PLOS digital health最新文献

{"title":"Classifying the features of digital mental health interventions to inform the development of a patient decision aid.","authors":"Gemma Bradley, Lucia Rehackova, Kayleigh Devereaux, Tor Alexander Bruce, Victoria Nunn, Liam Gilfellon, Scott Burrows, Alisdair Cameron, Rose Watson, Katie Rumney, Darren Flynn","doi":"10.1371/journal.pdig.0000752","DOIUrl":"10.1371/journal.pdig.0000752","url":null,"abstract":"<p><p>Digital mental health interventions (DMHIs) are a potential scalable solution to improve access to psychological support and therapies. DMHIs vary in terms of their features such as delivery systems (Websites or Apps) and function (information, monitoring, decision support or therapy) that are sensitive to the needs and preferences of users. A decision aid is warranted to empower people to make an informed preference-based choice of DMHIs. We conducted a review of features of DMHIs to embed within a patient decision aid to support shared decision-making. DMHIs, with evidence of availability in the United Kingdom (UK) at the time of the review, were identified from interactive meetings with a multi-disciplinary steering group, an online survey and interviews with adults with lived experience of using DMHIs in the UK. Eligible DMHIs targeted users age ≥16 years with a mental health condition(s), delivered through a digital system. A previous classification system for DMHIs was extended to eight dimensions (Target population; System; Function; Time; Facilitation; Duration and Intensity; and Research Evidence) to guide data extraction and synthesis of findings. Twenty four DMHIs were included in the review. More than half (n = 13, 54%) targeted people living with low mood, anxiety or depression and were primarily delivered via systems such as Apps or websites (or both). Most DMHIs offered one-way transmission of information (n = 21, 88%). Ten (42%) also had two-way communication (e.g., with a healthcare provider). Eighteen (75%) had a function of therapy, with seven and five DMHIs providing monitoring and decision support functions respectively. Most DMHIs were capable of being self-guided (n = 18,75%). Cost and access were primarily free, with some free via referral from the UK NHS or through corporate subscription for employees (n = 11). Eight (33%) DMHIs had evidence of effectiveness from randomised controlled trials. Six statements were developed to elicit user preferences on features of DMHIs: Target Population; Function; Time and Facilitation; System; Cost and Access; and Research Evidence. Preference elicitation statements have been embedded into a prototype decision aid for DMHIs, which will be subjected to acceptability and usability testing.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000752"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11942417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the influence of depressive symptoms on engagement, adherence, and weight loss in an eHealth intervention.","authors":"Lex Hurley, Nisha G O'Shea, Julianne Power, Christopher Sciamanna, Deborah F Tate","doi":"10.1371/journal.pdig.0000766","DOIUrl":"10.1371/journal.pdig.0000766","url":null,"abstract":"<p><strong>Background: </strong>Digital behavior change interventions (eHealth, mHealth) are known to be capable of promoting clinically significant weight loss among some participants. However, these programs can struggle with declining engagement and adherence over time, which can hamper their effectiveness. This analysis examines the extent that depression symptoms may negatively influence engagement, adherence, and 6 month weight change in an eHealth intervention.</p><p><strong>Methods: </strong>Structural equation modeling is applied to test the effects of baseline depression symptoms on weight change outcomes, mediated through latent constructs of engagement and adherence, respectively. These constructs were highly correlated within this dataset and necessitated two separate models to be tested. Engagement was indicated by 6 month sums of website logins, user-created goals, visiting various webpages, and posts on the online discussion boards. Adherence was indicated by 6 month sums of weeks exercise goals met, days weight logged, and days of complete dietary tracking.</p><p><strong>Results: </strong>Depression symptoms showed no direct association with weight change (p's ≥ 0.6), but were negatively associated with both constructs of engagement and adherence (p's < 0.001), which in turn were negatively associated with weight change in both models (p's < 0.001). It was determined depression symptoms had a positive indirect association with weight change fully mediated through these variables, meaning less weight loss or possible weight gain (p < 0.001).</p><p><strong>Discussion: </strong>This analysis shows that depression symptoms had a significant, undesirable effect on weight loss outcomes within this eHealth intervention, fully mediated through measured participant engagement and adherence. Further research is needed to test these constructs within a longitudinal model to better understand their dynamic interrelationships, and consider means to address depression in future digital interventions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000766"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11936179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an artificial intelligence-enhanced warfarin interaction checker platform.","authors":"Monther Abdolmohsin Alsultan, Mohammed Alabdulmuhsin, Deema AlBunyan","doi":"10.1371/journal.pdig.0000756","DOIUrl":"10.1371/journal.pdig.0000756","url":null,"abstract":"<p><p>Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvantages including narrow therapeutic window, vast drug interactions (and wide variability with foods/herbs), as well as unpredictability of pharmacodynamics and/or kinetics. Complicating factors can present as challenges for the outpatient clinicians trying to strike that balance due to the potential consequences of over or under dose anticoagulation with associated increased risk of bleeding and/or thromboembolic events, respectively. Because warfarin interactions can drastically affect therapeutic outcomes, patient to healthcare provider communication regarding such potential drug-drug or diet-warfarin interactions is crucial for compliance with the medication and achieving successful treatment. Furthermore, language barriers cause low patient satisfaction scores and poor quality/safety health care. In fact, the advancement and improvements in healthcare technology promise artificial intelligence (AI) as one of ideal options to optimize delivery of health care. The goal of this study is to develop Warfa-Check, a bilingual AI-based web app that matches both speakers of Arabic and English. The application helps users recognize potential warfarin-associated drug interactions with a simple user interface that accepts text, picture or voice commands. Warfa-Check, developed with Python and Flask as well as OpenAI's GPT-4 API with natural language processing tools trained to correctly interpret outbound warfarin interactions. Multiple validation methods and beta testing have been done to ensure that the app is data-driven, as well color coded alerts for interaction severity provide clear feedback to end-users. This easy-to-use application helps patients identify drug interactions in both English and Arabic. Warfa-Check represents a valuable avenue for improving the safety of our residents, simplifying medication management in high-risk individuals and streamlining workflow. Future development plans are to develop into other anticoagulants, and integrate with Electronic Health Records (EHRs).</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000756"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating fairness of machine learning prediction of prolonged wait times in Emergency Department with Interpretable eXtreme gradient boosting.","authors":"Hao Wang, Nethra Sambamoorthi, Nathan Hoot, David Bryant, Usha Sambamoorthi","doi":"10.1371/journal.pdig.0000751","DOIUrl":"10.1371/journal.pdig.0000751","url":null,"abstract":"<p><p>It is essential to evaluate performance and assess quality before applying artificial intelligence (AI) and machine learning (ML) models to clinical practice. This study utilized ML to predict patient wait times in the Emergency Department (ED), determine model performance accuracies, and conduct fairness evaluations to further assess ethnic disparities in using ML for wait time prediction among different patient populations in the ED. This retrospective observational study included adult patients (age ≥18 years) in the ED (n=173,856 visits) who were assigned an Emergency Severity Index (ESI) level of 3 at triage. Prolonged wait time was defined as waiting time ≥30 minutes. We employed extreme gradient boosting (XGBoost) for predicting prolonged wait times. Model performance was assessed with accuracy, recall, precision, F1 score, and false negative rate (FNR). To perform the global and local interpretation of feature importance, we utilized Shapley additive explanations (SHAP) to interpret the output from the XGBoost model. Fairness in ML models were evaluated across sensitive attributes (sex, race and ethnicity, and insurance status) at both subgroup and individual levels. We found that nearly half (48.43%, 84,195) of ED patient visits demonstrated prolonged ED wait times. XGBoost model exhibited moderate accuracy performance (AUROC=0.81). When fairness was evaluated with FNRs, unfairness existed across different sensitive attributes (male vs. female, Hispanic vs. Non-Hispanic White, and patients with insurances vs. without insurance). The predicted FNRs were lower among females, Hispanics, and patients without insurance compared to their counterparts. Therefore, XGBoost model demonstrated acceptable performance in predicting prolonged wait times in ED visits. However, disparities arise in predicting patients with different sex, race and ethnicity, and insurance status. To enhance the utility of ML model predictions in clinical practice, conducting performance assessments and fairness evaluations are crucial.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000751"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.","authors":"Zasim Azhar Siddiqui, Maryam Pathan, Sabina Nduaguba, Traci LeMasters, Virginia G Scott, Usha Sambamoorthi, Jay S Patel","doi":"10.1371/journal.pdig.0000765","DOIUrl":"10.1371/journal.pdig.0000765","url":null,"abstract":"<p><strong>Background: </strong>The use of social media platforms in health research is increasing, yet their application in studying rare diseases is limited. Hodgkin's lymphoma (HL) is a rare malignancy with a high incidence in young adults. This study evaluates the feasibility of using social media data to study the disease and treatment characteristics of HL.</p><p><strong>Methods: </strong>We utilized the X (formerly Twitter) API v2 developer portal to download posts (formerly tweets) from January 2010 to October 2022. Annotation guidelines were developed from literature and a manual review of limited posts was performed to identify the class and attributes (characteristics) of HL discussed on X, and create a gold standard dataset. This dataset was subsequently employed to train, test, and validate a Named Entity Recognition (NER) Natural Language Processing (NLP) application.</p><p><strong>Results: </strong>After data preparation, 80,811 posts were collected: 500 for annotation guideline development, 2,000 for NLP application development, and the remaining 78,311 for deploying the application. We identified nine classes related to HL, such as HL classification, etiopathology, stages and progression, and treatment. The treatment class and HL stages and progression were the most frequently discussed, with 20,013 (25.56%) posts mentioning HL's treatments and 17,177 (21.93%) mentioning HL stages and progression. The model exhibited robust performance, achieving 86% accuracy and an 87% F1 score. The etiopathology class demonstrated excellent performance, with 93% accuracy and a 95% F1 score.</p><p><strong>Discussion: </strong>The NLP application displayed high efficacy in extracting and characterizing HL-related information from social media posts, as evidenced by the high F1 score. Nonetheless, the data presented limitations in distinguishing between patients, providers, and caregivers and in establishing the temporal relationships between classes and attributes. Further research is necessary to bridge these gaps.</p><p><strong>Conclusion: </strong>Our study demonstrated potential of using social media as a valuable preliminary research source for understanding the characteristics of rare diseases such as Hodgkin's Lymphoma.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000765"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating knowledge fusion models on detecting adverse drug events in text.","authors":"Philipp Wegner, Holger Fröhlich, Sumit Madan","doi":"10.1371/journal.pdig.0000468","DOIUrl":"10.1371/journal.pdig.0000468","url":null,"abstract":"<p><p>Detecting adverse drug events (ADE) of drugs that are already available on the market is an essential part of the pharmacovigilance work conducted by both medical regulatory bodies and the pharmaceutical industry. Concerns regarding drug safety and economic interests serve as motivating factors for the efforts to identify ADEs. Hereby, social media platforms play an important role as a valuable source of reports on ADEs, particularly through collecting posts discussing adverse events associated with specific drugs. We aim with our study to assess the effectiveness of knowledge fusion approaches in combination with transformer-based NLP models to extract ADE mentions from diverse datasets, for instance, texts from Twitter, websites like askapatient.com, and drug labels. The extraction task is formulated as a named entity recognition (NER) problem. The proposed methodology involves applying fusion learning methods to enhance the performance of transformer-based language models with additional contextual knowledge from ontologies or knowledge graphs. Additionally, the study introduces a multi-modal architecture that combines transformer-based language models with graph attention networks (GAT) to identify ADE spans in textual data. A multi-modality model consisting of the ERNIE model with knowledge on drugs reached an F1-score of 71.84% on CADEC corpus. Additionally, a combination of a graph attention network with BERT resulted in an F1-score of 65.16% on SMM4H corpus. Impressively, the same model achieved an F1-score of 72.50% on the PsyTAR corpus, 79.54% on the ADE corpus, and 94.15% on the TAC corpus. Except for the CADEC corpus, the knowledge fusion models consistently outperformed the baseline model, BERT. Our study demonstrates the significance of context knowledge in improving the performance of knowledge fusion models for detecting ADEs from various types of textual data.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000468"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11918363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What makes clinical machine learning fair? A practical ethics framework.","authors":"Marine Hoche, Olga Mineeva, Gunnar Rätsch, Effy Vayena, Alessandro Blasimme","doi":"10.1371/journal.pdig.0000728","DOIUrl":"10.1371/journal.pdig.0000728","url":null,"abstract":"<p><p>Machine learning (ML) can offer a tremendous contribution to medicine by streamlining decision-making, reducing mistakes, improving clinical accuracy and ensuring better patient outcomes. The prospects of a widespread and rapid integration of machine learning in clinical workflow have attracted considerable attention including due to complex ethical implications-algorithmic bias being among the most frequently discussed ML models. Here we introduce and discuss a practical ethics framework inductively-generated via normative analysis of the practical challenges in developing an actual clinical ML model (see case study). The framework is usable to identify, measure and address bias in clinical machine learning models, thus improving fairness as to both model performance and health outcomes. We detail a proportionate approach to ML bias by defining the demands of fair ML in light of what is ethically justifiable and, at the same time, technically feasible in light of inevitable trade-offs. Our framework enables ethically robust and transparent decision-making both in the design and the context-dependent aspects of ML bias mitigation, thus improving accountability for both developers and clinical users.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000728"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11918422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal information-seeking on pregnancy-induced hypertension and associated factors among pregnant women, in low resource country, A cross-sectional study design.","authors":"Ayana Alebachew Muluneh, Fekade Demeke Bayou, Kegnie Shitu, Ayenew Sisay Gebeyew, Sefefe Birhanu Tizie, Mulugeta Desalegn Kasaye, Adamu Ambachew Shibabaw, Agmasie Damtew Walle","doi":"10.1371/journal.pdig.0000740","DOIUrl":"10.1371/journal.pdig.0000740","url":null,"abstract":"<p><p>Pregnancy-induced hypertension is the most prevalent medical problem associated with pregnancy. It has been reported to affect 6-10% of all pregnant women worldwide. Mothers' failure to seek information related to PIH increases the risk of death from the complication of pregnancy-induced hypertension. This study aimed to assess PIH information-seeking behaviour and its associated factors among pregnant women in rural Sekela Woreda. A community-based cross-sectional study was conducted from May 15 to June 15, 2022. An interviewer-administered structured questionnaire was used to collect the data. The sample size was 635. A cluster sampling technique was used to select the sampled kebeles. The study population included rural pregnant women. This study included pregnant women who were permanent residents of the study area, whereas this study excluded pregnant women who were admitted only for delivery services and temporary residents who visited the study area. The mean age of the participants was 31.8 ± 6.09 years, with minimum and maximum ages of 20 and 45 years, respectively. We conducted descriptive analysis, bivariable analysis, and multivariable analysis to identify determinants of PIH information seeking. The proportion of pregnancy-induced hypertension (PIH) information seeking among pregnant women was 214 (35.4%) out of 604. Pregnant mothers aged 35 years and above (AOR =0.67, 95% CI =0.46, 0.97), family resistance (AOR = 0.45, 95% CI =0.29, 0.69), health care satisfaction (AOR =1.7, 95% CI =1.1, 2.5), and perceived severity of pregnancy-induced hypertension (PIH) (AOR =1.6, 95% CI =1.1, 2.4) were significantly associated with pregnancy-induced hypertension information seeking. According to our findings Information seeking related to pregnancy-induced hypertension is low. Aged mothers, family resistance, mothers' satisfaction with health care services, and perceived severity of PIH were found to be associated with pregnancy-induced hypertension information seeking. Expanding health education programs for pregnant women and providing awareness and training about PIH to participants and their husbands is the most effective way to reduce the prevalence of PIH complications.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000740"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review.","authors":"Hwayeon Danielle Shin, Emily Hamovitch, Evgenia Gatov, Madison MacKinnon, Luma Samawi, Rhonda Boateng, Kevin E Thorpe, Melanie Barwick","doi":"10.1371/journal.pdig.0000418","DOIUrl":"10.1371/journal.pdig.0000418","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework (2017) was established as an evidence-based, theory-informed tool to predict and evaluate the success of implementing health and care technologies. While the NASSS is gaining popularity, its use has not been systematically described. Literature reviews on the applications of popular implementation frameworks, such as the RE-AIM and the CFIR, have enabled their advancement in implementation science. Similarly, we sought to advance the science of implementation and application of theories, models, and frameworks (TMFs) in research by exploring the application of the NASSS in the five years since its inception. We aim to understand the characteristics of studies that used the NASSS, how it was used, and the lessons learned from its application. We conducted a scoping review following the JBI methodology. On December 20, 2022, we searched the following databases: Ovid MEDLINE, EMBASE, PsychINFO, CINAHL, Scopus, Web of Science, and LISTA. We used typologies and frameworks to characterize evidence to address our aim. This review included 57 studies that were qualitative (n=28), mixed/multi-methods (n=13), case studies (n=6), observational (n=3), experimental (n=3), and other designs (e.g., quality improvement) (n=4). The four most common types of digital applications being implemented were telemedicine/virtual care (n=24), personal health devices (n=10), digital interventions such as internet Cognitive Behavioural Therapies (n=10), and knowledge generation applications (n=9). Studies used the NASSS to inform study design (n=9), data collection (n=35), analysis (n=41), data presentation (n=33), and interpretation (n=39). Most studies applied the NASSS retrospectively to implementation (n=33). The remainder applied the NASSS prospectively (n=15) or concurrently (n=8) with implementation. We also collated reported barriers and enablers to implementation. We found the most reported barriers fell within the Organization and Adopter System domains, and the most frequently reported enablers fell within the Value Proposition domain. Eighteen studies highlighted the NASSS as a valuable and practical resource, particularly for unravelling complexities, comprehending implementation context, understanding contextual relevance in implementing health technology, and recognizing its adaptable nature to cater to researchers' requirements. Most studies used the NASSS retrospectively, which may be attributed to the framework's novelty. However, this finding highlights the need for prospective and concurrent application of the NASSS within the implementation process. In addition, almost all included studies reported multiple domains as barriers and enablers to implementation, indicating that implementation is a highly complex process that requires careful preparation to ensure implementation success. Finally, we identified a need for better reporting when using the NASSS in implementa","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000418"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systemic transphobia and ongoing barriers to healthcare for transgender and nonbinary people: A historical analysis of #TransHealthFail.","authors":"Allison J McLaughlin, Saren Nonoyama, Lauren Glupe, Jordon D Bosse","doi":"10.1371/journal.pdig.0000718","DOIUrl":"10.1371/journal.pdig.0000718","url":null,"abstract":"<p><p>Transgender (T+) people report negative healthcare experiences such as being misgendered, pathologizing gender, and gatekeeping care, as well as treatment refusal. Less is known about T+ patients' perceptions of interrelated factors associated with, and consequences of, negative experiences. The purpose of this analysis was to explore T+ patients' negative healthcare experiences through Twitter posts using the hashtag #transhealthfail. Publicly available Tweets published between July 2015 and November 2021 from US-based Twitter accounts were collected via Mozdeh. Tweets were deductively analyzed for content using a list of a-priori codes developed from existing literature. Additional codes were developed as new ideas emerged from the data. When possible, type of care location, providers interacted with, and initial reason for seeking care were extracted. Each Tweet was coded by at least two team members using NVivo12. A total of 1,340 tweets from 652 unique Twitter users were analyzed. Negative experiences were reported across healthcare settings and professional types, with physicians, nurses, and counselors/therapists being named most frequently. Primary antecedents of negative healthcare experiences and barriers to accessing care were related to health insurance issues and providers' lack of knowledge, discomfort, and binary gender beliefs. Negative healthcare interactions led T+ patients to perceive receiving a different standard of care and having unmet needs, which could lead to delaying/avoiding care in the future. As such, these results highlight the potential for direct and indirect harm related to providers' specific actions. Patient strategies to prevent and/or manage negative encounters and care facilitators were also identified. A multi-pronged approach addressing healthcare policy, improving knowledge and attitudes of healthcare providers and ancillary staff, and creating clinical settings that are physically and psychologically safe for T+ patients is critical to improving the healthcare experiences, and ultimately health, of T+ people.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 3","pages":"e0000718"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}