9 Data Visualization Trends and Tools for Health Data (2024)

Data Visualization is the most enthusiastic subject among businesses nowadays. Today, companies are sitting on mountains of data with a goldmine of knowledge and information. The information, once properly comprehended, can be exceptionally useful for them to make better, data-driven decisions.

There are several data visualization trends, and as their value increases, the number of companies turning to the art of data visualization will keep growing.

The processes of data visualization equally influence the healthcare industry. Data visualization can represent data and information in visual forms such as graphs, charts and diagrams.

In the healthcare setting, professionals turn to data visualization to communicate patient information and other vital resources. This enables healthcare professionals to display critical information in graphs, charts, pictorials and other visuals. This system passes all the data and is processed quickly and efficiently.

This article will cover data visualization trends in the healthcare industry.

Top 9 Data Visualization Trends

Keep reading if you want to understand some data visualization trends to look out for in the coming years.

1. Cloud Computing

Cloud computing provides two-fold benefits to the healthcare industry. It has proved to be helpful for both healthcare providers and patients alike. Cloud computing has proven to be effective in the corporate sector for reducing operating costs while enabling providers to deliver high-quality, personalized treatment.

Patients increasingly used to the instant delivery of services can now take advantage of the same promptness from the health sector. By allowing them to manage their health records, Cloud amplifies patient interaction with their health plans, resulting in better patient results.

It further breaks down location barriers and provides remote accessibility for improved performance and experience. During the prevention, treatment, and recovery processes, cloud-based telehealth systems and applications facilitate easy sharing of health data, enhance accessibility, and provide patients with healthcare coverage.

Having the patient’s data in the cloud also facilitates interoperability between the pharmaceuticals, insurance, and payments of the different segments of the healthcare industry.

This facilitates the smooth transfer of data between the various stakeholders, thus speeding up the delivery of healthcare and introducing quality in the process.

Cloud computing enhances efficiencies with rapidly emerging technologies such as Big Data analytics, artificial intelligence, and the medical internet. It opens up multiple opportunities to streamline the delivery of healthcare. It improves the availability of services, enhances interoperability, and lowers costs.

2. Predictive Analysis

To make predictions or the unknown, predictive analytics is the method of learning from historical data. Predictive analytics would allow the right choices for health care, enabling care to be customized to each person.

Predictive analytics approximates the likelihood of a prediction based on observations in historical data rather than only providing information from past events to an end-user, a significant step forward for many personalized health organizations.

This helps physicians, financial analysts, and administrative staff get a “head up” on future situations before they happen and make decisions on how to proceed forward-thinking.

In emergency treatment, surgery, and intensive care, the importance of predictive modeling in healthcare can easily be observed. A patient’s outcome is directly linked to the care provider’s rapid response and acute decision-making when or if the condition takes an unexpected turn.

Leveraging predictive analytics will alter the dynamics of power between patients and doctors. For fear of losing decision-making power or liability issues, if doctors do not readily implement predictive analytics, patients will go straight to the algorithms to find their answers. But the accuracy and reliability of these recommendations must be trusted by consumers.

3. Strategy And Integration

Strategy and integration compare various types of data and create and deliver comprehensive, patient-specific care plans simpler for the care providers. A provider may, for example, display a patient’s medical history and compare it with other patients with similar conditions against the projected patterns and trajectories.

The reviews of the data visualization effectively check and verify various data points and apply information gained to improve patient care decisions.

4. Data Viz is More Social

With the growing social media trend in the past decade, data researchers have shifted to making data visualization more social, presentable, visually appealing, and easily understandable.

This trend is becoming increasingly popular, and researchers are adopting it pretty quickly, using instances like 3D animations, GIFs, and Youtube Stories.

5. Data Democratization

With the introduction of new data analysis platforms, the data that was once considered difficult to understand and required input from scientists and technical teams have completely changed with the introduction of no-code analysis platforms.

These platforms not only process the data but also show it in an easy-to-understand manner. This trend will not only help teams make data-driven decisions without any technical inputs but also increase data usage in decision-making.

6. Data Storytelling

Storytelling is among the most important and integral parts of the business process. It makes the customer feel connected and helps build a relationship with them.

The idea of visualizers becoming storytellers was out a few years ago, but in the past year, it has gained much traction. Data visualization is more of storytelling nowadays and is widely adopted by many organizations.

7. Data Journalism is Going Mainstream

It can’t be argued that data in charts, diagrams, and dashboards are easy to showcase and explain compared to long paragraphs and reports.

Over the years, this trend has taken over the top media organizations, and more and more of them are using this summarized data to show it to their viewers as it takes less space and is easy to explain and understand. It is already very popular in the mainstream media and will gain more popularity in the coming years.

8. Artificial Intelligence

Since the introduction of AI in the data visualization industry, not only has there been a transformation in how we look at data, but also the ease with which data can be consumed.

Big companies have massive data that can be overwhelming, and AI solves this problem by helping them discover what data they should be looking at. Over the coming years, more and more data will be available, and AI will make it easy to get a sense of the data.

9. Mobile-Friendly Data

Mobile devices need no introduction, as we have used them for almost all our tasks. Since mobile experiences matter most for businesses and their customers, engaging in providing mobile-friendly data will be among the key trends in the coming years.

Many data visualization enterprises have already incorporated features in their tools that can be used on mobile devices, and more and more organizations are adopting this trend rapidly.

Tools For Visualizing Health Data

Data visualization also takes the form of a “dashboard,” which can be understood as a series of interactive reports that enable decision-makers to analyze metrics easily or scan patterns. Several types of dashboards can be used in healthcare organizations. Some of these are briefly explained below.

🔹Operational Dashboards

Just as the name suggests, an operational dashboard is designed to display the everyday, routine operations of the hospital. It is a monitoring tool used to track the hospital’s constantly evolving processes and monitor the current performance of key metrics.

The data is updated regularly, sometimes minute-by-minute, making the working very quick and efficient.

It gives out real-time information about whatever is happening in the hospital at the current moment. All this information and data regarding routine operations can be seen and reviewed. From base-level information to hospital administration, all the data and information can be checked and reviewed throughout the day.

🔹Strategic Dashboards

A strategic dashboard is a monitoring tool commonly used by executives to track the status of main performance indicators. The data of the strategic dashboard is also updated regularly but at less regular intervals than an organizational dashboard.

Just as operational dashboards are used for routine activities, strategic dashboards are created to see and review trends and changes in the key indicators.

It also provides collective information for quick and easy viewing and reviewing. For instance, a strategic dashboard enables healthcare professionals to review various changes over the months. This can be viewed through different strategies and events.

🔹Analytical Dashboard

An analytical dashboard is a reporting tool that analyzes large volumes of information to enable users to examine trends, predict results, and discover insights. Within business intelligence tools, analytical dashboards are more common because they are typically developed and designed by data analysts. The analytical dashboards work for analysis and bringing similar information together.

It includes various data visualization tools for extrapolating or concluding the findings from a large dataset. The analytical dashboard is a mix of various tools and helps further review and gather information. This dashboard is efficient enough to comprehend and understand relevant patterns from a wide set of patient medical records This makes things quick and easy and reduces efforts.

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Curious about how data visualization trends are influencing tools like Power BI? Discover more about the impact of these trends on practical data visualization in our article on Data Visualization with Power BI.

Frequently Asked Questions

How does data science contribute to artificial intelligence (AI) development?

Data science provides the foundation for AI by using algorithms and statistical models to analyze and learn from data. Machine learning, a subset of AI, relies heavily on data science techniques for training models.

How does cloud computing impact data visualization trends in healthcare?

Cloud computing in healthcare enhances accessibility, interoperability, and efficiency. It allows for the quick sharing of health data, facilitates remote accessibility, and contributes to streamlined healthcare delivery by leveraging technologies like Big Data analytics and AI.

What role does predictive analysis play in healthcare data visualization?

Predictive analysis in healthcare allows for making informed decisions based on historical data. It helps in anticipating outcomes, enabling personalized care and rapid response in emergency situations.

What is the best data visualization for healthcare?

The ideal tool for visualizing healthcare data varies depending on the objectives and needs. Nonetheless, GIS, interactive dashboards, and heatmaps are well-liked and practical tools. These tools support trend analysis, geographic disparity visualization, and patient data analysis. Solutions for complicated requirements can be customized with specialized software like Tableau, Power BI, and QlikView. In the end, the optimal tool fits user requirements, data infrastructure, and the capacity to convert complicated data into useful insights for improved patient care and decision-making.

How do I choose the right visualization for my data?

Choose the best visualization based on your target audience, desired insights, and data type (numerical, temporal, or categorical). For example, scatter plots and heatmaps illustrate correlations, pie/bar charts show proportions, and line graphs track trends. Remember that simplicity is often more effective in communicating ideas than complexity. Try out different kinds of visualization, but keep readability and clarity as top priority. Numerous options are available to match your data and goals with tools like Tableau, Power BI, and Matplotlib in Python.