Role of Social Media and AI in Transforming Healthcare Communication

Advances in electronic government, digital divide, and regional development book series, Год журнала: 2024, Номер unknown, С. 29 - 51

Опубликована: Авг. 23, 2024

This study explores the transformative impact of social media and AI on healthcare communication, emphasizing their potential in information dissemination, patient engagement, misinformation management. It highlights how platforms provide real-time updates support, while technologies like machine learning natural language processing revolutionize data analysis personalized care. Despite these benefits, integrating tools raises ethical concerns, such as privacy issues, security risks, informed consent complexities. addresses challenges interoperability, infrastructure sufficiency, system scalability, need for comprehensive provider training unbiased algorithms. also examines AI's role curbing balancing considerations free speech. The advocates a collaborative approach to ensure innovative ethically sound improve outcomes, foster more equitable efficient system.

Язык: Английский

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Sentiment Analysis of COVID-19 Survey Data: A Comparison of ChatGPT and Fine-tuned OPT Against Widely Used Sentiment Analysis Tools (Preprint)

Опубликована: Июнь 21, 2023

Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to free-text survey data. Most state-of-the-art applications were developed in domains such as social media, their performance the health context remains relatively unknown. Moreover, existing studies indicate that these often lack accuracy produce inconsistent results. This study aims address of comparative on applied data COVID-19. The objective was automatically predict sentence for 2 independent COVID-19 sets from National Institutes Stanford University. Gold standard labels created a subset each set using panel human raters. We compared 8 both evaluate variability disagreement across tools. In addition, few-shot learning explored by fine-tuning Open Pre-Trained Transformers (OPT; large language model [LLM] with publicly available weights) small annotated zero-shot ChatGPT (an LLM without weights). comparison revealed high evaluated OPT demonstrated superior performance, outperforming all other outperformed OPT, exhibited higher 6% <i>F</i>-measure 4% 7%. demonstrates effectiveness LLMs, particularly approaches, These results have implications saving labor improving efficiency tasks, contributing advancements field automated analysis.

Язык: Английский

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Sources of Information about COVID-19 Vaccines for Children and Its Associations with Parental Motivation to Have Their Children Vaccinated in Taiwan

Vaccines, Год журнала: 2023, Номер 11(8), С. 1337 - 1337

Опубликована: Авг. 7, 2023

Pediatric COVID-19 vaccines have been developed to reduce the risk of contracting and subsequent hospitalization in children. Few studies examined whether different sources information regarding pediatric parents' trust effects on parental motivation their child vaccinated. No study has demographic factors related parents these sources. Understanding vaccines, information, can contribute development strategies for promoting knowledge acceptance vaccination among parents. This used by parents, level sources, that influence this trust, associations such with get vaccinated against COVID-19. In total, 550 (123 men 427 women) completed a questionnaire was collect measure Parental measured using Motors Vaccination Acceptance Scale Parents. Multivariate linear regression analysis performed examine two associations, namely For traditional mass media medical staff healthcare settings were most common vaccines. The rated as trustworthy source information. Obtaining from acquaintances through social obtaining significantly associated Trust provided coworkers vaccinate children Compared fathers, mothers more likely obtain media. Parents higher education settings. trusting obtained coworkers. Health professionals should consider when establishing increase

Язык: Английский

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Trajectories of and spatial variations in HPV vaccine discussions on Weibo, 2018-2023: a deep learning analysis

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 9, 2023

Summary Research in context Evidence before this study We first searched PubMed for articles published until November 2023 with the keywords “(“HPV”) AND (“Vaccine” or “Vaccination”) (“Social Media”)”. identified about 390 studies, most of which were discussions on potentials feasibility social media HPV vaccination advocacy research, manual coding-driven analyses text (eg., tweets) vaccines emerged platforms. When we added keyword “Machine Learning”, only 12 several them using AI-driven approach, such as deep learning, machine and natural language process, to analyze extensive data public perceptions perform monitor platforms, X (Twitter) Reddit. All these studies are from English-language platforms developed countries. No date has monitored developing countries including China. Added value This is deep-learning monitoring expressed Chinese (Weibo our case), revealing key temporal geographic variations. found a sustained high level positive attitude towards exposure norms facilitating among Weibo users, lower national prevalence negative attitude, perceived barriers accepting vaccination, misinformation indicating achievement relevant health communication. High practical was associated relatively insufficient vaccine accessibility China, suggesting systems should prioritize addressing issues supply. Lower perception male higher hesitancy 2-valent vaccine, provincial-level spatial cluster indicate that tailored strategies need be formed targeting specific population, areas, type. Our practice shows realizing surveillance potential listening context. Leveraging recent advances approach could cost-effective supplement existing techniques. Implications all available evidence highlights learning-driven convenient effective identifying emerging trends inform interventions. As techniques, it particularly helpful timely communication resource allocation at multiple levels. Key stakeholders officials maintain focus education highlighting risks consequences infections, benefits safety types vaccines; aim resolve accessibility. A proposed research area further development learning models analyzing Background rate low Understanding multidimensional impetuses by individuals essential. assess perceptions, barriers, facilitators platform Weibo. Methods collected posts regarding between 2018 2023. annotated 6,600 manually according behavior change theories, subsequently fine-tuned annotate collected. Based results models, conducted attitudes its determinants. Findings Totally 1,972,495 vaccines. Deep reached predictive accuracy 0.78 0.96 classifying posts. During 2023, 1,314,510 (66.6%) classified attitudes. And 224,130 (11.4%) misinformation, 328,442 (16.7%) vaccines, 580,590 (29.4%) vaccination. The increased 15.8% March 79.1% mid-2023 (p < 0.001), declined 36.6% mid-2018 10.7% (P .001). Central regions exhibited norms, whereas Shanghai, Beijing megacities northeastern showed misinformation. Positive significantly (65.7%), than 4-valent 9-valent (79.6% 74.1%). Interpretation Social represents promising can enable strategies.

Язык: Английский

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