Mobile Applications Assignment
Please endeavor to use all articles attached to complete the assignment. You may add 2 other resources also.
VERY IMPORTANT: Please follow the RUBRIC ATTACHED
Select a disease or condition and research online support groups and websites or mobile applications you would recommend for patient education. As a guide for assessing the content, refer to the USA.gov site:
In a 6 page, APA formatted Assignment:
1. Choose 3 sites or applications (one must be a support group) and explain what the critical components are that you used to evaluate them.
2. Explain from a provider perspective the benefits of each site and also what, if any, improvements are needed.
3. How do these sites or applications support diverse and hard-to-reach populations?
Readings for the Assignment ATTACHED
McBride and Tietze (2022)
∙ Chapter 16: Telehealth and Mobile Health
∙ Chapter 28: Social Media: Ongoing Evolution
Additional resources:
∙ Davenport, T. & Kalakata, R. (2019) The potential for Artificial Intelligence in Healthcare, Future Healthcare Journal
The potential for AI in Healthcare.pdf ARTICLE ATTACHED
∙ Singh, K., Meyer, S. & Westfall, J. (2019). Consumer-facing data, information, and tools: self-management of health in the digital age. Available at: https://northernkentuckyuniversity.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/consumer-facing-data-information-tools-self/docview/2188845391/se-2?accountid=12817 ARTICLE PDF ATTACHED
∙ Kim, H. (2019). Understanding of how older adults with low vision obtain, process, and understand health information and services. Understanding of how older adults with low vision obtain,, process and understand health information and services.pdf ARTICLE ATTACHED
94 © Royal College of Physicians 2019. All rights reserved.
FUTURE Future Healthcare Journal 2019 Vol 6, No 2: 94–8
DIGITAL TECHNOLOGY The potential for artificial intelligence
in healthcare
Authors: Thomas Davenport A and Ravi Kalakota B
The complexity and rise of data in healthcare means that
artificial intelligence (AI) will increasingly be applied within
the field. Several types of AI are already being employed by
payers and providers of care, and life sciences companies. The
key categories of applications involve diagnosis and treatment
recommendations, patient engagement and adherence, and
administrative activities. Although there are many instances
in which AI can perform healthcare tasks as well or better
than humans, implementation factors will prevent large-scale
automation of healthcare professional jobs for a considerable
period. Ethical issues in the application of AI to healthcare are
also discussed.
KEYWORDS : Artifi cial intelligence , clinical decision support ,
electronic health record systems
Introduction
Artificial intelligence (AI) and related technologies are increasingly
prevalent in business and society, and are beginning to be applied
to healthcare. These technologies have the potential to transform
many aspects of patient care, as well as administrative processes
within provider, payer and pharmaceutical organisations.
There are already a number of research studies suggesting that
AI can perform as well as or better than humans at key healthcare
tasks, such as diagnosing disease. Today, algorithms are already
outperforming radiologists at spotting malignant tumours, and
guiding researchers in how to construct cohorts for costly clinical
trials. However, for a variety of reasons, we believe that it will be
many years before AI replaces humans for broad medical process
domains. In this article, we describe both the potential that AI
offers to automate aspects of care and some of the barriers to
rapid implementation of AI in healthcare.
Types of AI of relevance to healthcare
Artificial intelligence is not one technology, but rather a collection
of them. Most of these technologies have immediate relevance
to the healthcare field, but the specific processes and tasks they
A
B
ST
R
A
C
T
support vary widely. Some particular AI technologies of high
importance to healthcare are defined and described below.
Machine learning – neural networks and deep learning
Machine learning is a statistical technique for fitting models
to data and to ‘learn’ by training models with dat
By Karandeep Singh, Sean R. Meyer, and John M. Westfall
Consumer-Facing Data,
Information, And Tools: Self-
Management Of Health In The
Digital Age
ABSTRACT Consumers have greater access to data, information, and tools
to support the management of their health than ever before. While the
sheer quantity of these resources has increased exponentially over the
past decade, the accuracy of consumer-facing resources is variable, and
the value to the individual consumer remains uncertain. In general, the
quality of these resources has improved, mostly because of improvements
in web and mobile technologies and efforts to restructure health care
delivery to be more patient centered. We describe the major initiatives
that have led to consumers’ increased access to both their own health
data and performance data for health care providers and hospitals. We
explore how search engines and crowdsourced review websites help and
hinder the dissemination of medically accurate information. We highlight
emerging examples of websites and apps that enable consumers to make
medical decisions more in concert with their preferences. We conclude by
describing key limitations of consumer-facing resources and making
recommendations for how they may best be curated and regulated.
T
he doctor-patient relationship his-
torically has been heavily imbal-
anced, with doctors being the sole
party with direct access to patients’
health and medical information
about diagnoses and treatments. Thematuration
of electronic health records (EHRs), patient por-
tals, and websites targeting health care consum-
ers has led to a rapid expansion in the number
and types of resources that consumers can use in
making decisions about their health. At the same
time, health systems are in the midst of a major
cultural shift, recognizing that empowering
consumers with easier access to health data, in-
formation, and tools may have a number of
downstream benefits for health outcomes and
satisfaction.1,2 As these resources become more
accessible, patients can take amore active role in
managing their care. Thus, patients are increas-
ingly finding themselves in the role of consum-
ers, where they have the opportunity (and are
often expected) tomake choices about their care
in partnership with providers.
In the context of health, we use the term data
to refer to facts or observations about one or
more patients, such as the results of a laboratory
test. Information consists of data that have been
aggregated or summarized in some way that
makes them usable by consumers. Examples of
health information include results from search
engines on health topics or information about
the quality and cost of care. Tools are interactive
representations of data or information that pro-
vide a deeper level of ongoing engagement, such
as consumer-facing health apps.
Consumers Have Greater Access To
Their Health Data Than Ev
Understanding of how older adults with low vision obtain,
process, and understand health information and services
Hyung Nam Kim, PhD
North Carolina Agricultural and Technical State University, Industrial and Systems Engineering, Greensboro, North
Carolina, USA
ABSTRACT
Introduction: Twenty-five years after the Americans with Disabilities Act,
there has still been a lack of advancement of accessibility in healthcare
for people with visual impairments, particularly older adults with low vision.
This study aims to advance understanding of how older adults with low
vision obtain, process, and use health information and services, and to seek
opportunities of information technology to support them. Methods: A con-
venience sample of 10 older adults with low vision participated in semi-
structured phone interviews, which were audio-recorded and transcribed
verbatim for analysis. Results: Participants shared various concerns in acces-
sing, understanding, and using health information, care services, and multi-
media technologies. Two main themes and nine subthemes emerged from
the analysis. Discussion: Due to the concerns, older adults with low vision
tended to fail to obtain the full range of all health information and services
to meet their specific needs. Those with low vision still rely on residual
vision such that multimedia-based information which can be useful, but it
should still be designed to ensure its accessibility, usability, and
understandability.
KEYWORDS
Accessibility; aging; assistive
technologies; health
information; low vision
Introduction
In the United States, 21.2 million adults are visually impaired,1 and approximately 3% of individuals
aged 6 years and over have difficulty seeing letters in ordinary newspaper print even if wearing
glasses or contact lenses.2 Low vision is defined as the best-corrected visual acuity equal to or better
than 20/400 and worse than 20/70 in the better seeing eye.3 Each year 75,000 more Americans are
expected to become visually impaired4; many of whom were born with intact vision but lost their
vision due to eye diseases or health conditions.5 As the population ages, it is anticipated that age-
related eye diseases will dramatically increase the number of Americans with visual impairments
over the next 30 years.4 In 2006, one of every six Americans older than 70 years was visually
impaired; his figure doubled among individuals 80 years or older compared with those in the
seventies.6 Low vision is particularly prevalent among older adults7 with two-thirds of individuals
with low vision being older than 65 years.8
Twenty-five years after the Americans with Disabilities Act (ADA), there has still been a lack of
advancement in healthcare for people with visual impairments associated with healthcare facilities,
equipment, health promotion, and disease prevention programs,9,10 leading to poor health outcomes
and decrease
Rubric for Module 4 Assignment
Mobile Applications Assignment
|
Mobile Applications Assignment |
|||||
|
Criteria |
Ratings |
Pts |
|||
|
This criterion is linked to a Learning OutcomeDisease or Condition |
|
10 pts |
|||
|
This criterion is linked to a Learning OutcomeWebsites and applications |
|
20 pts |
|||
|
This criterion is linked to a Learning OutcomeProvider Benefits |
|
20 pts |
|||
|
This criterion is linked to a Learning OutcomeDiversity |
|
20 pts |
|||
|
This criterion is linked to a Learning OutcomeThesis/Topic |
|
6 pts |
|||
|
This criterion is linked to a Learning OutcomeContent/Development |
|
