Social media: How hospital facebook activity may influence patient satisfaction

Use of Social Media in Orthopaedic Surgery Training and Practice: A Systematic Review

Background

Social media use has grown across healthcare delivery and practice, with dramatic changes occurring in response to the coronavirus (COVID-19) pandemic. The purpose of this study was to conduct a comprehensive systematic review to determine the current landscape of social media use by (1) orthopaedic surgery residencies/fellowship training programs and (2) individual orthopaedic surgeons and the change in use over time.

Methods

We searched 3 electronic databases (PubMed, MEDLINE, and Embase) from their inception to April 2022 for all studies that analyzed the use of social media in orthopaedic surgery. Two reviewers independently determined study eligibility, rated study quality, and extracted data. Methodology was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

Twenty-eight studies were included, of which 11 analyzed social media use by orthopaedic surgery residency and fellowship training programs and 17 examined its use by individual orthopaedic surgeons. Among residency and fellowship programs, Instagram was identified as the most common platform used, with 42% to 88% of programs reporting program-specific Instagram accounts, followed by Twitter/X (20%-52%) and Facebook (10%-38%). Social media was most commonly used by programs for recruitment and information dissemination to prospective residency applicants (82% and 73% of included studies, respectively). After the start of the COVID-19 pandemic, there was a 620% and 177% increase in the number of training programs with Instagram and Twitter/X accounts, respectively. Individual use of social media ranged from 1.7% to 76% (Twitter/X), 10% to 73% (Facebook), 0% to 61% (Instagram), 22% to 61% (LinkedIn), and 6.5% to 56% (YouTube).

Conclusions

Instagram, Twitter/X, and Facebook are the premier platforms that patients, residency applicants, and institutions frequent. With the continued growth of social media use anticipated, it will be critical for institutions and individuals to create and abide by guidelines outlining respectful and professional integration of social media into practice.

Level of Evidence

Level IV.

Emerging technologies adoption in healthcare: A SOHI model

The internet and emerging technologies have increased the utilisation of digital platforms. This study aims to draw on performance expectancy, social influence, and satisfaction to investigate a proposed model which is geared toward social media healthcare information (SOHI) adoption. The proposed model uses a structured online questionnaire, and 300 responses were evaluated using partial least squares and structural equation modelling [PLS-SEM]. From the findings, performance expectancy of social media (PESM) and satisfaction with social media (SATSM) were revealed to be significant predictors of behavioural intention towards social media (BISM). Satisfaction with social media (SATSM) had the greatest impact on BISM, accounting for 63.8 per cent of the variance in users' intentions to utilise SOHI. Similarly, PESM and social influence on social media (SISM) had the most predictive influence on SATSM, accounting for 50.5 per cent of the variance in users' social media satisfaction, which led to SOHI adoption. Unlike others, the social influence on social media (SISM) did not have a significant effect on BISM. BISM and satisfaction with social media (SATSM) were significant predictors of SOHI adoption, accounting for 54.5 per cent of the variance in SOHI adoption. The recommendations in this study would help healthcare professionals change their approach to digital healthcare engagement.

Scandinavian Nurses’ Use of Social Media during the COVID-19 Pandemic—A Berger and Luckman Inspired Analysis of a Qualitative Interview Study

There is a knowledge gap about nurses’ use of social media in relation to and during the COVID-19 pandemic, which demands the upholding of a physical distance to other people, including patients and their relatives. The study aims to explore how nurses in the Scandinavian countries used social media for professional purposes in relation to the first 15 months of the COVID-19 pandemic. Qualitative, semi-structured interviews with 30 nurses in three Scandinavian countries (Denmark, Norway, and Sweden) were conducted. Thematic analyses were made, methodically inspired by Braun and Clarke, and theoretically inspired by Berger and Luckmann’s theory about the construction of social reality. The Standards for Reporting Qualitative Research (SRQR) checklist was used. The results showed that social media was a socialisation tool for establishing new routines in clinical practice. Virtual meeting places supported collective understandings of a specific COVID-19 ‘reality’ and ‘knowledge’ amongst nurses, with the pandemic bringing to the fore the issue of e-professionalism among nurses relating to their clinical practice. However, social media and virtual education were not commonly used in patient contacts. Further, nurses attempted a re-socialisation of the public to proper COVID-19 behaviour through social media. Moreover, blurred boundaries between acting as a private individual and a professional nurse were identified, where ethics of the nursing profession extended to nurses’ private lives.

Atividade das Unidades de Saúde Familiares da Área Metropolitana do Porto no Facebook em ano de COVID-19

Introdução: As redes sociais têm possibilitado, nos últimos anos, novas formas de interação entre pessoas e entidades e a partilha escalável de conteúdos de diversas áreas, embora nem sempre de forma criteriosa. Objetivos: Caracterizar a presença de páginas das Unidades de Saúde Familiar da Área Metropolitana do Porto (Portugal) na plataforma Facebook à data de dezembro de 2020, suas métricas de idade, seguidores, publicações e interações num dado intervalo de tempo e sua distribuição por modelo organizacional (Unidades de Saúde Familiar–A/B) e Agrupamento de Centros de Saúde; verificar a tendência de criação de páginas em 2020 – ano de pandemia por COVID-19 – e aferir as temáticas abordadas pelas 50 publicações dos últimos 60 dias que obtiveram mais interações. Métodos: Estudo exploratório transversal, descritivo e analítico, com verificação individual das páginas das Unidades de Saúde Familiar da Área Metropolitana do Porto a 30 de dezembro de 2020 e obtenção de métricas relativas a um intervalo de 60 dias de atividade por meio da página Fanpage Karma. Foram calculadas frequências, intervalos, médias e medianas e aplicados testes paramétricos e não paramétricos. Resultados: Das 135 Unidades de Saúde Familiar funcionantes (64% Unidades de Saúde Familiar–B), 53% tinham página ativa (61% Unidades de Saúde Familiar–B, p<0,05), variando entre 0 e 81,3% das Unidades de Saúde Familiar em cada Agrupamento de Centros de Saúde, criadas nos últimos dez anos (mediana 4,6 anos, Unidades de Saúde Familiar–A 1,5 versus Unidades de Saúde Familiar–B 5,3, p<0,05), com crescimento de 44% no ano de 2020. O número de seguidores distribui-se heterogeneamente entre diferentes Unidades de Saúde Familiar e Agrupamento de Centros de Saúde, contudo sem diferenças entre modelos de Unidades de Saúde Familiar, não ultrapassando o milhar em 69% das páginas, e com apenas cinco páginas alcançando mais de 2 mil seguidores. Das páginas ativas, 75% (54/72) publicaram em média 0,3 vez por dia nos últimos 60 dias. Não se verificam associações significativas entre o número de seguidores ou entre modelos Unidades de Saúde Familiar–A/B e o tempo da última publicação ou o número de publicações a 60 dias. Durante esse tempo, foram geradas 15.913 interações (média de 18,8 por publicação). Analisadas as 50 publicações com mais interações dos últimos 60 dias, verifica-se o predomínio de temas relacionados com a COVID-19 e com questões organizacionais e burocráticas, efemérides relativas às Unidades de Saúde Familiar e informação/promoção da vacina contra a COVID-19. Discussão: Admite-se haver aplicação reduzida pelas Unidades de Saúde Familiar (embora crescente em ano de pandemia) do potencial comunicacional e colaborativo das redes sociais. Havendo margem de progressão, estas podem constituir uma ferramenta complementar e interativa para a promoção do acesso e a melhoria da qualidade dos serviços, o combate à desinformação, a capacitação para a saúde dos cidadãos e a melhoria de resultados em saúde.

Social Media and Social Support: A Framework for Patient Satisfaction in Healthcare

Social media has been a powerful source of social support for health consumers. In the healthcare sector, social media has thrived, building on various dynamic platforms supporting the connection between social relationships, health, and wellbeing. While prior research has shown that social support exerts a positive impact on health outcomes, there is scant literature examining the implications of social support for patient satisfaction, which suggests that there is a profound gap in the extant literature. The objective of this study is to develop and test a theoretical model for understanding the relationship between different dimensions of social support and patient empowerment. The study further investigates the debated relationship between patient empowerment and patient satisfaction. The measurement model indicated an acceptable fit (χ2 = 260.226; df, 107, χ2/df = 2.432, RMSEA = 0.07, GFI = 0.90, IFI = 0.95, TLI = 0.94, and CFI = 0.95). Findings indicate that emotional support (p < 0.001), information support (p < 0.05), and network support (p < 0.001) positively influence the notion of patient empowerment. In turn, patient empowerment positively influences patient satisfaction (p < 0.001). The proposed framework contributes to the health communication literature by introducing a novel framework for patient satisfaction in the social media context, which provides important inputs for healthcare service providers in developing patient empowerment strategies.

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital's Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.

Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook

Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study's objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals' Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

Facebook Reviews as a Supplemental Tool for Hospital Patient Satisfaction and Its Relationship with Hospital Accreditation in Malaysia

Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman’s correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions’ quality of care in real time.

Overview of Facebook Use by Hospitals in Italy: A Nationwide Survey during the COVID-19 Emergency

Background: Facebook is the most popular social network across the world and also allows users access to health information. Our study presents an overview of the official Facebook profiles of hospitals in Italy (n = 1351) and how much they are used. Methods: All hospitals were surveyed on the number of Facebook posts in May (post-lockdown) and October (second pandemic wave) 2020. The number of followers, the creation date of the official page, and the frequency of publication—that is, the average number of days between two subsequent posts—were determined. Results: In Italy, only 28% (n = 379) of the hospitals had official Facebook pages, of which 20.6% (n = 78) were public hospitals, and 79.4% (n = 301) were private hospitals. Of the hospitals with Facebook pages, 49.1% used them every week, and public hospitals published more often. Conclusions: Despite the differences between regions and types of management, the number of hospitals in Italy that use Facebook as a tool for the public dissemination of health information is still low. Hospitals should adopt an effective communication strategy using social networks to improve the quality of health care.