Collaborative Healthcare: Exploring its Meaning through Content Analysis in Social Media (Preprint)

2021 ◽  
Author(s):  
Tahereh Maghsoudi ◽  
Ana Beatriz Hernández-Lara ◽  
Rosalía Cascón-Pereira
Keyword(s):  
Social Media ◽  
Meaning Making ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Health Indicators ◽  
Healthcare Delivery System ◽  
The Social ◽  
Health Related ◽  
Multi Stakeholder ◽  
Evolution Of Meaning

BACKGROUND The healthcare delivery system is a multi-stakeholder and multi-player system aiming to provide services to promote, restore, and improve health indicators in communities. This highlights the importance of collaboration in the healthcare system. Collaborative healthcare is an emerging concept that has mainly been explored in terms of the practices of healthcare professionals. In an era when social media are becoming key to the evolution of meaning-making, there is a lack of knowledge on how collaborative healthcare meanings are being constructed in the social media discourse OBJECTIVE This paper aims to explore the meanings of collaborative healthcare on social media. METHODS We utilize a dual qualitative approach of visual and textual analysis of posts extracted from Instagram to examine the meanings of collaborative healthcare as perceived by both healthcare providers and laypeople. We use the web scraping technique to extract posts from Instagram. RESULTS wellness centers and health-related professionals were the main group of users who contributed to constructing the meaning of collaborative healthcare (38% and 36%, respectively). The study reveals that Instagram users highlight four main themes within the concept of collaborative healthcare, namely knowledge sharing, events, self-care, and advertising. Interestingly, we found that advertising has the highest frequency (262 posts); the term “collaborative” is used by wellness centers as a hallmark for advertising their services. CONCLUSIONS This study has contributed to unveiling the multiple meanings of collaborative healthcare shared by social media users and accordingly in developing some theoretical reflections and practical implications to improve public health.

How Research Will Adapt to HIPAA: A View from Within the Healthcare Delivery System

2002 ◽  
Vol 28 (4) ◽  
pp. 491-502
Author(s):  
Mary L. Durham
Keyword(s):  
Health Information ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
The United States ◽  
Personal Health Information ◽  
Personal Health ◽  
Business Associations ◽  
Electronic Information ◽  
Healthcare Delivery System ◽  
Institutional Review

While the new Health Insurance Privacy and Accountability Act (HIPAA) research rules governing privacy, confidentiality and personal health information will challenge the research and medical communities, history teaches us that the difficulty of this challenge pales in comparison to the potential harms that such regulations are designed to avoid. Although revised following broad commentary from researchers and healthcare providers around the country, the HIPAA privacy requirements will dramatically change the way healthcare researchers do their jobs in the United States. Given our reluctance to change, we risk overlooking potentially valid reasons why access to personal health information is restricted and regulated. In an environment of electronic information, public concern, genetic information and decline of public trust, regulations are ever-changing. Six categories of HIPAA requirements stand out as transformative: disclosure accounting/tracking, business associations, institutional review board (IRB) changes, minimum necessary requirements, data de-identification, and criminal and civil penalties.

Telegentics and COVID 19 : Through the Pandemic and Beyond (Preprint)

2021 ◽  
Author(s):  
Soumiya Ravi ◽  
Radhika Dhamija ◽  
Aaina Kocchar
Keyword(s):  
Social Media ◽  
Disease Transmission ◽  
Healthcare Delivery ◽  
Professional Societies ◽  
Viable Solution ◽  
Adequate Care ◽  
The Social ◽  
Social Media Platforms ◽  
Federal Laws ◽  
Lasting Change

BACKGROUND The COVID 19 pandemic led to restrictions on the conventional ways of healthcare delivery. Telemedicine provided a viable solution that was in line with the social distancing policies imposed to minimize disease transmission. This demanded physicians adapt to new ways of healthcare delivery. OBJECTIVE We surveyed geneticists across the country to determine their experience and to ascertain if telegenetics will be a lasting change. METHODS A 23 item standardized survey was distributed to various US-based geneticists via email and other social media platforms focusing on their experience of providing care via telemedicine. RESULTS We received 69 responses from physicians across 26 states. Of these, 91% practiced in academia. 70% responded that pediatric genetics takes up more than 50% of their practice. 68% had over 50% of their practice switch to telemedicine. 77% felt they could provide adequate care via telemedicine and 94% of providers would like to continue telemedicine post-pandemic. CONCLUSIONS The future of telemedicine looks promising as the majority of clinicians would like to routinely use telemedicine post-pandemic. Uniform guidelines for use of telemedicine in genetics may need to be proposed by professional societies and supported by federal laws.

scholarly journals Understanding patient health-seeking behaviour to optimise the uptake of cataract surgery in rural Kenya, Zambia and Uganda: findings from a multisite qualitative study

2021 ◽  
Author(s):  
Stevens Bechange ◽  
Emma Jolley ◽  
Patrick Tobi ◽  
Eunice Mailu ◽  
Juliet Sentongo ◽  
...  
Keyword(s):  
Cataract Surgery ◽  
Visual Impairment ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Health Seeking ◽  
Healthcare Delivery System ◽  
Group Discussions ◽  
Patient Health ◽  
Rural Kenya ◽  
Rural Zambia

Abstract Background Cataract is a major cause of visual impairment globally, affecting 15.2 million people who are blind, and another 78.8 million who have moderate or severe visual impairment. This study was designed to explore factors that influence the uptake of surgery offered to patients with operable cataract in a free-of-charge, community-based eye health programme. Methods Focus group discussions and in-depth interviews were conducted with patients and healthcare providers in rural Zambia, Kenya and Uganda during 2018–2019. We identified participants using purposive sampling. Thematic analysis was conducted using a combination of an inductive and deductive team-based approach. Results Participants consisted of 131 healthcare providers and 294 patients. Two-thirds of patients had been operated on for cataract. Two major themes emerged: (1) surgery enablers, including a desire to regain control of their lives, the positive testimonies of others, family support, as well as free surgery, medication and food; and (2) barriers to surgery, including cultural and social factors, as well as the inadequacies of the healthcare delivery system. Conclusions Cultural, social and health system realities impact decisions made by patients about cataract surgery uptake. This study highlights the importance of demand segmentation and improving the quality of services, based on patients’ expectations and needs, as strategies for increasing cataract surgery uptake.

Direct-to-Consumer Genetic Testing: Value and Risk

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Mary A. Majumder ◽  
Christi J. Guerrini ◽  
Amy L. McGuire
Keyword(s):  
Genetic Testing ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Relevant Information ◽  
Third Party ◽  
Annual Review ◽  
Publication Date ◽  
Direct To Consumer ◽  
Number Of Patients ◽  
Health Related

Although the explosive growth of direct-to-consumer (DTC) genetic testing has moderated, a substantial number of patients are choosing to undergo genetic testing outside the purview of their regular healthcare providers. Further, many industry leaders have been expanding reports to cover many more genes, as well as partnering with employers and others to expand access. This review addresses continuing concerns about DTC genetic testing quality, psychosocial impact, integration with medical practice, effects on the healthcare system, and privacy, as well as emerging concerns about third-party interpretation services and non-health-related uses such as investigative genetic genealogy. It concludes with an examination of two possible futures for DTC genetic testing: merger with traditional modes of healthcare delivery or continuation as a parallel system for patient-driven generation of health-relevant information. Each possibility is associated with distinctive questions related to value and risk. Expected final online publication date for the Annual Review of Medicine, Volume 72 is January 27, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Automated extraction of treatment patterns from social media posts: an exploratory analysis in renal cell carcinoma

2019 ◽  
Vol 15 (31) ◽  
pp. 3587-3596
Author(s):  
Sreeram V Ramagopalan ◽  
Bill Malcolm ◽  
Evie Merinopoulou ◽  
Laura McDonald ◽  
Andrew Cox
Keyword(s):  
Renal Cell Carcinoma ◽  
Social Media ◽  
Cell Carcinoma ◽  
Language Processing ◽  
Renal Cell ◽  
Treatment Patterns ◽  
The Social ◽  
Health Related ◽  
Development And Validation ◽  
Further Development

Aim: The use of health-related social media forums by patients is increasing and the size of these forums creates a rich record of patient opinions and experiences, including treatment histories. This study aimed to understand the possibility of extracting treatment patterns in an automated manner for patients with renal cell carcinoma, using natural language processing, rule-based decisions, and machine learning. Patients & methods: Obtained results were compared with those from published observational studies. Results: 42 comparisons across seven therapies, three lines of treatment, and two-time periods were made; 37 of the social media estimates fell within the variation seen across the published studies. Conclusion: This exploratory work shows that estimating treatment patterns from social media is possible and generates results within the variation seen in published studies, although further development and validation of the approach is needed.

Hepatitis C elimination in Pakistan is a distant dream unless government controls the health sector

2020 ◽  
pp. 1-8
Author(s):  
Arshad Altaf ◽  
Safdar Kamal Pasha
Keyword(s):  
Hepatitis C ◽  
Delivery System ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Health Sector ◽  
World Health Organisation ◽  
World Health ◽  
Short Report ◽  
Healthcare Delivery System ◽  
The Government

Abstract The World Health Organisation (WHO) has set an ambitious target to eliminate hepatitis C virus (HCV) by 2030. Pakistan is one of the focused countries because of the high prevalence of HCV. The prices of direct-acting antiviral drugs(DAA)have significantly reduced to between 11-25 dollars for a month’s treatment. To achieve the 2030 elimination target, Pakistan has to provide treatment to one million HCV-infected patients every year, beginning from 2018. This short report highlights a key barrier to achieve this target,i.e. the unsafe practices by regulated and unregulated healthcare delivery system comprising trained and untrained healthcare providers who can continue to churn out new patients with their unsafe healthcare practices and increase the possibility of re-infection in those who have been treated. Only the government has the power and authority to regulate and control the healthcare delivery system. Continuous...  

Critical Social Theory as a Means of Analysis for Caring in Nursing

2007 ◽  
Vol 11 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Jane Sumner
Keyword(s):  
Social Construction ◽  
Social Theory ◽  
Delivery System ◽  
Nursing Practice ◽  
Healthcare Delivery ◽  
Critical Social Theory ◽  
Healthcare Delivery System ◽  
Norms And Values ◽  
The Social ◽  
Critical Social

Critical social theory (CST) is offered as a means to explore the social construction of the patient-nurse relationship within the power constraints of the healthcare delivery system. It is a tool that probes for gaps, silences, and false construction in discourse within this relationship in order to identify excluded or marginalized voices. It provides an opportunity to question historical influences, confront unquestioned norms and values and their relevance in today’s nursing practice. Power is examined as knowledge and as moral, and is explored within the patient-nurse hierarchy. As a method, CST facilitates understanding of caring in nursing.

Social Media News Classification in Healthcare Communication

2019 ◽  
Vol 9 (6) ◽  
pp. 1215-1223 ◽  
Author(s):  
Fiaz Majeed ◽  
Muhammad Waqas Asif ◽  
Muhammad Awais Hassan ◽  
Syed Ali Abbas ◽  
M. Ikramullah Lali
Keyword(s):  
Social Media ◽  
Support Vector ◽  
Learning Approaches ◽  
Healthcare Organizations ◽  
Significant Information ◽  
Health News ◽  
Related Data ◽  
The Social ◽  
Health Related ◽  
Better Than

The trend of news transmission is rapidly shifting from electronic media to social media. Currently, news channels in general, while health news channels specifically send health related news on social media sites. These news are beneficial for the patients, medical professionals and the general public. A lot of health related data is available on the social media that may be used to extract significant information and present several predictions from it to assist physicians, patients and healthcare organizations for decision making. However, A little research is found on health news data using machine learning approaches, thus in this paper, we have proposed a framework for the data collection, modeling, and visualization of the health related patterns. For the analysis, the tweets of 13 news channels are collected from the Twitter. The dataset holds approximately 28k tweets available under 280 hashtags. Furthermore, a comprehensive set of experiments are performed to extract patterns from the data. A comparative analysis is carried among the baseline method and four classification algorithms which include Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (J48). For the evaluation of the results, the standard measures accuracy, precision, recall and f-measure have been used. The results of the study are encouraging and better than the other studies of such kind.

scholarly journals Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

2018 ◽  
Vol 25 (10) ◽  
pp. 1274-1283 ◽  
Author(s):  
Abeed Sarker ◽  
Maksim Belousov ◽  
Jasper Friedrichs ◽  
Kai Hakala ◽  
Svetlana Kiritchenko ◽  
...  
Keyword(s):  
Social Media ◽  
Text Classification ◽  
Training Data ◽  
Support Vector ◽  
Shared Task ◽  
Social Media Mining ◽  
The Social ◽  
Related Text ◽  
Health Related ◽  
Media Mining

AbstractObjectiveWe executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.Materials and MethodsWe organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.ResultsAmong 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.DiscussionAmong individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).ConclusionsData imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).

scholarly journals Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India During COVID-19 Infodemic

2021 ◽  
Vol 6 ◽  
Author(s):  
Palash Aggrawal ◽  
Baani Leen Kaur Jolly ◽  
Amogh Gulati ◽  
Amarjit Sethi ◽  
Ponnurangam Kumaraguru ◽  
...  
Keyword(s):  
Social Media ◽  
Management Strategy ◽  
State Level ◽  
Medical Emergency ◽  
Governance Systems ◽  
The Social ◽  
Health Related ◽  
The Government ◽  
Epidemic Response ◽  
Second Wave

COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people’s health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID-19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger’s causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.

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