DEVELOPING CONSUMER ENGAGEMENT STRATEGIES AND IMPLEMENTING TREND ANALYSIS TO ESTABLISH RISK PROFILES FOR EHEALTHBRIEFCASE'S (EHB) APPLICATION
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Developing Consumer Engagement Strategies and Implementing Trend
Analysis to Establish Risk Profiles for eHealthBriefcase’s (eHB) Application
Alina Panjwani1, Brandon Lade1, Emmanuel Adeyemi 1, Monica Vickery1, Soo Shin2 , Sweta Sneha3
1
Master of Science Healthcare Management and Informatics, Kennesaw State University. Kennesaw, Georgia, U.S.A.
apanjwa9@students.kennesaw.edu, blade@students.kennesaw.edu, eadeyem2@students.kennesaw.edu, mvicke10@students.kennesaw.edu
2
Assistant Professor of Information systems, Department of Information Systems, Kennesaw State University, Kennesaw, Georgia, U.S.A.
sshin12@kennesaw.edu
3
Executive Director & Professor, Healthcare Management & Informatics, Kennesaw State University, Kennesaw, GA, USA.
ssneha@kennesaw.edu
Abstract
eHealth applications have been receiving a lot of attention from patients, providers, insurance companies
and researchers. eHealth applications not only help patients take ownership of their health but also help
them stay active and achieve better results. These applications can provide various major benefits, such as
better decision making, chronic disease management and improved patient/provider communication. Apart
from these advantages, there are multiple challenges that the eHealth applications currently face – an
important one being consumer engagement. In this research, we address the gaps with eHealth applications
and their engagement with consumers. First, we identify what target populations would benefit most from
this eHealth application and how to engage those consumers. Second, we develop a framework for a trend
analysis of a chronic condition focused on diabetes as well as a health score feature. Third, we highlight the
benefits of the vaccine passport feature and how it will play a role in the community moving forward in the
COVID-19 pandemic. Lastly, we also propose a conceptual model of the optimal eHealth Briefcase
application based on our target population and consumer engagement research and the proposed framework.
Keywords: eHealth application, consumer engagement, trend analysis, risk profile
1. Introduction
Currently, in healthcare, a single patient with a chronic illness can have numerous providers
depending on their health conditions and needs. There are numerous sources of data from different
inputs such as providers, wearables, and Internet-of-Things (IOT) devices. What does it all mean
to you as a consumer and where does all that data go? This potentially results in an abundance of
compound medical information and data not being utilized for the best benefit of the patient.
eHealth Briefcase’s (eHB) application will give consumers the ability to advocate and take control
of their health with their own records and data. It is a consumer-centric application that will allow
users to seamlessly integrate and consolidate their health history, fitness, and medications within
the application to work to better manage their health (eHealth Briefcase, 2021). The platform will
improve consumer health outcomes and reduce Medicare and Medicaid costs.
1
One focus will be on consumer acquisition and targeting a specific population for higher customer
engagement. We chose to focus on caregivers as our target population since these individuals are
working professionals who can best benefit from the technology and can easily afford to pay for
the application. This population will be highly satisfied by the benefits of the application as it will
allow them to integrate patient health information from multiple sources and make caregiving an
easy process. Consumer acquisition strategy will be focusing on the marketing message, and
criteria of digital engagement and sign-ups. Interoperability in eHealth system is concerning as it
affects consumers and clinicians. The ability to compile and store patient information and data can
interfere with usability and efficiency. Since there is not a standard format for how data is saved
or made available from providers, consumers may find it difficult to have all information consistent
within their health application (Gold, M. et al., 2015). The application will need to use and simplify
all forms of data and documents from various systems and providers into uniform and usable
formats. This will improve efficiency for both consumers as well as providers. By integrating data
from various sources, eBriefcase allows the caregiver to easily manage their health as well the
health of the people they are taking care of. Our marketing message and digital engagement
strategy will be strengthened by various important features of the application.
Consumer engagement is of great concern when looking at the caregiver population as they make
up a large population who supports those with chronic conditions. Diabetes is a disease that affects
over 10.9 million adults at the age of 65 and older and is expected to increase over the next 20 plus
years (Chentli, F. et al., 2015). Diabetes is a serious chronic disease that can be managed by taking
proper steps and lifestyle modifications. The eHB application allows easy transfer of medical
records between providers while being digitally organized within the application dashboard
(eHealth Briefcase, 2021). There is a need to aid caregivers in the understanding and utilization of
this application.
A second focus will be on the needs and concerns of diabetes to develop a risk profile for the
chronic condition. By using available data, we can identify trends and help analyze the risk for
diabetic users. This will allow the users to better manage their health and give providers better
insight into their health records (Mueller-Peltzer, et al. 2020). Based on the National Diabetes
Statistics Report, currently, there are 34.2 million Americans who have diabetes (10.5% of the
population). Of the 34.2 million with diabetes, 26.8 million were diagnosed, and 7.3 million
2
(21.4%) were undiagnosed. Furthermore, 88 million people aged 18 years or older have
prediabetes (34.5% of the adult U.S population) (National Diabetes Statistics Report, 2020). Data
will be gathered to build a risk profile and then subsequently create a trend analysis feature for
those who are prediabetic or diabetic within the application.
As a new eHealth application coming into the digital health market eHB will face challenges on
how to market their application to stand out in the booming eHealth domain. eHB faces another
challenge of finding their target consumers and how to engage them successfully. Ultimately their
greatest challenge will be to provide features that separate themselves from other applications in
the market. As stated above, our research aims to achieve the following goals to address these
challenges. First, we strive to develop consumer marketing and engagement strategies for
caregivers based on prior literature review. Second, we target to generate a health scoring system
and create a dashboard for both consumers and management staffs. Last, we plan to implement
trend analysis on factual data and develop risk profile for diabetes population.
2. Methodology Design Structure
The organizational structure of the current study begins with identifying existing problems that
eHB is facing. Then, we explore existing marketing strategies, the tentative goal of the trend
analysis, and develop a health score. A literature review is conducted focusing on the challenges
of consumer engagement, needs and concerns of diabetic patients, and socio-economic factors.
The study design and development is completed by building eHB a visualizing dashboard to
incorporate a health scoring system as well as a trend analysis. Lastly, an evaluation of the
dashboard system is to be done to ensure success. Our research constructs the following design
and development structure.
Identifying Objective of Literature Design /
Evaluation
Problems the Research Review Development
Building
Consumer
Marketing Dashboard to
Understanding engagement
Strategy incorporate Evaluating
the current Diabetes
Trend Analysis health scoring the system
status of eHB Socio-economic
Health Scores and trend
factor
analysis
3
3. Literature Review and Conceptual Model
3.1 Patient Engagement
Health applications have the opportunity to improve health management for multiple medical
necessities from any smart device (Varshney, U. et al., 2014). However, the concern for consumer
engagement becomes a topic for discussion. Data shows that most consumers give less than 30
seconds to learn how to use a new app before either abandoning it or giving up (Liew, M. S. et al.,
2019). Learning anything new in that amount of time can be difficult. This section is going to focus
on the strategies to improve consumer engagement and retaining users.
To begin, the understanding of consumer engagement in healthcare can be difficult. The definition
varies throughout, yet the most commonalities focus on improving patients' needs and preferences
at the individual, organizational, and policy levels (Gold, M. et al., 2015). That results in patients
becoming better informed and involved in making their own health decisions and processes. Health
applications have the opportunity to give that involvement to the patient so they are able to stay
well informed.
The widespread adoption of health applications gives consumers the high expectation of having
an application that is both simple and intuitive in the palm of their hand (Dameff C. et al., 2019).
This cannot always be the case with every health application. An assessment done on a basic
mHealth app using the Nielsen model indicated that learnability, satisfaction, and efficiency were
the top attributes of concern when using it (Liew, M. S. et al., 2019). The next section will touch
on strategies to improve each of those attributes and consumer engagement.
4Figure 1. Conceptual Model
3.1.1 Learnability
The newness of technology is a common challenge when it comes to willingness to use health
applications. As stated before, consumers do not put forth the adequate time to truly learn a new
application. This can lead to frustration and negative opinions of all health applications, current or
future. A solution could be to add educational prompts such as a demo video or a short walk-
through of the application to show all it has to offer (Consumer Health IT). A lack of understanding
of medical terms or results can also lose a consumer’s interest. A suggested strategy would be to
5ask how the consumer would like to obtain the information, for example, basic, intermediate, or
advanced explanations (Gold, M. et al., 2015). Consumer understanding is vital when thriving for
adherence in using the application.
3.1.2 Satisfaction
A study done at the University of California at San Diego Health in 2018 concluded that
participants using a health application recognized improvements in either their own health
understanding, provider relationship or sharing capabilities by 90%, still, only 48% saw
improvements in all three aspects (Dameff C. et al., 2019). This study showed that consumers
could see multiple beneficial outcomes when utilizing their health applications. Satisfaction has
been known as a key attribute that explains sustainability, commitment, loyalty, and consistent use
of a certain IT artifact.
3.1.3Involvement
The role of providers in engaging with consumers is very important. Providers can utilize their
role and relationship by motivating the patient to use the application rather than unused. Integrating
tools within the application to work well for both provider workflow and patient needs can further
push higher consumer engagement and retention (Gold, M. et al., 2015). Providers have better
knowledge and understanding of the targeted consumer base motivations, which can lead to higher
engagement.
Some developers have established partnerships with large companies to help to improve their
application by using additional technology products. For example, Amazon has partnered with
developers to create software using virtual assistant products (Alexa) to collect data and improve
patient engagement and as well as education (Dameff C. et al., 2019). Implemented correctly, this
can be extremely beneficial for all parties involved.
Strategies to help improve willingness for consumer engagement can be challenging but can be
done successfully. There will always be barriers with continuous technological advances, the goal
is to find solutions to current or future barriers to improving engagement and patient outcomes
(Dameff C. et al., 2019). It is also essential to recognize and appeal to consumer motivations to
decrease the number of barriers using health applications (Liew, M. S. et al., 2019). The strategies
6included above can improve consumer satisfaction, learnability, and efficiency for present and
future barriers.
3.1.4 Digital Health Communication
Late research from the World Health Organization (WHO. et al., 2009/2012) implies that much of
the “global burden of disease” is attributable to preventive behavioral lifestyle factors such as diet,
exercise, blood pressure, and blood glucose management (Neuhauser, L. et al., 2013). Digital
health communications have typically focused on generic intervention leading to ineffective
outcomes. Researchers and clinicians have proposed that digital health communication be “aligned
with the user’s literacy, language, culture, and social contexts and be accessible, understandable,
interactive and motivating” (Neuhauser, L. et al., 2013). eHB’s diabetes trend analysis feature will
be an innovative path for digital health communication utilizing AI to provide users personalized
means to understand and manage their health.
3.2 Socio-economic Factors
Chronic diseases constitute a significant challenge that is currently being faced by the healthcare
system. As reported by CDC, chronic diseases are the leading cause of death and disability and a
leading driver of the nation’s $3.8 trillion annual health care costs (CDC. et al., 2021). The most
prevalent chronic conditions are cancer, chronic respiratory disease, cardiovascular disease, and
diabetes (Reiners F. et al., 2019). Most of these are related to patients’ behavioral factors, such as
diet, lack of exercise, nicotine, and alcohol consumption. The higher prevalence of chronic
conditions among patients is causing a higher burden on healthcare services; therefore, patient-
centered care needs to be enforced where patients take responsibility for their health. This concept
is known as self-management, and it is currently widely adopted to improve health outcomes and
quality of life among chronically ill patients (Reiners F. et al., 2019) (Gheorghe A. et al., 2018)
(Settumba S.N. et al., 2015). Patients have started using a range of digital health interventions
(DHIs), such as telehealth, patient portals, personal health records (PHRs) and mobile health
applications. These DHIs not only help them manage chronic illness at home and support
independent living and self-care but also help them stay connected to health and care providers
(O’Connor S. et al., 2016) (Georgsson M. et al., 2016). DHIs may address many of the problems
patients experience with today’s health systems, such as poor access, uncoordinated care and
increasingly costly healthcare (O’Connor S. et al., 2016) (Reti, S. R. et al., 2010).
7Even with the belief that health care will be improved with these tools, there remain concerns that
disadvantaged populations, such as those of low socio-economic status, minorities, and the elderly,
may obtain fewer benefits (Senecal C. et al., 2018). As the use of technology in health care is
increasing, it is essential to study the population group who are not aware of these technologies or
who are not able to use eHealth applications and what barriers these patients face (Reiners F. et
al., 2019).
3.2.1 Age
Age is an important factor as most patients with one or more chronic conditions belong to older
age groups. According to a study conducted by Kaiser Permanente Northern California Region’s
Community Benefit Program, adults aged 70-74 and 75-79 were significantly less likely than 65-
69 years to be registered to use the patient portal, and among those registered, to have used the
portal to send messages, view lab test results, or order prescription refills (Gordon N. P. et al.,
2016). This could be because older people are afraid of losing personal contact with their physician
when using eHealth (Terschüren C. et al., 2012). Another research shows that older people with
chronic diseases who are living alone are less inclined to use eHealth, because family members
often help with difficulties experienced while using eHealth (Reiners F. et al., 2019). In contrast,
a study conducted by Nadine Bol showed that only users of fitness and reproductive health apps
were on average younger, while users of self-care and vitals apps were usually older (Bol, N. et
al., 2018).
3.2.2 Race and Ethnicity
It has been found that racial and ethnic minority groups are more likely to have multiple major
chronic diseases as compared to Whites (Millett G. A. et al., 2020). These inequities can also be
seen in the use of DHIs by the minority group. According to a study, online patient portal usage
was markedly decreased in Blacks and Hispanics who mostly belong to low socio-economic status
(Senecal C. et al., 2018) (Graetz I. et al., 2016). This could also be related to factors, such as
education, digital literacy, and other factors are possible contributors as well (Senecal C. et al.,
2018) (Neter E. et al., 2012). A cross-sectional analysis conducted to analyze the use of digital
health intervention based on socio-economic status as well as age, gender, and race, showed that
among Caucasians, the frequency of DHI log-ins showed a small nonsignificant increase as socio-
economic status increased. In comparison, Hispanic participants saw a>50% increase in log-in
8frequency with a doubling of income and Black populations displayed a>40% increase (Senecal
C. et al., 2018).
3.2.3 Socio Economic Status (SES) and Patient Residence
People living in rural areas are most in need of electronic healthcare to overcome scarcity in health
care provisions; however, they are the ones who have less access to these opportunities (Reiners
F. et al., 2019). People living in rural regions have a lower socio-economic status and therefore
have less access to DHI (Reiners F. et al., 2019). Based on a retrospective secondary cross-
sectional analysis, it was found that socio-economic status, as derived from the zip code of
residential addresses, significantly affected digital health usage in a large cohort (Senecal C. et al.,
2018). Some studies show that people with higher income tend to have more interest in eHealth
compared to people with lower income (Reiners F. et al., 2019). It is indicated that lower income
is associated with limited availability and access to Internet healthcare resources (Reiners F. et al.,
2019).
A few more factors that impact consumer engagement include as follows in the table 1.
Table 1. Factors impacting consumer engagement
gender income education vocational status
marital status health insurance size of habitat living conditions
presence of chronic lack of access to
poor digital literacy disability
illness equipment
concern about lack of access to busy lifestyle with lack of support for
security and privacy internet other priorities the application
lack of advice from
unawareness of the
trusted sources such
benefits of the cost of a DHI
as families and
application
friends.
Multiple researchers demonstrate that DHI is least used by people, such as older age group,
persons with chronic diseases, and those living in rural areas; however, they are the ones who
appear to need it most (Reiners F. et al., 2019). In order to increase the use of DHI, it is highly
important to take action to eliminate these inequities.
93.3 Trend Analytics
Precision medicine uses advanced biomedical tools, including genetic and molecular testing and
big-data analytics, to help clinicians better predict which treatment and prevention strategies will
work best for which patients. It aspires to replace the common one-size-fits-all approach with one
that tailors care to each patient’s unique biology and life circumstances. Mobile applications like
eHB can play an especially important, but assistive, role in decision making by supporting the
needed information anytime anywhere to anyone authorized (Varshney, 2014). This could include
mobile access to expert systems and evidence-based medicine tools. Big-data analytics in
healthcare can utilize health data for insights, decision making, planning, early prediction and
detection of diseases using different statistical, predictive, and quantitative models (Sarwar, M. A.
et al., 2018). One type of data analytics that can be used to predict diseases and improve decision-
making is machine learning. Simply, it is a type of artificial intelligence (AI) where computers are
programmed to learn information without human intervention. Our research employes a linear
regression modeling approach and a supervised machine learning algorithm where the predicted
output is continuous and has a constant slope. It’s used to predict values within a continuous range
rather than trying to classify them into categories. This method can help with forecasting and
finding out the cause-and-effect relationship between the variables. Providing these analytics to
eHealth application users will give them insights into their health and aid them in taking ownership
in their overall wellness.
3.3.1 Trend Analysis Medical Condition
Within the eHB application, we want to develop a diabetes trend analysis feature using linear
regression. Our research aims to determine if we can identify trends for diabetes that would be
clinically significant to the users and their clinicians and lead to early detection of diabetes as well
as improved management for current diabetics. This patient-centered feature would support users
to make informed health decisions that would improve health outcomes while potentially reducing
health care costs. Users would greatly benefit from tangible proof on how their lifestyle changes
are affecting their diabetes trends. The glucose predictions would help users know what’s coming
next so that they can adjust their lifestyle and prevent future complications. In the current health
application landscape, there are thousands of apps with specific features geared towards diabetes,
this proposed feature would separate eHB from other applications.
10Table 2. Trend analysis factors for diabetes patient
Diabetes Trend Analysis Factors (Tentative)
• Glucose- from glucometer or
• Weight – BMI >30.0 or 27.0-
CGM
30.0 with a co-morbidity
Metrics • Blood pressure >130/80
• Age
• Activity level (wearable)
• Gender
• Nutrition intake (logging)
• Hemoglobin A1c w/ EAG –
• POCT Glucose
5.7-6.4 (prediabetes), 6.5-6.9
• HOMA score- Insulin to glucose
(controlled diabetes), >7.0
ratio for detecting insulin
(un-controlled diabetes)
Labs resistance
• Triglyceride to HDL ratio
• Microalbumin- increased levels
>4/1
can be early sign of diabetic
• LDL >70 mg/dL
nephropathy
• Total insulin
• Medication compliance • Right upper quadrant abdominal
• Polyuria- abnormally pain – comes and goes with
excessive urinating water intake
• Polydipsia- abnormally • Tingling or loss of sensation in
excessive thirst toes
• Polyphagia- excessive eating • Discoloration in lower
Questionnaires
from excess hunger extremities (dark spots)
• Sudden unexpected weight • Sudden skin tags- especially
loss around neck
• Sudden unexpected fatigue • Acanthosis nigricans- darkening
• Extreme sleepiness skin patches especially around
• Sudden blurry vision neck
3.3.2 Target Variables for Trend Analysis
A dummy data consisting of demographic variables and lab-related variables has been created to
develop a prototype dashboard for the eHB team. For lab-related data, an average monthly
reading for each month is calculated and data from the last 12 months is used for this purpose.
11Table 3. List of variables
Demographic Variables Lab Variables
User ID (Unique ID assigned to a user) Month of lab
Firstname Average fasting glucose (FBG)
Lastname HbA1c
Average Low-Density Lipoprotein (LDL)
Age
level
Gender Average Triglyceride (TGL) level
Education Avg systolic blood pressure (SBP)
Race Avg diastolic blood pressure (DBP)
Housing
Number of Dependents
Marital Status
Employment
Income group
Residential area (suburb/rural/urban)
Smoking (pack/ years)
Alcohol intake (drinks per day)
Activity level (sedentary/light/vigorous)
Weight (lbs.)
Height (inches)
BMI (Weight in kg/ Height in m 2)
Polyuria
Polyphagia
Blurry vision
Insulin intake
Number of medications that are currently
being taken
3.4 Health Score
The Health Score summarizes an individual's health status into one number that measures the
overall health and wellbeing of a person in real-time. There are over 18 products or services that
utilize a Health Score. Backed by algorithms, the score crunches your health data to produce a
number. Typically, the higher number, the better you are doing in terms of engaging with your
health. The proposed eHB Health Score will be a calculated number from 1 (low) to 1000 (high)
that moves up or down, depending on how your body, emotional wellbeing, or lifestyle data
change. The scoring system will give you a health score (1-1000) like a credit score to determine
12what grade your health score is. A poor health score is less than 400. A fair health score will be a
score greater than 400 but less than 550. A good health score will be a score greater than 550 but
less than 700. An excellent health score will be a score greater than 700.
When tracked over time, it offers a robust indicator of how your health and wellbeing are evolving.
Conceptually, it is based on who you are (physical health & lifestyle), how you feel (mental
wellbeing) and your environment (socio-economic factors). It is the interrelationships among
these factors that determine individual and population health (Determinants of Health | Healthy
People 2020, 2021).
Table 4. Health score features
eHB Health Score Factors
• Age • Resting heart rate
Physical • Gender • Blood pressure
Health • Chronic conditions • Blood work (lipids)
• BMI • Waist & neck circumference
• Family medical history
• Exercise
• Nutrition • Smoke
Lifestyle • Stress level • Alcohol consumption
• Compliance with medical • Illicit drug use
appointments
• How you feel about
• Anxiety
your life (positive)
Mental Well- • Energy level
• State of mindfulness
being • Depression
• Emotional distress
(interfering with daily
activities)
• Cultural and religious • Income
Socio- • Education
discrimination
Economic • Employment
• Overpopulation
Factors • Social support • Community Safety
• Family size • Overpopulation
133.4.1 Health Score Importance
With the introduction of wearable devices and smartphones, Apple Health Kit, and other health-
focused digital tools, we are closer to our health than ever before. We can know more about our
health daily than at any time in history. We have the science and the technology to get a baseline
snapshot of our health metrics. We have actionable information that can reveal a lot about our
heart, steps, sleep, and the overall activity we are producing.
Individuals benefit from having peace of mind about their health status. People take their annual
physical examinations essentially for discovering health problems but really, they want to obtain
peace of mind regarding their health status. Currently, the individual will meet with their primary
care physician (PCP) after their examination, where they will receive insight into their results. The
PCP might offer lifestyle changes, further screening, or referral to specialists. The eHB Health
Score could be able to provide reports and additional knowledge of a person’s risk of certain
chronic conditions. A person with a lower health score could be highly motivated to follow their
physician’s suggestions (Chen et al., 2016).
But what do we do with these metrics? How do we know we are getting enough exercise or that
we are in good to excellent health? What if we need to improve our heart rate, or activity to prevent
a chronic condition? We need to take these metrics and generate more data with them. We need a
Health Score.
3.4.2 Gamification of Health
Gamification is a concept that entices us to be incentivized for something, which is a great tool to
help increase client engagement, thus promote marketing and user traffic. Just as you would
receive coins, points or something of value in a video game for competing- we can borrow
gamification and apply it to our own lives. Our wearables and smartphones are all the hardware
we need to make this possible. The activity, steps, and sleep we complete, we can be rewarded for
those healthy activities. Not only will we be generating data that compiles into a Health Score, but
when we remain at optimal levels, we unlock the value of that activity and are incentivized for it.
Health is more than just fitness. If we think about the Pokemon Go craze, which began in 2015
and still holds popularity today, this was one way to get us up, walk around, encourage us to move,
and take more steps. What you received were more Pokemon characters. That may be fun but try
14trading those characters in to pay for your medical bills. The Attain® app by Aetna is another
example of an app designed to give you real ways to be healthier so you can hit goals — and earn
rewards when you do (https://www.attainbyaetna.com/).
While physical wellness, mental wellness, and medical care contribute to ones’ health, health is
comprised of so much more than “the absence of disease” (United States Department of Health &
Human Services, 2015, n.p.). In fact, research suggests medical care is only responsible for 10-
15% of preventable mortality; the remaining 85-90% is derived from social and physical
environments (Braveman & Gottlieb. et al., 2014; Sederer. et al., 2015). As a result, it is critical to
examine social determinants of health. In this edition of the Capstone e-Newsletter we do just that
by first examining the construct of social determinants of health and then exploring a new tool for
human service organizations to measure it with the Social Determinants of Health Index.
3.4.3 Social Determinants of Health Score
Healthy People 2020, which identifies objectives for improving health, defines social determinants
of health as “conditions in the environments in which people are born, live, learn, work, play,
worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and
risks” (United States Office of Disease Prevention and Health Promotion, n.d., n.p.). Social
determinants of health recognize that many factors can contribute to health beyond just health and
safety. By examining social determinants of health, we are better able to create social and physical
environments that promote the health of everyone – it is necessary for health equity. Table 5 shows
six most common socio-economic factors (American Hospital Association, 2021; Singu, S. et al.,
2020.)
Table 5. Six Most Common Socio-economic Factors
Education Employment
Relationship and Social Capital Discrimination
Housing (size of household) Income Inequality
People with disabilities, in particular, face a number of disparities and poorer outcomes compared
to non-disabled peers due to an “increased risk of exposure to socio-economic disadvantage”
(Emerson. et al., 2011). People with disabilities are more likely to live in poverty, face social
15isolation, and have trouble finding affordable, accessible housing. Ableism also impacts people
with disabilities health and outcomes (Emerson. et al., 2011).
4. Health Passport
The year 2021 is an exciting year for eHB and the launch of their highly anticipated mobile health
application. It gives consumers ownership of their data and the ability to personalize, sync and
share digitally. Among many other features, they have developed their own vaccine passport
option on their application. A vaccine passport is a document or digital record that clarifies if an
individual has been vaccinated against a specific disease (Rouw, Kates, Michaud, 2021). This is a
great feature in terms of offering vaccination information to customers, markets, and the
community usefully. Once you have registered for the app and added basic personal information
into the app, you can click on the Vaccine Passport and add any immunizations or vaccines. It will
ask for the date of the immunization/vaccine, the purpose and offers a dropdown box to specify
what it is for. After that information has been input, you have the option to generate a vaccine
passport or not. You can also upload documentation via PDF or a photo of the
immunization/vaccine. Once that is completed, it is stored as a report within the application, and
you have the opportunity to generate a QR code that can then be scanned.
The initial approach to market the vaccine passport was to seize the moment by utilizing the
availability to have a COVID-19 passport that can be used anywhere as needed. The focus was to
showcase the opportunity of easier travel capabilities, for example, domestic/international travel
and airports. Other features integrated for travelers are quarantine information, requirements for
returning after traveling, and restrictions. Some countries have considered accepting the passport
for travelers to avoid some restrictions, for example, testing or quarantining (Rouw, Kates,
Michaud, 2021). This will make it easier and more appealing to get back to traveling for those who
are required for business or for leisure. Another conversation is the possibility for vaccinations to
be required for access to sporting events, cruise lines, work (healthcare and school settings), and
various other community activities (Rouw, Kates, Michaud, 2021). It is uncertain if there will be
a mandatory vaccine mandate, but eHB is going in the right direction. The marketing approach is
to push the advantage of having the COVID passport integrated in the vaccine passport.
There is an important relationship that needs to be done between consumers and the seller/product.
Social media is another factor that needs to be addressed when thinking of a marketing approach.
16The roles media platforms currently holding on consumers are very valuable. A prior study showed
that personal and social-related ads have a higher chance of motivation and engagement (Geng et
al., 2021). Therefore, a consumer can self-relate to the product and is able to see if it is useful to
achieve their goals. Consumers strive towards personal growth and self-goal awareness, which
fuels adoption and motivation to best avoid failure (Peng et al., 2020). Santini et al. (2020)
developed a model that visualizes consumer engagement with connection, interaction, satisfaction,
retention, loyalty, advocacy, and engagement as stages in the cycle by using a matrix. They
concluded that social media gives consumers a chance to engage with products and brands while
also gaining trust and positive emotions (Sashi, 2012) better. This is a great way to get the attention
of various age groups to gain a larger following. Twitter is shown to be a platform to successfully
gain consumer engagement via satisfaction and positive emotions (Santini, 2020). Other platforms
include Facebook, Instagram, LinkedIn, Pinterest, YouTube, as well as social networking services
such as Nextdoor. The importance of marketing is to gain the consumer's trust and associate your
brand as helpful to them and their needs. The importance of consumers' emotions and trust is a
driving force to dedication and consumer engagement (Santini, 2020). Users who trust the product
tend to feel more comfortable with the product by communicating, sharing, and engaging (Geng,
et al., 2021). To accomplish more consumer engagement, it is essential to use advertising methods
with the target population's characteristics (Peng et al., 2020).
Top challenges and concerns include equity and access, access to the vaccine has been a global
issue. Within the U.S., minorities are having a harder time getting vaccinated, and due to the
potential of discrimination, travel and venues cannot make it mandatory (Rouw, Kates, Michaud,
2021). Another concern is the lack of uniform standards. There currently is a lack of digital
standards related to vaccine passports. Lastly, privacy and security are major concerns when
storing personal data on a centralized database (Rouw, Kates, Michaud, 2021). Some individuals
are for utilizing the passport while others are strictly against it. In reality, the COVID passport
needs to be seen as a way for eHB to offer something for the community as a positive movement
into the future.
Starting Spring in 2021, U.S. states have partnered with different companies promoting vaccine
passports. The NHL is planning to use Clear’s Health Pass during their playoffs to screen all
players, coaches and staff. The Clear’s Health Pass is an air travel security platform that uses a
17biometric system and can house lab test results and vaccine status. The state of Hawaii is also
working with Clear’s Health Pass as well as the Commons Project Foundation on flights to and
from Los Angeles and Hawaii and between the Hawaiian Islands in hopes to promote event
gatherings. In California, they are looking at a start-up company FaceFirst who is endorsing it as
a ‘coronavirus immunity registry’ that holds individuals COVID-19 status information of test
results (antigen or antibody) and can be used as a form of contact tracing. New York was one of
the hardest-hit states, they are teaming with IBM’s Digital Health Pass, Excelsior Pass, to use
during large event gatherings to show proof of a negative test or vaccination. The Excelsior Pass
has also stated to be flexible, built to scale and allows for other states to eventually join. Florida is
a state that has prohibited the idea of vaccine passports requirements for access into theatres,
sporting events, theme parks, and travel. These are just a few of the states that have shared their
thoughts on whether or not they will be moving forward with vaccine passports (Ada Lovelace
Institute, 2020).
As far as mega health systems, Epic Systems is a well-known system that is promoting a form of
passport that will be an extension of their current application, MyChart to be used as a way to
indicate whether the individual is clear (green), infected (red) or unknown (yellow). Each state
seems to be taking a stance on promoting the use of applications or physical cards to show their
vaccine verification (Ada Lovelace Institute, 2020).
5. Trend Analysis and Health Score Dashboards
5.1. Trend analysis dashboard
Trend analysis dashboards were created using Tableau application. An imaginary dataset
consisting of 42 users was created and used for this purpose. As specified in Table 3, the target
variables included various socio-economic factors and users’ lab results over one year. Three
different dashboards were developed that show users’ health progress over a period of time, their
health position in regard to other users, and the effect of a socio-economic factor on the users’
health. All dashboards attempted to show visual insights through a lens of socio-economic factors
over a people’s lifestyle and health-related activities and over basic vital signs (e.g., blood
pressure). Additionally, a dashboard focuses on a specific chronic disease influenced by various
18factors. Consistent monitoring such factors through dashboards can provide medical insights to
healthcare professionals, indicating the current patients’ diverse aspects of health conditions.
These dashboards would not only promote patient-centered care but de-identified data can also
be used for research purposes which can help government agencies and policymakers make
informed decisions and provide equal opportunities to every individual for healthcare access.
Figure 2. HbA1c trend analysis dashboard
19Figure 3. Social economic status vs. BMI dashboard
Figure 4. Smoking against diabetes risk factors and HbA1c dashboard
205.2 Health Score Dashboard
The health score dashboard was created from an excel file that included our proposed health score
factors from our four major categories physical health, lifestyle, mental wellbeing, and social
determinants of health. In fact, building a health score, in general, is not separable from all factors
addressed in previous sections. For instance, socio-economic and simple vital signs can be treated
as outcomes of an individual’s lifetime activities that may reflect current health conditions. We
determined that physical health and lifestyle sections were weighted more than the other two
factors. The users can earn a certain number of points in each section all adding up to their
cumulative health score.
Figure 5. Health scoring system
From this scoring algorithm, we created an imaginary data set using a random generator with a
uniform distribution to populate health scores for 250 potential users based off the algorithm.
We were able to visualize the health scores of these 250 potential users using Tableau, creating a
health score dashboard. With these visualizations, the potential users would be able to see where
their health score falls compared to other potential eHB users.
21Figure 6. Health score dashboard
6. Conclusion and Future Research
eHealth is an emerging domain that has caught the attention of the digital health and healthcare
market. The hesitation in utilizing some of these applications is the uncertainty of how to use the
data they generate and what features would really be assistive to help the user better manage their
health conditions and help support their clinical providers. This paper is to actualize those features
that would help improve their health outcomes and motivate users to take ownership in their overall
wellness. This is an opportunity to give wellness back to the consumer and give them a tool to help
guide them in managing and understanding it. The visuals provide a way for them to know how
their health stands, why it’s there, and how to manage it.
The purpose of this research is to make an impact on individual patients and their understanding
of their own health. We have identified the gaps in consumer engagement with eHealth
applications using a targeted population that could potentially see the highest benefit. In addition,
the framework was developed to do a trend analysis on diabetics as well as a health score
calculation that would best give the results of the consumers' health condition. We show how those
trends and scores can be presented to the consumer through dashboard visualizations to give them
22actionable insights. The proposed conceptual model will give away to optimize eHB’s application
and consumer audience. Furthermore, our study suggests three steps to build up detailed Go-to-
market business operating strategies: 1) identifying market environment and aspects of customers,
2) understanding and visualizing customers over tactical business targets, and 3) building up Go-
to-Market business operating strategies.
Figure 7. Suggested a Go-to-Market steps.
This paper can lead to further research in the area of eHealth applications as the proposed
framework, and the prototype models can be useful in developing high-level solutions to some of
the current challenges. While this research focused on particular chronic conditions diabetes, the
framework can be used to analyze all major chronic conditions for future work.
23References
Ada Lovelace Institute. (2020). International monitor: vaccine passports and COVID status apps.
https://www.adalovelaceinstitute.org/project/international-monitor-vaccine-passports-
covid-status-apps/.
American Hospital Association (AHA). (2021). Social Determinants of Health. Available at:
https://www.aha.org/social-determinants-health/populationcommunity-
health/community-partnerships
Bol, N., Helberger, N., & Weert, J. C. (2018). Differences in mobile health app use: A source of
new digital inequalities? The Information Society, 34(3), 183-193.
doi:10.1080/01972243.2018.1438550
Braveman, P., & Gottlieb, L. (2014). The social determinants of health: it’s time to consider the
causes of the causes. Public health reports, 129(1_suppl2), 19-31.
CDC (2021, January 12). Chronic diseases in America. Retrieved February 23, 2021, from
https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm
Chen, L., Li, X., Yang, Y., Kurniawati, H., Sheng, Q., Hu, H. and Huang, N., 2016. Personal
health indexing based on medical examinations: A data mining approach. Decision Support
Systems, 81, pp.54-65.
Chentli, F., Azzoug, S., Mahgoun, S. (2015). Diabetes Mellitus in Elderly. National Center for
Biotechnology Information. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673801/
Consumer Health IT Applications. Agency for Healthcare Research and Quality.
https://digital.ahrq.gov/key-topics/consumer-health-it-applications
Citizen Health, 2 Jan. 2020. “What's a Health Score?” citizenhealth.io/whats-a-health-score/.
Dameff C, Clay B, Longhurst CA. (2019). Personal Health Records: More Promising in the
Smartphone Era? JAMA.2019;321(4):339–340. doi:10.1001/jama.2018.20434
eHealth Briefcase. (2021). http://ehealthbriefcase.com.
24Emerson, E., Madden, R., Graham, H., Llewellyn, G., Hatton, C., & Robertson, J. (2011). The
health of disabled people and the social determinants of health. Public health, 125(3),
145-147.
Geng S., Yang P., Gao Y. & Congcong Yang Y.T.,c, (2021). The effects of ad social and
personal relevance on consumer ad engagement on social media: the moderating role of
platform trust, Computers in Human
Behavior, https://doi.org/10.1016/j.chb.2021.106834.
Georgsson M, Staggers N. Quantifying usability: an evaluation of a diabetes mHealth system on
effectiveness, efficiency, and satisfaction metrics with associated user characteristics. J
Am Med Inform. 2016;23:5–11.
Gheorghe, A.; Griffiths, U.; Murphy, A.; Legido-Quigley, H.; Lamptey, P.; Perel, P. The
economic burden of cardiovascular disease and hypertension in low- and middle-income
countries: A systematic review. BMC Public Health 2018, 18, 975.
Gold, M., Hossain, M., & Mangum, A. (2015). Consumer Engagement in Health IT:
Distinguishing Rhetoric from Reality. EGEMS (Washington, DC), 3(1), 1190.
https://doi.org/10.13063/2327-9214.1190
Gordon, N. P., & Hornbrook, M. C. (2016). Differences in access to and preferences for Using
patient portals and Other eHealth technologies based on race, ethnicity, and Age: A
database and survey study of seniors in a large health plan. Journal of Medical Internet
Research, 18(3). doi:10.2196/jmir.5105
Graetz, I., Gordon, N., Fung, V., Hamity, C., & Reed, M. E. (2016). The digital divide and
patient portals. Medical Care, 54(8), 772-779. doi:10.1097/mlr.0000000000000560
Healthypeople.gov. 2021. Determinants of Health | Healthy People 2020. [online] Available at:
[Accessed 7 May 2021].
Liew, M. S., Zhang, J., See, J., & Ong, Y. L. (2019). Usability Challenges for Health and
Wellness Mobile Apps: Mixed-Methods Study Among mHealth Experts and Consumers.
JMIR mHealth and uHealth, 7(1), e12160. https://doi.org/10.2196/12160
25Millett, G. A., Jones, A. T., Benkeser, D., Baral, S., Mercer, L., Beyrer, C., . . . Sullivan, P.
(2020). Assessing differential impacts of covid-19 on black communities.
doi:10.1101/2020.05.04.20090274
Mueller-Peltzer, M., Feuerriegel, S., Molgaard Nielsen, A., Kongsted, A., Vach, W., &
Neumann, D. (2020). Longitudinal healthcare analytics for disease management:
Empirical demonstration for low back pain. Decision Support Systems, 132, 113271.
https://doi.org/10.1016/j.dss.2020.113271
National Diabetes Statistics Report. (2020). Centers for Disease Control and Prevention.
National Diabetes Statistics Report, 2020 | CDC.
Neter, E., & Brainin, E. (2012). Ehealth literacy: Extending the digital divide to the realm of
health information. Journal of Medical Internet Research, 14(1). doi:10.2196/jmir.1619
Neuhauser, L., Kreps, G. L., Morrison, K., Athanasoulis, M., Kirienko, N., & Van Brunt, D.
(2013). Using design science and artificial intelligence to improve HEALTH
communication: ChronologyMD case example. Patient Education and Counseling, 92(2),
211-217. doi:10.1016/j.pec.2013.04.006
O’Connor, S., Hanlon, P., O’Donnell, C. A., Garcia, S., Glanville, J., & Mair, F. S. (2016).
Understanding factors affecting patient and public engagement and recruitment to digital
health interventions: A systematic review of qualitative studies. BMC Medical
Informatics and Decision Making, 16(1). doi:10.1186/s12911-016-0359-3
Peng Z., Ziwei W., Xiaotong L., Ying-Hsang L., Xingzhen Z. (2020). Understanding promotion
framing effect on purchase intention of elderly mobile app consumers, Electronic
Commerce Research and Applications, https://doi.org/10.1016/j.elerap.2020.101010.
Reiners, F., Sturm, J., Bouw, L. J., & Wouters, E. J. (2019). Sociodemographic factors
influencing the use of Ehealth in people with chronic diseases. International Journal of
Environmental Research and Public Health, 16(4), 645. doi:10.3390/ijerph16040645
Reti, S. R., Feldman, H. J., Ross, S. E., & Safran, C. (2010). Improving personal health records
for patient-centered care. Journal of the American Medical Informatics Association:
JAMIA, 17(2), 192–195. https://doi.org/10.1136/jamia.2009.000927
26Rouw, Anna & Kates, Jennifer & Michaud, Josh. (2021). Key Questions about COVID-19
Vaccine Passports and the U.S. Kaiser Family Foundation.
https://www.kff.org/coronavirus-covid-19/issue-brief/key-questions-about-covid-19-
vaccine-passports-and-the-u-s/.
Santini, Fernando & Ladeira, Wagner & Costa Pinto, Diego & Herter, Márcia & Sampaio,
Claudio & Babin, Barry. (2020). Customer engagement in social media: a framework and
meta-analysis. Journal of the Academy of Marketing Science. 48. 10.1007/s11747-020-
00731-5.
Sarwar, M. A., Kamal, N., Hamid, W., & Shah, M. A. (2018). Prediction of diabetes using
machine learning algorithms in healthcare. 2018 24th International Conference on
Automation and Computing (ICAC). doi:10.23919/iconac.2018.8748992
Sashi, C.M. (2012), "Customer engagement, buyer‐seller relationships, and social
media", Management Decision, Vol. 50 No. 2, pp. 253-
272. https://doi.org/10.1108/00251741211203551.
Senecal, C., Widmer, R. J., Bailey, K., Lerman, L. O., & Lerman, A. (2018). Usage of a digital
Health Workplace intervention based on socio-economic environment and RACE:
RETROSPECTIVE Secondary cross-sectional study. Journal of Medical Internet
Research, 20(4). doi:10.2196/jmir.8819
Settumba, S.N.; Sweeney, S.; Seeley, J.; Biraro, S.; Mutungi, G.; Munderi, P.; Grosskurth, H.;
Vassall, A. The health system burden of chronic disease care: An estimation of provider
costs of selected chronic diseases in Uganda. Trop. Med. Int. Health 2015, 20, 781–790.
Sederer, L. I. (2015). The social determinants of mental health. Psychiatric services, 67(2), 234-
235
Singu, S., Acharya, A., Challagundla, K., Byrareddy, S. (2020). Impact of Social Determinants
of Health on the Emerging COVID-19 Pandemic in the United States. Frontiers in Public
Health. doi:10.3389/fpubh.2020.00406
Terschüren, C., Mensing, M., & Mekel, O. C. (2012). Is telemonitoring an option AGAINST
shortage of physicians in rural regions? Attitude towards telemedical devices in the North
27Rhine-Westphalian health Survey, germany. BMC Health Services Research, 12(1).
doi:10.1186/1472-6963-12-95
Thin Nguyen, Mark E. Larsen, Bridianne O’Dea, Duc Thanh Nguyen, John Yearwood, Dinh
Phung, Svetha Ve. Decision Support Systems: Kernel-based features for predicting
population health indices from geocoded social media data. Volume 102, October 2017,
Pages 22-31
United States Department of Health & Human Services. (2015). Healthy people 2020: an
opportunity to address societal determinants of health in the United States. Washington,
DC: Author. Retrieved from
https://www.healthypeople.gov/sites/default/files/SocietalDeterminantsHealth.pdf
United States Office of Disease Prevention and Health Promotion. (n.d.). Social determinants of
health. Retrieved from: https://www.healthypeople.gov/2020/topics-
objectives/topic/social-determinants-of-health
Varshney, U. (2014). Mobile health: Four emerging themes of research. Decision Support
Systems, 66, 20–35. https://doi.org/10.1016/j.dss.2014.06.001
World Health Organization. Global health risks: mortality and burden of disease attributable to
selected major risks. Geneva: WHO; 2009
World Health Organization. The global burden of disease Fact Sheet No. 310. Geneva: WHO;
2012, updated June 2011.
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