HABIT is a healthiness trend measuring visualization dashboard. HABIT integrates over platforms capturing coherent data and has the capability to provide weightage to various health criterions. Based on the weightages assigned to the criterions show visualization patterns depicting increase and decrease in healthiness trends. HABIT allows this weightage to be saved as standard benchmarks as per various geographies. Based on the set benchmark organizations can visualize healthiness trends through comparative charts.

1. Health Actionable Behavior based Information Technology
Challenges
As the intervention of technology in healthcare practices is becoming stronger, we have data in various
forms available. Data is growing and moving faster than healthcare organizations can consume it; 80% of
medical data is unstructured and is clinically relevant. This data resides in multiple places like individual
EMRs, lab and imaging systems, physician notes, medical correspondence, claims, CRM systems and
finance. Getting access to this valuable data and factoring it into clinical and advanced analytics is critical
to improving care and outcomes, incentivizing the right behavior and driving efficiencies. After more than
20 years of steady increases, health-care expenses now represent 17.6 percent of GDP-nearly $600 billion
more than the expected benchmark for a nation of the United States’s size and wealth.
The challenge lies in building medical systems that can share data with each other, document data from
health fairs, combining it with data collected by wearable devices and mobile health applications; for
providing deep insights in healthiness trends for particular areas. In current times the data collected by
mobile health applications, wearable devices, EHR systems and other medical measuring devices is not
coherent and thus it is difficult to have a consistent cross-sectional view at a particular point of time or
for an individual/entity.
Public health agencies have difficulties in analyzing data from these diverse sources leading to challenges
in forming actionable insights which need aggregated data insights. At more granular level corporates,
communities, universities and other organizations that focuses on improving health of people associated
with them have challenges in identifying success of their campaigns due to lack of aggregated view of
data.
The Pathways in action:
Some health-care leaders have already captured value from big data by focusing on the concepts outlined
in our pathways or have set the groundwork for doing so. Consider a few examples:
 Kaiser Permanente has fully implemented a new computer system, HealthConnect, to ensure data
exchange across all medical facilities and promote the use of electronic health records. The
integrated system has improved outcomes in cardiovascular disease and achieved an estimated
$1 billion in savings from reduced office visits and lab tests.
 Blue Shield of California, in partnership with NantHealth, is improving health-care delivery and
patient outcomes by developing an integrated technology system that will allow doctors,
hospitals, and health plans to deliver evidence-based care that is more coordinated and
personalized. This will help improve performance in a number of areas, including prevention and
care coordination.
 The Validic Ecosystem is comprised of healthcare companies – such as payers, providers, health
information technology platforms, wellness companies, health clubs, consultants, hospital
systems and pharmaceutical companies – as well as digital health technologies, including clinical
and remote-monitoring devices, sensors and wearables, fitness equipment, and patient
healthcare applications. Clinical researchers and universities are also contributing to and
benefiting from this ecosystem.

2. Opportunity
As per Health Research Initiative survey 28% of consumers said they have a healthcare, wellness, or
medical app on their mobile device, up from 16% last year. Nearly 66% of physicians would prescribe an
app to help patients manage chronic diseases such as diabetes. Also 79% of physicians and close to 50%
of consumers believe using mobile devices can help physician better coordinate care. As per PWC survey,
through wearable devices 46 % believe it will lower obesity rates, 42 % believe the athletic ability will
improve, 21 % of US consumers own a wearable product. Data suggest wearables combined with
geospatial data and analytic tools enables improved person and population health management. It can
also be an opportunity to create social analytics across health departments and other public stakeholders.
Our Solutions
HABIT is a healthiness trend measuring visualization dashboard. HABIT integrates over platforms
capturing coherent data and has the capability to provide weightage to various health criterions. Based
on the weightages assigned to the criterions show visualization patterns depicting increase and decrease
in healthiness trends. HABIT allows this weightage to be saved as standard benchmarks as per various
geographies. Based on the set benchmark organizations can visualize healthiness trends through
comparative charts. Our system will provide insights on nutrition, glucometers and BG apps, biometrics,
sleep trackers, tobacco cessation, medication adherence with more categories to be added in the future.
Key components
HABIT consists of three key components in the system which helps to overcome the above mentioned
challenges and provide insights over coherent data. The three key components are as follows:
1. Benchmarking healthiness parameters as per geographies:
Figure 1. Benchmarking criteria’s

3. As shown in figure 1. In the map view, users can see which area is most problematic in terms of healthy
life style in the map view. The darker area represents states with worse life style. The calculation of life
style score is customizable in the ranking view below. By adjusting the weights of each column, such as
fitness, routine, nutrition, sleep, weight and diabetes. When the users change the importance of each
factor, the recalculated healthy life score is reflected in the map view. When the users want to explore
specific area, they can select an area of interest to explore the raw data from the area. In this view, the
user can examine the various data by selecting different data dimension for X or Y axis. This is to help
prevent developing false decision based on the peculiarity of data.
2. Habit explorer as per benchmarks:
Figure 2. Habit explorer as per benchmarks
As shown in figure 2. User can analyze interaction of various variables with each other and by selecting
the appropriate filters can view the correlation and pattern amongst them.

4. Figure 3. Habit explorer as per benchmarks (3 parameters)
As shown in figure 3. User can analyze interaction of three variables with each other and by selecting the
appropriate filters can view the correlation and pattern amongst them.
Figure 4. Mapping visualization of health trends
The visualization map shown in figure 4.above helps to visualize changes in healthiness trends in real-
time as per data coming from various health applications, wearable devices, and EHR systems. This data
will be categorized into various colors based on the healthiness trends benchmark set for that area. By
clicking on any of the dots the details for that instant can be seen. The map visualization can be changed
in various ways for better analysis purpose.
D3.js (http://d3js.org) javascript was used to build web based visualization. To build dynamic ranked list,
lineup.js (http://caleydo.org/projects/lineup/) was used. We slightly modified the original open source
code to include a map showing resulting new ranking. For the Validic data explorer, gatherplot
(http://www.gatherplot.org ) was used to visualize both categorical and numerical variables.
References:
 Big Data Has the Potential to Transform Health Care - Stefan's Blog. (n.d.). Retrieved November
28, 2014, from http://www.datameer.com/ceoblog/big-data-potential-transform-health-care/
 Big data. (n.d.). Retrieved November 28, 2014, from http://www-
01.ibm.com/software/data/bigdata/industry-healthcare.html
 Kayyali, B., Knott, D., & Van Kuiken, S. (2013, April 1). Retrieved November 28, 2014, from
http://www.mckinsey.com/insights/health_systems_and_services/the_big-
data_revolution_in_us_health_care
 Health wearables: Early days. (n.d.). Retrieved November 28, 2014, from
http://www.pwc.se/sv_SE/se/halso-sjukvard/assets/health-wearables-early-days.pdf