This document describes the development of a healthcare dashboard to analyze heart failure (HF) readmission rates. The dashboard visualizes data on HF readmission interventions like early physician follow-up and a patient portal. It aims to identify factors influencing readmissions and determine which interventions are most effective. The dashboard displays data through graphs, charts and traffic light indicators. It was designed for various users like physicians, nurses and administrators. The goal is for the dashboard to help decision-making, guide resource allocation and lower HF readmission risks through data-driven insights.

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Healthcare Dashboard: Heart Failure Readmission Analysis
University of Colorado School of Nursing
Healthcare Dashboard: Heart Failure Readmission Analysis
With the emergence of electronic health records (EHRs), come immense quantities of
invaluable clinical data or Big Data. The questions are: how do we collect this data, how do we
interpret this data, and how will we utilize this data? One such solution is a learning healthcare

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system that will enable “practice-based evidence to the point of care where its application will
mean improved quality and patient outcomes” (Mason & Barton, 2013). We are in need of a
healthcare system that is able to “assess the risks and benefits of treatment options and learn
from previous experiences and outcomes in determining beneficial options,” hence a learning
healthcare system (Mason & Barton, 2013). So it is imperative that our healthcare decisions
involve meaningful and real time data. Accumulating data and providing efficient visualization
and display of this information through dashboards can support “meaningful use” and ensure that
all recipients and respondents will understand the potential and current problems at hand
(Friedman, Wong, & Blumenthal, 2010). Data visualization is an invaluable way to portray
visual representations of data to understand, interpret, and communicate data. It is the “intuitive
representation of large, complex data sets with multiple dimensions and measures” (Sharda,
Delen, & Turban, 2015). Dashboards can be utilized to display consolidated information onto a
single screen “so that information can be digested at a single glance and easily drilled in and
further explored” (Sharda et al., 2015). They are an important tool in improving the quality of
healthcare as they can discover correlations and outline factors that are related to certain health
advents. One such example would be a healthcare dashboard on specific interventions in
preventing hospital readmissions for heart failure (HF).
In our example, a healthcare dashboard is used to not only identify the potential root
causes of HF readmissions, but to provide solutions. Decision-making will be influenced and
mirrored by the visualization of our data. It is imperative to measure which efforts are working to
reduce readmission, as well as what needs to be changed and improved upon. We have proposed
the following interventions regarding follow-up care after a HF hospitalization which include,
early follow-up office appointment with a physician or scheduled telephone appointment with a
HF nurse within seven days and introduction to a patient portal which utilizes secure messaging
to allow for ongoing monitoring of daily weights, home blood pressures, symptoms, and access
to the patient’s updated medications and dosages. The true gift of the dashboard will lie in the
ability to pinpoint and focus on which interventions are best at improving HF readmission rates.
This will then influence the organization on where time and resources need to be directed.
Financial partners will have access to HF readmission rates after the specific interventions have
been implemented in the hopes to match the goals for the Centers for Medicare and Medicaid
Services (CMS) reimbursement. Once the results have shown that our target goals have been
met, reimbursement from CMS will help finance the ongoing efforts of our HF interventions.
Trends in HF readmission rates will be followed closely to aid decision makers in identifying
ongoing impedances and what continues to work. Beyond these data driven decisions, the ability
to visualize readmission rates at 30 day and 90 day intervals through our dashboard will only
continue to validate our interventions.
Our HF dashboard has displayed data clearly and without distraction in a way that is
clearly understood upon first glance. Transparency of the displayed information is important and
requires minimal training. We have made our healthcare definitions clear, consistent, and
unambiguous (Fuchs, 2010). Our dashboard shows whether a particular number is good or bad,
or if the trend is heading in the right direction as we have utilized visual objects, such as traffic
lights to set the evaluative context (Sharda et al). The traffic lights help us drive our user’s
attention to where performance is either exceeding expectations or falling short (Fuchs, 2010).
Some examples of this include seeing comparable performance benchmarks, monitoring progress
and hitting targets for Key Performance Indicators (KPIs). Furthermore, trend detections allow
the user to quickly see comparative results within the data. We chose to use bar graphs as they

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are useful when a picture of the data makes meaningful relationships visible (i.e., patterns,
trends, and exceptions), which could not easily be discernable from a table. Bar graphs also
allow comparison of percentages (Few, 2007). They are recommended for proportions, value
comparisons and trend detections, all of which are included in our dashboard (Pieczkiewicz,
2015). The use of bar graphs helps identify relationships between variables and generate real
time trend reports. Being mindful of task ordering, we designed our dashboard with the intention
of our users being able to interpret the data quickly. We also kept in mind that certain displays
have different variations of cognitive burden for certain tasks (Pieczkiewicz, 2015). The types of
displays that have been utilized in our dashboard have been influenced by our intended audience
and users.
“Different people have different visual literacy” (Pieczkiewicz, 2015). As stated
previously, you will notice an assortment of different visual literary tools to display HF metrics
including bar graphs and traffic lights in a carefully chosen color scheme. This format is used to
meet the needs of our many intended users which include: physicians, nurses, clinical
informatics, finance, quality improvement, operations analytics, senior administration and end
users/patients. For example, senior administration, nurses and finance will find a line graph,
which displays a decreasing trend of HF readmission rates occurring in relations to patients
utilizing the patient portal helpful in resource allocation. A patient who logs into the portal can
relay information or more importantly, prevent their own readmission due to easily visualizing
trends in weight gain or decreased activity. Lastly, physicians can adjust treatment plans easily
and efficiently in real time if new interventions prove to be a success in preventing readmission.
While developing the dashboard, we also took into account the use of qualitative versus
quantitative data. For example, the use of colors in trends and speedometers help present our
qualitative data while some of the more detailed number focused data is used for quantitative
data. This differentiation helps direct the audience to the appropriate areas of the dashboard in
yet another way. These are just a few examples of how our dashboard has been developed to
meet the needs of our many intended users. Both patients and physicians will be able to see
within our dashboard speedometer how satisfied they are in the use of a patient portal in
preventing HF readmissions. In order for successful implementation of our work, we will heed to
the advice of Gabriel Fuchs and design a change implementation plan, “simply because the
dashboard is such a hip tool. Any new solution needs marketing, which is where the change-
management aspects are important.”
Our HF dashboard encompasses the following three layers of information: monitoring
which is composed of our graphical data to monitor our key interventions; analysis or our
compiled data to analyze the possible root causes of HR readmissions; and management to
identify what actions are needed to resolve HF readmissions (Sharda et al). We have been able to
take data and not only create a beautiful visualization or illumination of information, but also
create knowledge and resolution of problems such as lowering the risk of HF readmissions.
References
Few, S. (2007, April). Save the Pies for Dessert. Visual Business Intelligence Newsletter.

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Friedman, C.P., A.K. Wong, and D. Blumenthal, Achieving a Nationwide Learning Health
System. Science Translational Medicine, 2010. 2(57): p. 57cm29.
Fuchs, G. (2010). Dashboard Best Practices: An in-depth look at the best practice standards in
dashboards within business intelligence applications. McLean, VA: LogiXML, Inc.
Mason, A.R. and A.J. Barton, The Emergence of a Learning Healthcare System. Clinical Nurse
Specialist, 2013. 27(1): p. 7-9.
Pieczkiewicz, D. S. (2015, April 30). Visualization Webinar by Dr Pieczkiewicz-part 2.
Retrieved July 15, 2015, from
https://www.youtube.com/watch?v=3Fb9aJRKbxA&feature=youtu.be
Sharda, R., Delen, D., & Turban, E. (2015). Business Intelligence and Analytics (10th ed.).
Essex, England: Pearson.

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Figure 1. Trend summary of 30-day HF readmissions after the implementation of the patient portal and early follow-up

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Figure 2. Analysis of HF readmissions for the month of March and patient satisfaction of the portal

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Figure 3. Analysis of how well the interventions for the month of March are working and physician satisfaction with the portal