Tools and Strategies for Visualizing Health Data

View all blog posts under Articles | View all blog posts under HIM

Two physicians discuss visual health data displayed as a graph on a computer screen.Health care professionals tap into complex data when making important decisions about patient care and resources. What’s the best way to communicate this vital information to colleagues and the general public? Increasingly, health informatics professionals share their findings through data visualization. Visualizing health data allows experts to present key trends and information via graphs, charts, and other visuals that show as well as tell.

Visualizing health data is a powerful way to share urgent health information swiftly and effectively. This guide highlights the major tools and strategies.

What Is Health Data Visualization?

Let’s consider what health data visualization encompasses. How does it benefit health organizations?

Health Data Visualization’s Purpose

Through fields such as health data analysis and health informatics, medical organizations accrue large amounts of raw data. Examples of such data include patient diagnosis, outcome and length of stay.

How can data analysts meet challenges in streamlining processes, better allocating resources or improving patient care? Visualizing health data offers a solution. Medical organizations can synthesize raw data and convert it into graphics, charts and dashboards. Visually displaying health data points helps identify trends and significant “data” clusters.

What’s more, it provides a way to share important findings with stakeholders who may not be data literate, such as hospital executives and administrators. Health data visualization can illuminate the general public and alert them to important developments in a crisis.

The Process of Visualizing Health Data

Today’s health organizations use a number of data collection points. For example, when doctors and nurses treat patients or conduct follow-up visits, they enter patient-specific data into electronic health records (EHRs).

As information is collected, data analysts use machine learning, artificial intelligence (AI) and other technical tools to export data and create visual presentations. We’ll highlight some specific tools later in this guide.

Health Data Types

Health data takes many forms. Sometimes data is specific to the patient experience, such as trends in hospital readmission, or responsiveness to a certain medication or therapy. Alternatively, some data collection is specific to a disease; we’ve seen this in the collection of data regarding coronavirus transmission. Community health data is also important, enabling public health experts to chart local trends in diabetes, cancer or infectious diseases.

Presenting Health Data Visualizations

Health data visualizations are helpful because they condense complex data into more straightforward presentations. Potential applications include the following:

  • Presentations to a hospital board of directors, showing evidence for why certain service lines need additional funding
  • Public health presentations, providing communities with information about environmental health concerns
  • One-on-one applications between providers and their patients, allowing doctors to visually display the patient’s long-term blood pressure or cholesterol trends

Additional Resources

For additional insight into the process of visualizing health data, consider these resources:

Health Data Visualization: Tools and Strategies

As health data visualization becomes integral to medical and public health organizations, more tools become available.

Tools for Visualizing Health Data

Data visualization often assumes the form of a “dashboard”: a set of interactive reports that allow decision-makers to quickly review metrics or scan trends. Several types of dashboards may be used in health care organizations:

  • Operational dashboards. Operational dashboards show real-time data about what’s happening in the hospital. For example, they may display current information about hospital admissions that can be reviewed throughout the day.
  • Strategic dashboards. Strategic dashboards show trends and changes over time. For example, a strategic dashboard allows hospital executives to review month-over-month changes in patient length of stay.
  • Analytical dashboard. Analytical dashboards provide tools for extrapolating conclusions from broad datasets. One application is calling out specific trends from a large collection of patient medical records.

Types of Visualization Designs

Pictorial representation of data offers many options. Data analysts choose different visualization designs based on the kind of information they’re exhibiting, as well as the intended audience. The most common types of visualization designs include the following:

  • Infographics and mini-infographics
  • Charts
  • Tables
  • Maps
  • Timelines
  • Scatter plots

More About Data Sourcing

To create visualizations, the critical first step is cultivating the right kinds of data. A data source can be anything that produces digital information, which can then be exported into a report. For additional information about data sourcing, consider the following resources:

Additional Information About Data Model Creation

Data modeling is an important part of the analytic process. In this field, analysts determine the structure and organization for storing data in a database. A data model functions as a kind of blueprint, or floor plan, showing where different pieces of information are stored in relation to one another. Efficiency data modeling makes it easier to efficiently visualize data.

Learn more by consulting the following resources:

Additional Information About Data Preparation

Data preparation means ensuring that raw data is ready to use in a visualization model. This involves validating the data, checking for accuracy, and reviewing outliers. To learn more about data preparation, check out these links:

Health Data Visualization and Other Trends

Health data visualization is increasingly a major focus in health analytics. Its rise in prominence coincides with several other significant technological trends.

Cloud Computing

As more medical organizations store data in cloud servers, clinicians and team members can more easily access data from connected devices. For example, a clinician in the treatment room of the hospital can use a tablet to access a specific patient’s health records. A data analyst in another part of the hospital can access those same records and study them for use in data visualization. Learn more about the value in cloud computing through HealthTech.

Predictive Analytics

Predictive data analysis uses historical data to predict future outcomes, often via machine learning algorithms, which can more efficiently evaluate large bodies of raw data. For example, predictive analytics can consider trends from past flu seasons to help predict the severity of an upcoming flu season.

Strategy and Integration

Being able to compare different types of data makes it easier for providers to offer robust, patient-specific treatment plans. For example, a provider can view a patient’s medical history, and then compare it against trends and trajectories with other patients who have similar conditions. Data visualization efficiently reviews multiple data points and applies insights gained to make informed decisions about patient care.

Data Visualization Creates New Possibilities in Health Care

Clinicians, hospital administrators, and public health officials have long turned to data to drive key decisions. Through data visualization, health informatics, and health information management professionals can play a crucial role in offering information and decision support. Visualizing health data opens significant avenues to educate and empower doctors, executives, and the public at large.