet hospital</td><td align="left" valign="top">40 (25.3)</td><td align="left" valign="top">22 (56.4)</td><td align="left" valign="top">2 (5)</td><td align="left" valign="top">10 (25.6)</td><td align="left" valign="top">6 (15)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Hospitals with official websites</td><td align="left" valign="top">103 (65.2)</td><td align="left" valign="top">35 (89.7)</td><td align="left" valign="top">11 (27.5)</td><td align="left" valign="top">31 (79.5)</td><td align="left" valign="top">27 (67.5)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Hospitals with WeChat accounts</td><td align="left" valign="top">155 (98.1)</td><td align="left" valign="top">39 (100)</td><td align="left" valign="top">38 (95)</td><td align="left" valign="top">39 (100)</td><td align="left" valign="top">39 (97.5)</td></tr><tr><td align="left" valign="top">Annual publication volume per hospital, median (IQR)<sup><xref ref-type="table-fn" rid="table1fn3">c</xref></sup></td><td align="left" valign="top">562 (156-1213)</td><td align="left" valign="top">724 (95-1345)</td><td align="left" valign="top">271 (83-759)</td><td align="left" valign="top">680 (300-1586)</td><td align="left" valign="top">743 (188-1250)</td></tr></tbody></table><table-wrap-foot><fn id="table1fn1"><p><sup>a</sup>Beds available in secondary general hospitals were significantly lower than those in both tertiary general hospitals and traditional Chinese medicine hospitals (Kruskal-Wallis test followed by Dunn post hoc tests with Bonferroni corrections; <italic>P</italic>&#x003C;.05). </p></fn><fn id="table1fn2"><p><sup>b</sup>Beds available in maternal and child health hospitals were significantly lower than those in both tertiary general hospitals and traditional Chinese medicine hospitals (Kruskal-Wallis test followed by Dunn post hoc tests with Bonferroni corrections; <italic>P</italic>&#x003C;.05).</p></fn><fn id="table1fn3"><p><sup>c</sup>Publication volumes differed significantly among hospital categories (Kruskal-Wallis H=15.520; <italic>P</italic>=.001). Of the 6 pairwise Dunn post hoc comparisons with Bonferroni correction, 3 remained statistically significant: secondary general hospitals had significantly lower publication volumes than tertiary general hospitals (adjusted <italic>P</italic>=.01), traditional Chinese medicine hospitals (adjusted <italic>P</italic>&#x003C;.001), and maternal and child health hospitals (adjusted <italic>P</italic>=.03).</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3-3"><title>Quality Assessment of BBD-Related Articles</title><sec id="s3-3-1"><title>Overview</title><p>Of the 158 hospitals in the overall sample, 103 contributed at least 1 eligible BBD-related article, yielding 464 articles for analysis. Among these, 248 (53.4%) focused on prevention and 216 (46.6%) on treatment. Linear regression confirmed a significant upward trend in annual publication volume over the 5-year study period (<italic>&#x03B2;</italic>=15.7 articles per year, 95% CI 6.0-25.4; <italic>P</italic>=.01; Figure S2 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>).</p></sec><sec id="s3-3-2"><title>Readability</title><p>The mean reading grade level was 12.9 (SD 1.7, range 6-20) years. Among these materials, 15.7% (73/464) were written at a primary school level, the majority (336/464, 72.4%) at a junior high school level, and 11.9% (55/464) at a senior high school level, indicating that the overall text complexity was generally acceptable and appropriate for the literacy level of the general population (<xref ref-type="table" rid="table2">Table 2</xref>).</p><table-wrap id="t2" position="float"><label>Table 2.</label><caption><p>Multidimensional quality assessment of benign breast disease articles on Chinese hospital WeChat public accounts.<sup><xref ref-type="table-fn" rid="table2fn1">a</xref></sup></p></caption><table id="table2" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Dimensions and attributes</td><td align="left" valign="bottom">Articles</td></tr></thead><tbody><tr><td align="left" valign="top" colspan="2">Readability (n=464)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Reading grade level (years), mean (SD; range)</td><td align="left" valign="top">12.9 (1.7; 6-20)</td></tr><tr><td align="left" valign="top" colspan="2"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Reading grade level, n (%)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;Primary school level (age group: 6-11 years)</td><td align="left" valign="top">73 (15.7)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;Junior high school level (age group: 12-14 years)</td><td align="left" valign="top">336 (72.4)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;Senior high school level (age group: 15-18 years)</td><td align="left" valign="top">55 (11.9)</td></tr><tr><td align="left" valign="top" colspan="2">Image quality (n=410), n (%)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Images clearly recognizable</td><td align="left" valign="top">399 (97.3)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Image reinforcing the text</td><td align="left" valign="top">348 (84.9)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Image consistent with the text</td><td align="left" valign="top">408 (99.5)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Images with captions</td><td align="left" valign="top">42 (10.2)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Image culturally appropriate</td><td align="left" valign="top">408 (99.5)</td></tr><tr><td align="left" valign="top" colspan="2">User-friendliness (%; Patient Education Materials Assessment Tool for Printable Materials; n=43), mean (SD)</td></tr><tr><td align="left" valign="top">&#x2003;Understandability</td><td align="left" valign="top">73.6 (17.3)</td></tr><tr><td align="left" valign="top">&#x2003;Actionability</td><td align="left" valign="top">50.0 (23.2)</td></tr><tr><td align="left" valign="top" colspan="2">Credibility (n=464)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>QUEST<sup><xref ref-type="table-fn" rid="table2fn2">b</xref></sup> score, mean (SD)</td><td align="left" valign="top">14.9 (4.7)</td></tr><tr><td align="left" valign="top" colspan="2"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>QUEST score groups, n (%)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;&#x2264;7</td><td align="left" valign="top">34 (7.3)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;8-14</td><td align="left" valign="top">133 (28.7)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;15-21</td><td align="left" valign="top">277 (59.7)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;22-28</td><td align="left" valign="top">20 (4.3)</td></tr><tr><td align="left" valign="top" colspan="2">Balance (n=216)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Brief DISCERN score, median (IQR)</td><td align="left" valign="top">12.5 (10.5-14.5)</td></tr><tr><td align="left" valign="top" colspan="2"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Brief DISCERN score groups, n (%)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;6-10</td><td align="left" valign="top">53 (24.5)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;11-15</td><td align="left" valign="top">129 (59.7)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;16-20</td><td align="left" valign="top">34 (15.7)</td></tr><tr><td align="left" valign="top">&#x2003;&#x2003;21-30</td><td align="left" valign="top">0 (0)</td></tr></tbody></table><table-wrap-foot><fn id="table2fn1"><p><sup>a</sup>Readability and Quality Evaluation Scoring Tool (QUEST) scores were calculated based on all included articles (n=464); image quality assessment was based on articles containing images (n=410); Brief DISCERN scores were based on treatment-related articles (n=216); and Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) scores were based on a randomly selected subsample of 43 treatment-related articles.</p></fn><fn id="table2fn2"><p><sup>b</sup>QUEST: Quality Evaluation Scoring Tool.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3-3-3"><title>Image Quality</title><p>Of 410 articles containing images, most visuals were clear (n=399, 97.3%) and consistent with the text (n=408, 99.5%), and 84.9% (n=348) reinforced the content. However, image labeling was inadequate: only 10.2% (n=42) included fully labeled captions, 23.7% (n=97) included partially labeled captions, and 66.1% (n=271) had no labels. Nearly all images (n=408, 99.5%) were culturally appropriate. Cohen &#x03BA; for the 5C Image Checklist ranged from 0.847 to 1.000 across the 5 domains.</p></sec><sec id="s3-3-4"><title>User-Friendliness</title><p>Forty-three articles, randomly sampled from 216 (20%) treatment-related articles, were included. The interrater intraclass correlation coefficient (ICC<sub>2,1</sub>) was 0.51. Mean understandability was 73.6% (SD 17.3%), and mean actionability was 50% (SD 23.2%).</p></sec><sec id="s3-3-5"><title>Credibility</title><p>The mean QUEST score was 14.9 (SD 4.7). Only 4.3% (20/464) of articles scored &#x003E;21, while 36% (167/464) of articles scored &#x2264;14 (<xref ref-type="table" rid="table2">Table 2</xref>). Interrater agreement for the QUEST total score was 0.79 (ICC<sub>2,1</sub>). Prevention-related articles scored higher than treatment-related articles (mean 15.8, SD 3.8 vs mean 13.8, SD 5.3; <italic>P</italic>&#x003C;.001; Table S5 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>).</p><p>In multivariable analysis, article content was the only factor independently associated with credibility (<italic>&#x03B2;</italic>=&#x2212;2.16; <italic>P</italic>&#x003C;.001). Neither hospital level nor service mode showed a significant independent association with QUEST scores. For article dissemination, all 3 factors were independently associated with views per day: tertiary hospitals (<italic>&#x03B2;</italic>=0.38; <italic>P</italic>=.02), internet hospitals (<italic>&#x03B2;</italic>=0.35; <italic>P</italic>=.008), and treatment-related articles (<italic>&#x03B2;</italic>=0.20; <italic>P</italic>=.006) each showed higher readership (Table S5 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>).</p></sec><sec id="s3-3-6"><title>Balance</title><p>The median Brief DISCERN score for treatment-related articles (n=216) was 12.5 (IQR 10.5-14.5), with 84.3% (182/216) falling below the high-quality threshold of 16 (<xref ref-type="table" rid="table2">Table 2</xref>). Interrater reliability for the total Brief DISCERN score was 0.57 (ICC<sub>2,1</sub>). Source information was rarely adequate (17/216, 7.8% scoring &#x2265;3), and only 3.2% (7/216) of articles achieved scores &#x2265;3 for currency. Mechanisms and benefits were addressed in more than 50% of articles (scoring &#x2265;2), whereas risks and quality-of-life impacts were addressed in only 12.5% (27/216, scoring &#x2265;2; <xref ref-type="fig" rid="figure3">Figure 3</xref>).</p><fig position="float" id="figure3"><label>Figure 3.</label><caption><p>Distribution of Brief DISCERN scores across 6 items among 216 treatment-related benign breast disease articles on Chinese hospital WeChat public accounts.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="medinform_v14i1e95994_fig03.png"/></fig></sec><sec id="s3-3-7"><title>Decision Relevance</title><p>Treatment-related articles (n=216) most often described surgery, particularly vacuum-assisted excision (VAE; 57/216, 26.4%) and open surgical excision (OSE; n=45, 20.8%; <xref ref-type="fig" rid="figure4">Figure 4A</xref>). Minimally invasive or ablative techniques (eg, focused ultrasound, microwave, and radiofrequency) were rarely mentioned. TCM therapies appeared in 17.6% (38/216) of articles, most often oral medications (n=17, 7.9%) and acupuncture (n=10, 4.6%; <xref ref-type="fig" rid="figure4">Figure 4B</xref>). Strengths were consistently emphasized for VAE, while limitations were noted mainly for OSE and seldom for VAE or focused ultrasound (Figure S3 in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). The coding reproducibility between the 2 independent reviewers across the entire dataset was 90%.</p><fig position="float" id="figure4"><label>Figure 4.</label><caption><p>Distribution of treatment approaches for benign breast diseases in 216 treatment-related WeChat articles from Chinese public hospitals. (A) Frequency of surgical treatment approaches and (B) frequency of traditional Chinese medicine&#x2013;based treatment approaches. Percentages indicate the proportion of articles that mentioned each treatment method.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="medinform_v14i1e95994_fig04.png"/></fig><p>Clinical indications for surgery were inconsistently reported. Tumor-related factors were most frequently mentioned (n=76, 35.2%), including growth, pathology, size, suspicion of malignancy, and imaging features. Patient-related factors were noted in 25 (11.6%) articles, including pregnancy planning, family history, and age, as well as concerns such as anxiety or cosmetic issues. Detailed distributions are shown in <xref ref-type="fig" rid="figure5">Figure 5</xref>.</p><fig position="float" id="figure5"><label>Figure 5.</label><caption><p>Directed content analysis of surgical recommendations for benign breast nodules in 216 treatment-related WeChat articles from Chinese public hospitals. BI-RADS: Breast Imaging Reporting and Data System; N/A: not available.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="medinform_v14i1e95994_fig05.png"/></fig></sec></sec></sec><sec id="s4" sec-type="discussion"><title>Discussion</title><sec id="s4-1"><title>Principal Findings</title><p>Using a nationwide random sampling approach, we evaluated the accessibility and SDM readiness of hospital WPAs in the context of BBD. This study, the first to explore this issue in China, revealed that WPA articles were generally accessible and had the potential to extend SDM beyond clinical encounters. However, the risk-benefit balance was often insufficient, and decision relevance was limited.</p></sec><sec id="s4-2"><title>Availability for Health Information Dissemination</title><p>SDM in China is still in its early stage, and successful implementation requires culturally appropriate pathways that are customized to fit the local clinical and resource context [<xref ref-type="bibr" rid="ref47">47</xref>]. Telemedicine can facilitate SDM, and the rapid development of internet hospitals in China provides a new avenue by allowing patients to access information before consultations [<xref ref-type="bibr" rid="ref48">48</xref>]. In our study, nearly all sampled hospitals operated WPAs, and approximately one-fourth were certified for internet health care, highlighting their commitment to digital service delivery. As part of the expanding digital health ecosystem, public hospitals are increasingly using online platforms such as WPAs to disseminate medical information and engage with patients [<xref ref-type="bibr" rid="ref49">49</xref>].</p><p>Although decision aids are implemented across diverse areas of health care, multiple studies have consistently highlighted significant barriers to their routine use in clinical practice, including time burden and lack of incentives [<xref ref-type="bibr" rid="ref50">50</xref>]. Effective decision support should convey the right information to the right person in the right format through the right channel and at the appropriate point in the workflow [<xref ref-type="bibr" rid="ref51">51</xref>]. Incorporating the patient portal into SDM delivery is a promising strategy, as it does not rely on clinicians' initiation [<xref ref-type="bibr" rid="ref52">52</xref>-<xref ref-type="bibr" rid="ref54">54</xref>]. This asynchronous SDM also extends decision-making through written or pictorial exchanges across space and time [<xref ref-type="bibr" rid="ref55">55</xref>,<xref ref-type="bibr" rid="ref56">56</xref>]. Developing WeChat-based integration tools could therefore represent a pragmatic strategy for embedding SDM into clinical practice.</p></sec><sec id="s4-3"><title>Accessibility for Patient Engagement</title><p>Despite digital health care platforms providing significant opportunities for SDM, information accessibility remains a critical factor in their effective use. Overall, WPA articles demonstrated acceptable accessibility. Most materials were written at a junior high school level, which was appropriate for the general population, although patients with low health literacy may still face challenges [<xref ref-type="bibr" rid="ref57">57</xref>]. Visual aids contribute to enhancing patients&#x2019; comprehension of health information [<xref ref-type="bibr" rid="ref58">58</xref>-<xref ref-type="bibr" rid="ref60">60</xref>]. In our study, visual aids were generally suitable, and optimizing captions and explanatory labels would further strengthen their educational value. From a user-friendliness perspective, materials were generally understandable, but actionability was limited. Although patients could follow the presented information, there was little explicit guidance on what steps to take or how to engage in the decision-making process [<xref ref-type="bibr" rid="ref40">40</xref>].</p><p>These findings align with evidence that hospital WPAs function as hybrid platforms integrating health service delivery with health information dissemination; however, their role has yet to fully extend from providing information to actively supporting patient decision-making [<xref ref-type="bibr" rid="ref61">61</xref>]. In addition, incorporating multimedia formats&#x2014;such as images, videos, and interactive tools&#x2014;can improve accessibility, clarify patient preferences, and foster more effective participation in decision-making [<xref ref-type="bibr" rid="ref62">62</xref>-<xref ref-type="bibr" rid="ref64">64</xref>]. Emerging digital technologies, including AI-enabled recommendation systems, may further support personalized decision-making and adherence [<xref ref-type="bibr" rid="ref65">65</xref>,<xref ref-type="bibr" rid="ref66">66</xref>].</p></sec><sec id="s4-4"><title>Readiness for SDM</title><p>Effective SDM requires not only the availability of health information but also high-quality and SDM-supportive content [<xref ref-type="bibr" rid="ref67">67</xref>]. In surgical decision-making for benign tumors, it is crucial for physicians and patients to engage in SDM, with patients receiving balanced information to help them make fully informed choices regarding treatment options [<xref ref-type="bibr" rid="ref13">13</xref>].</p><p>However, balance in treatment-related articles on BBD emerged as a major concern, with only 15.7% (34/216) of articles meeting the criteria for high-quality content. Most articles lacked balanced discussions of potential risks or quality-of-life impacts, both of which are essential for informed decision-making [<xref ref-type="bibr" rid="ref34">34</xref>]. This aligns with findings from US hospital websites, showing that such imbalanced information could mislead patients and undermine the SDM process [<xref ref-type="bibr" rid="ref68">68</xref>]. Ensuring that the content patients encounter before consultations is balanced and decision-relevant could make WPAs a practical vehicle for embedding SDM into routine telemedicine.</p><p>Patients often rely on clinicians for information, yet inconsistencies arising from institutional practices or individual physician experience may lead to patient confusion [<xref ref-type="bibr" rid="ref13">13</xref>,<xref ref-type="bibr" rid="ref69">69</xref>]. Our content analysis revealed wide variation in treatment recommendations, which was primarily reflected in 2 aspects: treatment approaches and surgical decision-making. Most articles presented surgical indications as a combination of tumor characteristics (size and growth rate) and patient-specific factors (symptoms, age, family history, and anxiety), generally supported by literature [<xref ref-type="bibr" rid="ref4">4</xref>,<xref ref-type="bibr" rid="ref70">70</xref>-<xref ref-type="bibr" rid="ref72">72</xref>]. However, the specific thresholds for tumor size that necessitate surgical excision and the definition of &#x201C;rapidly growing&#x201D; varied considerably. For instance, the recommended size threshold for fibroadenoma ranged from 2 cm to 3 cm across different regional and international guidelines [<xref ref-type="bibr" rid="ref70">70</xref>,<xref ref-type="bibr" rid="ref73">73</xref>,<xref ref-type="bibr" rid="ref74">74</xref>], resulting in inconsistent information in the WPA articles.</p><p>Considerations related to pregnancy were also presented inconsistently. Due to the visible changes in the mammary glands in response to hormonal stimuli, breast masses detected during pregnancy require timely evaluation and individualized management [<xref ref-type="bibr" rid="ref75">75</xref>,<xref ref-type="bibr" rid="ref76">76</xref>]. Some of the included WPA articles suggested that surgical management of BBD in the context of pregnancy tends to be proactive in China, which may be partly related to the accessibility of specialized medical services and the anxiety of these patients [<xref ref-type="bibr" rid="ref77">77</xref>-<xref ref-type="bibr" rid="ref79">79</xref>]. Women with breast issues often fear the possibility of breast cancer, resulting in unnecessary treatments, patient anxiety, and expenditures [<xref ref-type="bibr" rid="ref3">3</xref>,<xref ref-type="bibr" rid="ref80">80</xref>].</p><p>Treatment approaches discussed in WeChat articles exhibit a hybrid model that integrates Western medicine and TCM in China [<xref ref-type="bibr" rid="ref81">81</xref>]. Although VAE remains the dominant surgical approach, emerging treatments such as focused ultrasound also show promising potential [<xref ref-type="bibr" rid="ref82">82</xref>]. Although TCM has demonstrated benefits in symptom relief and postoperative recovery, its clinical efficacy in treating BBD requires further validation through high-quality studies [<xref ref-type="bibr" rid="ref83">83</xref>,<xref ref-type="bibr" rid="ref84">84</xref>]. In the context of BBD, where management options range from observation to intervention, it is important for patients to receive balanced information regarding the risks and benefits of these approaches. Delivering patient-centered care can enhance patient satisfaction and adherence and reduce overtreatment and its adverse impact on quality of life [<xref ref-type="bibr" rid="ref64">64</xref>,<xref ref-type="bibr" rid="ref85">85</xref>,<xref ref-type="bibr" rid="ref86">86</xref>].</p></sec><sec id="s4-5"><title>Strengths and Limitations</title><sec id="s4-5-1"><title>Strengths</title><p>This study has several strengths. First, it adopted a nationwide sample covering diverse hospital types and levels of public hospitals across China. Second, it is the first systematic evaluation of the quality and SDM-supportive potential of BBD-related health education articles on hospital WPAs. Finally, a comprehensive assessment framework combining validated tools was applied, incorporating both clinician and patient perspectives to strengthen the reliability of the findings.</p></sec><sec id="s4-5-2"><title>Limitations</title><p>First, the analysis was restricted to publicly available WPA articles and official institutional data, which may not fully reflect clinical communication and may contain reporting delays. Clinician and patient perspectives could complement these findings in future work. Second, retrospective web scraping may have missed deleted articles, although survivorship bias is likely limited given the large sample and the rarity of content removal by hospital WPAs. Third, the study assessed information quality rather than clinical accuracy. BBD management recommendations vary across guidelines, and future accuracy audits may be informative where consensus exists. Finally, interrater reliability for PEMAT-P was moderate, attributable to the diverse backgrounds of the 14 patient evaluators and the inherent subjectivity of judging understandability, which varies with individual health literacy and experience. The user-friendliness estimates thus warrant cautious interpretation, and more robust assessment tools and approaches are needed to complement the current evaluation.</p></sec></sec><sec id="s4-6"><title>Conclusions</title><p>Hospital WPAs have the potential to support SDM for BBD, owing to the availability and accessibility of medical information. However, their current SDM readiness is limited, underscoring the need for standardization and quality improvement initiatives to optimize patient-centered decision-making.</p></sec></sec></body><back><ack><p>The authors thank all the participants who contributed to this study by evaluating the quality of the WeChat public account articles. Their participation provided essential insights that strengthened the patient-centered relevance of the findings. Generative AI tools (ChatGPT, version 5; OpenAI) were used under the full supervision of the authors for language polishing and coding assistance only; the authors reviewed all content and bear full responsibility for it.</p></ack><notes><sec><title>Funding</title><p>This study was supported by the Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (grant 2024KFKT004) and the National Natural Science Foundation of China (grant 82427901).</p></sec><sec><title>Data Availability</title><p>Data will be made available upon request.</p></sec></notes><fn-group><fn fn-type="con"><p>Conceptualization: JL, JB, WX, QS</p><p>Data curation: JL, CH, SW, LK, HY, XW, CL, DH, ZZ, WZ, ML, WW</p><p>Data interpretation: JL, CH, SW, LK, HY, XW, CL, DH, ZZ, WZ, ML, WW</p><p>Formal analysis: JL, CH, SW, LK, HY, XW, CL, DH, ZZ, WZ, ML, WW</p><p>Project administration: JB, WX, QS</p><p>Study design: JL, JB, WX, QS</p><p>Supervision: JB, WX, QS</p><p>Writing &#x2013; original draft: JL, CH, WX</p><p>Writing &#x2013; review &#x0026; editing: WX, XW, CL, QS</p></fn><fn fn-type="conflict"><p>The first author (JL), corresponding author (QS), and the funding source are affiliated with the State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University. The funder had no role in the study design, data collection, analysis, interpretation, or the decision to publish. All other authors declare no other conflicts of interest.</p></fn></fn-group><glossary><title>Abbreviations</title><def-list><def-item><term id="abb1">BBD</term><def><p>benign breast disease</p></def></def-item><def-item><term id="abb2">CDC</term><def><p>Centers for Disease Control and Prevention</p></def></def-item><def-item><term id="abb3">ICC<sub>2,1</sub></term><def><p>2-way random-effects intraclass correlation coefficients for single-rating, absolute agreement</p></def></def-item><def-item><term id="abb4">IPDAS</term><def><p>International Patient Decision Aid Standards</p></def></def-item><def-item><term id="abb5">MCH</term><def><p>maternal and child health</p></def></def-item><def-item><term id="abb6">OSE</term><def><p>open surgical excision</p></def></def-item><def-item><term id="abb7">PEMAT-P</term><def><p>Patient Education Materials Assessment Tool for Printable Materials</p></def></def-item><def-item><term id="abb8">QUEST</term><def><p>Quality Evaluation Scoring 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