Patients' experiences of radiotherapy: Insights from Twitter

Radiation Reveal: Moving from research engagement to involvement

Here, we report on the process of a highly impactful and successful creative, collaborative, and multi-partner public engagement project, Radiation Reveal. It brought together ten young adults aged 17-25-year-olds with experience of radiotherapy with researchers at Cancer Research UK RadNet City of London across three 2-hour online workshops. Our aims were to 1) initiate discussions between young adults and radiation researchers, and 2) identify what people wish they had known about radiotherapy before or during treatment. These aims were surpassed; other benefits included peer support, participants' continued involvement in subsequent engagement projects, lasting friendships, creation of support groups for others, and creation and national dissemination of top ten tips for medical professionals and social media resources. A key learning was that this project required a dedicated and (com)passionate person with connections to national cancer charities. When designing the project, constant feedback is also needed from charities and young adults with and without radiotherapy experience. Finally, visually capturing discussions and keeping the door open beyond workshops further enhanced impact. Here, we hope to inform and inspire people to help project the patient voice in all we do.

Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review

BACKGROUND

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data.

OBJECTIVE

This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care.

METHODS

We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

RESULTS

After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001).

CONCLUSIONS

Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BACKGROUND

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

OBJECTIVE

This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

METHODS

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

CONCLUSIONS

Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

'Scanxiety': Content analysis of pre-MRI patient experience on Instagram

INTRODUCTION

Distress and anxiety are commonly reported during the Magnetic Resonance Imaging (MRI) experience with prior studies suggesting the pre-MRI period is a time of heightened distress. There is a paucity of literature exploring preprocedural distress and anxiety, in particular qualitative research analysing patient experience. Instagram is rapidly becoming an important social media platform though which to conduct health research. A gradually increasing number of studies have examined social media to gain insight into patient experience within medical radiation science (MRS). This study is considered as the first to explore patient experience of MRI using Instagram as a data source.

METHODS

This study investigated the patient experience during the pre-MRI period by performing a content analysis on open-source Instagram posts. Ethical approval for the study was sought and approved by the Charles Sturt University, Human Research Ethics Committee.

RESULTS

Six themes emerged from the extracted data; Journey to the MRI, Waiting, Anticipating the MRI procedure, Preparing for the MRI procedure, Negative interaction, and Fear of the results.

CONCLUSION

The findings of this study provide novel self-reported and unsolicited insight into the diverse, multifactorial, and often concomitant nature of preprocedural MRI anxiety and distress.

IMPLICATIONS FOR PRACTICE

This study adds to a growing body of literature advocating for a compassionate, holistic, and person-centered approach when caring for patients in MRI that also considers their emotional and psychological wellbeing.

Learning from patient experiences of projection imaging through the use of online feedback platforms

INTRODUCTION & BACKGROUND

Projection radiography remains a well-used diagnostic tool in healthcare, and its use is continually increasing. The volume of feedback collected from patients has grown exponentially but is rarely analysed within the service to meaningfully underpin change. Professions such as nursing currently make use of patient feedback during training yet there is comparatively little use in diagnostic radiography. Research exists into the use of social media during radiotherapy treatment, highlighting how it could be embraced in future research. However, there remains a sparsity of publications discussing the experiences of patients with projection radiography despite its prominence within diagnostic imaging. Online platforms for feedback are available to most industries and readily embraced and used. They are also becoming increasingly available to healthcare providers. This study aimed to assess and analyse the patient experience of projection radiography using the stories of patients via an online platform.

METHODOLOGY

Recognising that humans do not experience healthcare in a binary way, the authors selected a narrative method as the most appropriate qualitative methodology to analyse and understand 181 patient stories relating to projection radiography from the Care Opinion UK website. Each story was read three times to establish codes and themes and to ensure author familiarity with the patient's words & descriptions. This resulted in 30 empirical codes with the most frequently used being split into three major themes for discussion RESULTS & CONCLUSION: The three major themes considered the radiography experience, the encounter with professionals and service provision. Online sources of feedback provide valuable data for health researchers and provide access to insights which might otherwise go unconsidered. Patients instinctively perceive radiological examinations to result in delays to their care and report surprise when discovering examinations are delivered swiftly, though it remains that innovations such as radiographer-led discharge could be better utilised to enhance the patient experience. In addition, it is evident that administrative functions in diagnostic radiology departments are considered poor and from the descriptions given in the study by patients, the administrative side of the service does not meet their needs. Patient stories demonstrate that radiography is not perceived as vital to patient care and is frequently devalued through the notion that health professions are limited to medical doctor and nurse. The work of radiographers is not valueless to the patient evidenced by their desire to thank staff for their work, but its value is poorly understood and could be further enhanced by embracing online feedback as part of continuing professional and service development.

Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia

The sustainability of human existence is in dire danger and this threat applies to our environment, societies, and economies. Smartization of cities and societies has the potential to unite individuals and nations towards sustainability as it requires engaging with our environments, analyzing them, and making sustainable decisions regulated by triple bottom line (TBL). Poor healthcare systems affect individuals, societies, the planet, and economies. This paper proposes a data-driven artificial intelligence (AI) based approach called Musawah to automatically discover healthcare services that can be developed or co-created by various stakeholders using social media analysis. The case study focuses on cancer disease in Saudi Arabia using Twitter data in the Arabic language. Specifically, we discover 17 services using machine learning from Twitter data using the Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-services, namely, Prevention, Treatment, Psychological Support, Socioeconomic Sustainability, and Information Availability. Subsequently, we show the possibility of finding additional services by employing a topical search over the dataset and have discovered 42 additional services. We developed a software tool from scratch for this work that implements a complete machine learning pipeline using a dataset containing over 1.35 million tweets we curated during September–November 2021. Open service and value healthcare systems based on freely available information can revolutionize healthcare in manners similar to the open-source revolution by using information made available by the public, the government, third and fourth sectors, or others, allowing new forms of preventions, cures, treatments, and support structures.

Classifying patient and professional voice in social media health posts

Background

Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).

Results

We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.

Conclusion

The main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01577-9.