Research paper
3 MOST IMPORTANT CHALLENGES TO
BE CONSIDERED WHILE USING
SOCIAL MEDIA DATA FOR MEDICAL
& HEALTHCARE RESEARCH
TAGSTags: Social Media for Exchanging Health Information, Data Analysis, Data Collection, Data
Analyst, Data Collection, Healthcare Social Networking, Predictive Analytics
SERVICESResearch Planning | Data Collection | Semantic Annotation | Business Analytics |
Bio Statistics | Econometrics
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In Brief
Studies indicated that patients primarily used Twitter (59.9%), particularly for increasing knowledge and exchanging advice and
Facebook (52.3%), particularly for social support and trading advice.
Professionals mainly used LinkedIn (70.7%) and Twitter (51.2%), for communication with their colleagues and marketing details.
As patients continue to turn to online means for health care information to monitor their decisions, it is becoming gradually
significant for radiologists to involve with patients online via social media daises.
There are several means that physicians can use social media to deliver patients with valued information and develop the
complete patient experience.
By elevating discoverability, curating radiology, involving with patient communities, and generating mineable social media content,
radiologists can develop as thought leaders in this new form of patient-centered announcement and information discussion.
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Introduction
Social media channels are being used by patients for switching health
information, involvements with their chronic condition, and pursue
information from other people or establishments, for example
NHS Choices (www.nhs.uk)
Joining the Conversation describes Big White Wall (www.bigwhitewall.com)
Mental health SNS that facilitates peer-professional interactions
National Institute for Health & Care Excellence (NICE)
UK government (https://www.gov.uk/government/publications)
NHS England Publication stores (www.healthline.com)
Contd..
.
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Given that there is a high usage of social media for exchanging health information, it offers
new opportunities between and amongst healthcare professionals, patients and public.
Data Analysis on Healthcare service information ‘empowers’ patients and the general
public to make responsible decisions.
Today, publicly available real-world social media blogs, microblogs [such as twitter] internet forums, content communities [such
as YouTube, Flickr], social networking sites [such as Facebook and LinkedIn), data can now be analyzed with relative ease,
bypassing many logical challenges associated with traditional approaches of Data Collection (e.g. electronic medical records, or
conventional randomized clinical trials).
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THREE ESSENTIAL
CHALLENGES OF
RESEARCHERS DESPITE
THE HIGH FLOW OF DATA
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1. Which is the Most Common
Disease Category Shared in
Public Posts?
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2. What Kind of Information can be
Extracted from Social Networking Sites?
Social media channels provide efficient, permeating and user-friendly daises that can inspire involvement, engagement and
achievement required from both those who collect and deliver care to make health promotion interventions effective.
Participating health methods are progressively drawing consideration among the scientific community and could be used for
health promotion programs through social media such as reviews, forum discussion, blogs, micro blogs and social networks.
The time restraints of today’s medical practice pooled with the demand for chronic conditions make any additional request of
extra time used by Health Care Experts a challenge.
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3. What are Ethical Guidelines to be Adopted for
Extracting and Analyzing Health Behaviour?
According to the Fortune Report (March 18, 2019), the parliamentary group called on the social media platforms to give
researchers all the Data Analyst they need to analyses the problem and the following the polluter pays principle – to pay a
0.5% levy on their profit to fund a “Social Media Health Alliance”.
Study designs using social media-derived data should be transparent and readily available to the public.
Researchers should respect the context in which content is sent.
All data that can be used to recognize tweet authors, including geolocations, should be available.
No information collected from social media should be used to procure more data about tweet authors from other sources.
Study designs that necessitate Data Collection from a few personalities rather than combined analysis entail Institutional
Review Board (IRB) approval.
Researchers should adhere to a user’s attempt to control his or her data by respecting privacy settings.
Contd.
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Calculating the
predictability of medical
conditions from social
media posts
Contd.
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Social media applied in a healthcare context represent a tempting
opportunity to improve the patients’ well-being, promoting patient
care and education.
The major Healthcare Social Networking (HSN) platforms require
the massive action of a medical professional who replies to
patient’s queries, also acting as moderators on specific topics
when it is needed.
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Benefits of Healthcare Social Networks
Promoting networking and information exchange, enabling self-education among patients about particular diseases.
Sharing patients’ experiences that can be helpful for other ones
Supporting the treatment process
Reducing the patient’s stress when he/she is waiting for a diagnosis or when he/she discovers to be affected by a
particular disease
Promoting information gathering and prevention campaign regarding specific diseases
Optimizing the work of the clinical personnel who interact with patients skilled in their diseases
Promoting knowledge management
Promoting research and monitoring activities
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Conclusion
Social media information has the benefit that it often has a built-in
communication network back to patients.
For example, Facebook now allows users to flag posts within their
system that they think may suggest suicidal ideation. Facebook then
anonymously delivers resources for entities at risk. This study has
several boundaries.
Constellations of predictive words often do not representcausal
mechanisms, and the findings are cor-relational.
However, in enlightening, what people think, feel, and do social media
capture sensitive, cognitive, behavioural and environmental indications
that have substantial Predictive Analytics that to be validates and are
otherwise rationally elusive to researchers and clinicians.
Contd.
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People’s character, mental state, and health behaviours are all
reflected in their social media and all have a tremendous impact
on health.
This blog is to show that language on social media can predict
diagnoses within people’s health record, revealing new
prospects to personalize care and understand how patients’
ordinary daily lives relate to their health.
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