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
∙ 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
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
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 data. Machine
learning is one of the most common forms of AI; in a 2018
Deloitte survey of 1,100 US managers whose organisations
were already pursuing AI, 63% of companies surveyed were
employing machine learning in their businesses. 1 It is a broad
technique at the core of many approaches to AI and there are
many versions of it.
In healthcare, the most common application of traditional
machine learning is precision medicine – predicting what
treatment protocols are likely to succeed on a patient based on
various patient attributes and the treatment context. 2 The great
majority of machine learning and precision medicine applications
require a training dataset for which the outcome variable (eg onset
of disease) is known; this is called supervised learning.
A more complex form of machine learning is the neural
network – a technology that has been available since the 1960s
has been well established in healthcare research for several
decades 3 and has been used for categorisation applications like
determining whether a patient will acquire a particular disease.
It views problems in terms of inputs, outputs and weights of
variables or ‘features’ that associate inputs with outputs. It has
been likened to the way that neurons process signals, but the
analogy to the brain's function is relatively weak.
The most complex forms of machine learning involve deep
learning , or neural network models with many levels of features
or variables that predict outcomes. There may be thousands
of hidden features in such models, which are uncovered by the
faster processing of today's graphics processing units and cloud
architectures. A common application of deep learning in healthcare
is recognition of potentially cancerous lesions in radiology images. 4
Deep learning is increasingly being applied to radiomics, or the
detection of clinically relevant features in imaging data beyond
what can be perceived by the human eye. 5 Both radiomics and deep
learning are most commonly found in oncology-oriented image
analysis. Their combination appears to promise greater accuracy
in diagnosis than the previous generation of automated tools for
image analysis, known as computer-aided detection or CAD.
Deep learning is also increasingly used for speech recognition
and, as such, is a form of natural language processing (NLP),
Authors: A president's distinguished professor of information
technology and management, Babson College, Wellesley, USA ;
B managing director, Deloitte Consulting, New York, USA
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Artificial intelligence in healthcare
described below. Unlike earlier forms of statistical analysis, each
feature in a deep learning model typically has little meaning to
a human observer. As a result, the explanation of the model's
outcomes may be very difficult or impossible to interpret.
Natural language processing
Making sense of human language has been a goal of AI
researchers since the 1950s. This field, NLP, includes applications
such as speech recognition, text analysis, translation and other
goals related to language. There are two basic approaches to it:
statistical and semantic NLP. Statistical NLP is based on machine
learning (deep learning neural networks in particular) and has
contributed to a recent increase in accuracy of recognition. It
requires a large ‘corpus’ or body of language from which to learn.
In healthcare, the dominant applications of NLP involve
the creation, understanding and classification of clinical
documentation and published research. NLP systems can analyse
unstructured clinical notes on patients, prepare reports (eg on
radiology examinations), transcribe patient interactions and
conduct conversational AI.
Rule-based expert systems
Expert systems based on collections of ‘if-then’ rules were the
dominant technology for AI in the 1980s and were widely used
commercially in that and later periods. In healthcare, they were
widely employed for ‘clinical decision support’ purposes over
the last couple of decades 5 and are still in wide use today. Many
electronic health record (EHR) providers furnish a set of rules with
their systems today.
Expert systems require human experts and knowledge engineers
to construct a series of rules in a particular knowledge domain.
They work well up to a point and are easy to understand. However,
when the number of rules is large (usually over several thousand)
and the rules begin to conflict with each other, they tend to break
down. Moreover, if the knowledge domain changes, changing the
rules can be difficult and time-consuming. They are slowly being
replaced in healthcare by more approaches based on data and
machine learning algorithms.
Physical robots are well known by this point, given that more than
200,000 industrial robots are installed each year around the
world. They perform pre-defined tasks like lifting, repositioning,
welding or assembling objects in places like factories and
warehouses, and delivering supplies in hospitals. More recently,
robots have become more collaborative with humans and are
more easily trained by moving them through a desired task.
They are also becoming more intelligent, as other AI capabilities
are being embedded in their ‘brains’ (really their operating
systems). Over time, it seems likely that the same improvements
in intelligence that we've seen in other areas of AI would be
incorporated into physical robots.
Surgical robots, initially approved in the USA in 2000, provide
‘superpowers’ to surgeons, improving their ability to see, create
precise and minimally invasive incisions, stitch wounds and so
forth. 6 Important decisions are still made by human surgeons,
however. Common surgical procedures using robotic surgery include
gynaecologic surgery, prostate surgery and head and neck surgery.
Robotic process automation
This technology performs structured digital tasks for
administrative purposes, ie those involving information systems,
as if they were a human user following a script or rules. Compared
to other forms of AI they are inexpensive, easy to program and
transparent in their actions. Robotic process automation (RPA)
doesn't really involve robots – only computer programs on
servers. It relies on a combination of workflow, business rules and
‘presentation layer’ integration with information systems to act
like a semi-intelligent user of the systems. In healthcare, they are
used for repetitive tasks like prior authorisation, updating patient
records or billing. When combined with other technologies like
image recognition, they can be used to extract data from, for
example, faxed images in order to input it into transactional
We've described these technologies as individual ones, but
increasingly they are being combined and integrated; robots are
getting AI-based ‘brains’, image recognition is being integrated
with RPA. Perhaps in the future these technologies will be so
intermingled that composite solutions will be more likely or
Diagnosis and treatment applications
Diagnosis and treatment of disease has been a focus of AI since
at least the 1970s, when MYCIN was developed at Stanford for
diagnosing blood-borne bacterial infections. 8 This and other early
rule-based systems showed promise for accurately diagnosing and
treating disease, but were not adopted for clinical practice. They
were not substantially better than human diagnosticians, and they
were poorly integrated with clinician workflows and medical record
More recently, IBM's Watson has received considerable attention
in the media for its focus on precision medicine, particularly cancer
diagnosis and treatment. Watson employs a combination of
machine learning and NLP capabilities. However, early enthusiasm
for this application of the technology has faded as customers
realised the difficulty of teaching Watson how to address
particular types of cancer 9 and of integrating Watson into care
processes and systems. 10 Watson is not a single product but a set
of ‘cognitive services’ provided through application programming
interfaces (APIs), including speech and language, vision, and
machine learning-based data-analysis programs. Most observers
feel that the Watson APIs are technically capable, but taking on
cancer treatment was an overly ambitious objective. Watson and
other proprietary programs have also suffered from competition
with free ‘open source’ programs provided by some vendors, such
as Google's TensorFlow.
Implementation issues with AI bedevil many healthcare
organisations. Although rule-based systems incorporated within
EHR systems are widely used, including at the NHS, 11 they lack the
precision of more algorithmic systems based on machine learning.
These rule-based clinical decision support systems are difficult to
maintain as medical knowledge changes and are often not able to
handle the explosion of data and knowledge based on genomic,
proteomic, metabolic and other ‘omic-based’ approaches to care.
This situation is beginning to change, but it is mostly present
in research labs and in tech firms, rather than in clinical practice.
Scarcely a week goes by without a research lab claiming that it
has developed an approach to using AI or big data to diagnose
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Thomas Davenport and Ravi Kalakota
a patient does not follow a course of treatment or take the
prescribed drugs as recommended – is a major problem.
In a survey of more than 300 clinical leaders and healthcare
executives, more than 70% of the respondents reported having
less than 50% of their patients highly engaged and 42% of
respondents said less than 25% of their patients were highly
If deeper involvement by patients results in better health
outcomes, can AI-based capabilities be effective in personalising
and contextualising care? There is growing emphasis on using
machine learning and business rules engines to drive nuanced
interventions along the care continuum. 22 Messaging alerts and
relevant, targeted content that provoke actions at moments that
matter is a promising field in research.
Another growing focus in healthcare is on effectively designing
the ‘choice architecture’ to nudge patient behaviour in a
more anticipatory way based on real-world evidence. Through
information provided by provider EHR systems, biosensors,
watches, smartphones, conversational interfaces and other
instrumentation, software can tailor recommendations by
comparing patient data to other effective treatment pathways
for similar cohorts. The recommendations can be provided to
providers, patients, nurses, call-centre agents or care delivery
There are also a great many administrative applications
in healthcare. The use of AI is somewhat less potentially
revolutionary in this domain as compared to patient care, but
it can provide substantial efficiencies. These are needed in
healthcare because, for example, the average US nurse spends
25% of work time on regulatory and administrative activities. 23
The technology that is most likely to be relevant to this objective
is RPA. It can be used for a variety of applications in healthcare,
including claims processing, clinical documentation, revenue cycle
management and medical records management. 24
Some healthcare organisations have also experimented with
chatbots for patient interaction, mental health and wellness, and
telehealth. These NLP-based applications may be useful for simple
transactions like refilling prescriptions or making appointments.
However, in a survey of 500 US users of the top five chatbots
used in healthcare, patients expressed concern about revealing
confidential information, discussing complex health conditions
and poor usability. 25
Another AI technology with relevance to claims and payment
administration is machine learning, which can be used for
probabilistic matching of data across different databases. Insurers
have a duty to verify whether the millions of claims are correct.
Reliably identifying, analysing and correcting coding issues
and incorrect claims saves all stakeholders – health insurers,
governments and providers alike – a great deal of time, money
and effort. Incorrect claims that slip through the cracks constitute
significant financial potential waiting to be unlocked through data-
matching and claims audits.
Implications for the healthcare workforce
There has been considerable attention to the concern that AI
will lead to automation of jobs and substantial displacement of
the workforce. A Deloitte collaboration with the Oxford Martin
and treat a disease with equal or greater accuracy than human
clinicians. Many of these findings are based on radiological image
analysis, 12 though some involve other types of images such as
retinal scanning 13 or genomic-based precision medicine. 14 Since
these types of findings are based on statistically-based machine
learning models, they are ushering in an era of evidence- and
probability-based medicine, which is generally regarded as positive
but brings with it many challenges in medical ethics and patient/
clinician relationships. 15
Tech firms and startups are also working assiduously on the
same issues. Google, for example, is collaborating with health
delivery networks to build prediction models from big data to warn
clinicians of high-risk conditions, such as sepsis and heart failure. 16
Google, Enlitic and a variety of other startups are developing
AI-derived image interpretation algorithms. Jvion offers a ‘clinical
success machine’ that identifies the patients most at risk as well as
those most likely to respond to treatment protocols. Each of these
could provide decision support to clinicians seeking to find the best
diagnosis and treatment for patients.
There are also several firms that focus specifically on diagnosis
and treatment recommendations for certain cancers based on
their genetic profiles. Since many cancers have a genetic basis,
human clinicians have found it increasingly complex to understand
all genetic variants of cancer and their response to new drugs and
protocols. Firms like Foundation Medicine and Flatiron Health,
both now owned by Roche, specialise in this approach.
Both providers and payers for care are also using ‘population
health’ machine learning models to predict populations at risk
of particular diseases 17 or accidents 18 or to predict hospital
readmission. 19 These models can be effective at prediction,
although they sometimes lack all the relevant data that might add
predictive capability, such as patient socio-economic status.
But whether rules-based or algorithmic in nature, AI-based
diagnosis and treatment recommendations are sometimes
challenging to embed in clinical workflows and EHR systems. Such
integration issues have probably been a greater barrier to broad
implementation of AI than any inability to provide accurate and
effective recommendations; and many AI-based capabilities
for diagnosis and treatment from tech firms are standalone in
nature or address only a single aspect of care. Some EHR vendors
have begun to embed limited AI functions (beyond rule-based
clinical decision support) into their offerings, 20 but these are in the
early stages. Providers will either have to undertake substantial
integration projects themselves or wait until EHR vendors add
more AI capabilities.
Patient engagement and adherence applications
Patient engagement and adherence has long been seen as the
‘last mile’ problem of healthcare – the final barrier between
ineffective and good health outcomes. The more patients
proactively participate in their own well-being and care, the better
the outcomes – utilisation, financial outcomes and member
experience. These factors are increasingly being addressed by big
data and AI.
Providers and hospitals often use their clinical expertise to
develop a plan of care that they know will improve a chronic or
acute patient's health. However, that often doesn't matter if the
patient fails to make the behavioural adjustment necessary, eg
losing weight, scheduling a follow-up visit, filling prescriptions
or complying with a treatment plan. Noncompliance – when
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Artificial intelligence in healthcare
Institute 26 suggested that 35% of UK jobs could be automated
out of existence by AI over the next 10 to 20 years. Other studies
have suggested that while some automation of jobs is possible,
a variety of external factors other than technology could limit
job loss, including the cost of automation technologies, labour
market growth and cost, benefits of automation beyond simple
labour substitution, and regulatory and social acceptance. 27 These
factors might restrict actual job loss to 5% or less.
To our knowledge thus far there have been no jobs eliminated
by AI in health care. The limited incursion of AI into the industry
thus far, and the difficulty of integrating AI into clinical workflows
and EHR systems, have been somewhat responsible for the lack
of job impact. It seems likely that the healthcare jobs most likely
to be automated would be those that involve dealing with digital
information, radiology and pathology for example, rather than
those with direct patient contact. 28
But even in jobs like radiologist and pathologist, the penetration
of AI into these fields is likely to be slow. Even though, as we
have argued, technologies like deep learning are making inroads
into the capability to diagnose and categorise images, there
are several reasons why radiology jobs, for example, will not
disappear soon. 29
First, radiologists do more than read and interpret images.
Like other AI systems, radiology AI systems perform single
tasks. Deep learning models in labs and startups are trained for
specific image recognition tasks (such as nodule detection on
chest computed tomography or hemorrhage on brain magnetic
resonance imaging). However, thousands of such narrow
detection tasks are necessary to fully identify all potential
findings in medical images, and only a few of these can be done
by AI today. Radiologists also consult with other physicians on
diagnosis and treatment, treat diseases (for example providing
local ablative therapies) and perform image-guided medical
interventions such as cancer biopsies and vascular stents
(interventional radiology), define the technical parameters of
imaging examinations to be performed (tailored to the patient's
condition), relate findings from images to other medical records
and test results, discuss procedures and results with patients, and
many other activities.
Second, clinical processes for employing AI-based image work
are a long way from being ready for daily use. Different imaging
technology vendors and deep learning algorithms have different
foci: the probability of a lesion, the probability of cancer, a nodule's
feature or its location. These distinct foci would make it very difficult
to embed deep learning systems into current clinical practice.
Third, deep learning algorithms for image recognition require
‘labelled data’ – millions of images from patients who have
received a definitive diagnosis of cancer, a broken bone or other
pathology. However, there is no aggregated repository of radiology
images, labelled or otherwise.
Finally, substantial changes will be required in medical
regulation and health insurance for automated image analysis to
Similar factors are present for pathology and other digitally-
oriented aspects of medicine. Because of them, we are unlikely to
see substantial change in healthcare employment due to AI over
the next 20 years or so. There is also the possibility that new jobs
will be created to work with and to develop AI technologies. But
static or increasing human employment also mean, of course, that
AI technologies are not likely to substantially reduce the costs of
medical diagnosis and treatment over that timeframe.
Finally, there are also a variety of ethical implications around
the use of AI in healthcare. Healthcare decisions have been
made almost exclusively by humans in the past, and the use
of smart machines to make or assist with them raises issues of
accountability, transparency, permission and privacy.
Perhaps the most difficult issue to address given today's
technologies is transparency. Many AI algorithms – particularly
deep learning algorithms used for image analysis – are virtually
impossible to interpret or explain. If
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