Data Visualization in Healthcare: Mission-Critical or an Unnecessary Splurge?

Our previous article was dedicated to predictive analytics in healthcare, where we discussed how it helps to move the industry forward and which underwater rocks may occur on the way. In the current blog post, we’d like to touch upon a quite related topic — data presentation and its meaning for the medical world.

Continue reading to learn about the importance of data visualization in healthcare and how to build a reliable and well-balanced dashboard that would help medical facilities streamline their daily operations and execute first-class patient care.

Data Visualization in Healthcare in Action

To some, the way you present data may seem to be something unserious and unworthy of such close attention. However, let’s consider some healthcare data visualization examples that will showcase the fallacy of such a way of thinking.

Information reflecting bed occupancy rates in city clinics and hospital locations to assess the distance from the fire and the number of medical staff on shifts — all these metrics are critical for decision-making. But when every second counts, will you have time to open numerous Excel reports on each clinic in the city? The answer lies on the surface — no.

To make a swift and data-based decision, the critical information must be quickly accessible, and this can be resolved by visualization.

Sure thing, considering each result separately, we have no opportunity to see the overall picture and suspect that something went wrong. Visualization will help to detect such errors as improper data preparation and processing, wrong algorithm selection, or medical equipment malfunctions.

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Using visualizations, doctors have critical information on a patient right before their eyes, bypassing the need to open numerous laboratory and examination results in the system separately. As a result, it saves both medics’ and patients’ time. Also, when prescribing treatments, medical staff can monitor their effectiveness and promptly adjust if a patient’s vitals get worse.

Clinical data visualization can help medical facilities resolve these issues. Having a comprehensive view of staff workload, hospital admission rate, and inventory availability empowers clinics to improve staff performance. They can also adjust medications and reagents procurement plans and create their budget more thoughtfully.

Tools for Dashboard Building: What the Choice Depends On

In the previous section, we considered several healthcare data visualization examples and use cases that can be addressed with the right approach. But bear in mind that high-quality clinical data visualization is impossible without specific tools.

Of course, the choice depends on our aims and a range of factors: software capabilities, integrations, and limitations. Below, let’s consider some of the most popular healthcare data visualization tools that can be used for the resolution of related tasks.

Which ones are most suitable if we speak about data visualization in healthcare? Here, we need to consider the capabilities of robust data analysis, carrying out some ETL activities, the ability to handle massive datasets, customizability, and possibilities to build predictions. And also, the type of medical facility or organization for which we need a health data visualization solution.

For example, the first three tools listed above have quite an expanded pool of capabilities; they are highly customizable and suitable for, let’s say, predictive analytics. This may be relevant for hospitals, clinics, ambulances, and laboratory networks. It’s also applicable to health insurance companies and multidisciplinary private and public medical centers with large patient flow.

What if it’s a single dental office, a small private specialized medical center, or a tiny laboratory taking tests on a single indicator? Not sure that such advanced tools will be of critical need.

Looker Studio, a convenient and intuitive visualization platform, for example, doesn’t support predictive analytics and is not suitable for building highly customizable dashboards. But are these features really critical for such small medical facilities? The question is quite rhetorical.

Healthcare Dashboard UX: Minor Issue or Pain in the Back?

It appears that a healthcare dashboard should be functional and effectively present the information we intend to view. However, what about usability, design concept, and other UX-related aspects? Are they crucial when it comes to medical data visualization, or are they not worth much attention? Let’s try to find out.

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Remember our very first example with an ambulance dispatcher who needs to distribute injured people among clinics? The dispatcher on duty must have crucial info readily available, minimizing the need for additional effort to locate necessary information when every second is vital.

For example, we have a dashboard containing information on a city hospital. The key metrics that are crucial for us should be visible immediately — such as the number of available and occupied beds, admissions or discharges per day, and average doctor and patient ratio.

Of course, this is all high-level information that doesn’t reflect the full picture. We can obtain more detailed information with the drill-down principle by clicking on the metrics. Therefore, we can access data for different departments of the clinic, statistics, graphs, forecasts for the future, and much more. The point here is that the dashboard must be concise and intuitive, with the elements placed in a logical way to bring maximum value.

Read more about Healthcare UI/UX

Dashboard Reactive Behavior: Yes or No?

Visual analytics in healthcare is often dynamic and requires data updates over the given period. The majority of dashboards are updated manually or automatically several times a day. If we speak about healthcare organizations working with statistics or making forecasts on epidemic waves, it’s completely justified that such facilities are not in critical need of the info relevant to the current moment.

Another matter is if we speak about hospitals and clinics. In these cases, the dashboard’s reactive behavior is a real must. For example, there is a patient in intensive care in a critical condition. There are multiple sensors transmitting vital indicators to the system: heartbeat rate, saturation, body temperature, blood pressure, and more. Real-time updates and configured alerts help medical staff more carefully track patients’ conditions and take immediate measures in case of negative dynamics.

Sounds quite simple, but nobody has canceled implementation complexities and increased related costs. The issue is that to display information in real time, the software must have a specific architecture able to receive data continuously. Especially, if you have a lot of data that will be recorded every 10-30 seconds and loaded to storage, the costs for system design and its maintenance may be quite high.

Hence, it’s better to think carefully, whether a real-time healthcare dashboard is something you can’t do without. If you have a continuous data flow and your healthcare system is high-load and requires real-time updates for critical decision-making, investing in such a dashboard is indeed worthwhile in terms of costs and efforts.

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Machine Learning for Health Data Visualization

Yes, we can use machine learning for data analytics and visualization in healthcare to enrich our dataset. There are many contemporary business intelligence solutions that leverage ML. For example, Power BI’s feature, AutoML, allows even those who are not proficient in machine learning to train, validate, and invoke ML models directly in the tool.

Let’s imagine, we have a table with patients’ comments on their treatment satisfaction. With ML, we can insert an additional column with numbers reflecting the positivity rate of this or that comment from 0 to 10. AutoML possesses a set of pre-trained models that we can use to read the reviews and evaluate patients’ satisfaction with provided healthcare services. And note, there’s no need to write even a single line of code!

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Key Types of Data Visualization to Streamline Analytics

So far, we have highlighted a couple of visualization types, such as charts and graphs, that are common for healthcare data analytics. However, there are many other effective methods for variable representation. Let’s briefly outline the key ones:

• Interactive dashboards: It is a dynamic and user-friendly environment allowing processing different data in one place. For example, you can have one interface displaying various metrics such as current hospitalization numbers, readmission cases, and death rates for prompt and reliable decision-making. Moreover, these tools typically encompass such features as zooming, filtering, and drill-down, which enhance data analysis even further.
• Infographics: When it comes to combining visuals and text to simplify the presentation of complex information, infographics can become a real helping hand. They are mostly employed to create guides and brochures for both doctors and patients to share crucial information, such as disease details, statistics, and treatment methods.
• Heat maps: These tools use color gradients to highlight different values in medical files. For example, death rates may be shown in red, recovery rates in green, and current hospitalizations in yellow. This improves the clarity of data, allowing doctors to smoothly identify patterns that might otherwise be missed.
• Time-series charts: This is a perfect tool allowing you to analyze data over a given period and gain a clear view of how metrics evolve over time. For example, the charts can assist in tracking current infection rates and hospitalization trends, and monitor changes, so you can predict possible outbreaks and prepare for them.

Balancing Benefits and Challenges in Healthcare Data Visualization

Having explored so many interesting aspects of healthcare data visualization, we understand your urge to tap into implementation.

While visualization tools offer a range of benefits, they also come with certain pitfalls worth considering for a smooth start. So, before wrapping up, we suggest taking one more look at the core advantages and exploring the key challenges you may encounter along the way.

Pros

• Elevated patient care: Let’s say, you need to assess a patient’s medical record to create an effective treatment plan. You have to review test results, diagnoses, medication history, and past treatments across multiple documents, which can be time-consuming. Visualization of all this information in one place provides a comprehensive view of the patient profile and streamlines decision-making.
• Timely disease trend detection: Consider a hospital that employed a time-series chart to track a sudden increase in infections with high hospitalization rates. This helps clinics better prepare resources to handle a predicted surge and plan vaccination efforts.
• Improved patient engagement: Let’s admit that for patients, medical information can sometimes seem like something that needs to be decoded. Visualization assists here as well. For example, doctors can explain a person’s health condition and how it changes over time using infographics, which can also simplify treatment plans by breaking them down into clear steps and timelines.
• Increased operational efficiency: Data visualization also helps hospitals track resource allocation. For example, by using heat maps, clinics can find out that the maternity ward has empty beds, while the emergency department is facing increasing hospitalization rates and requires more resources. These insights help allocate equipment and staff accordingly to meet current demand.

Cons

• Integration challenges: This is a common issue, especially among legacy systems. Being built on outdated architectures, they often lack compatibility with modern tools. Ensuring smooth connections with visualisation tools can be hard, time-consuming, and, most importantly, costly.
• Data privacy and security: In healthcare, patient data protection is a focal point. Typically, you should follow HIPAA compliance rules when collecting, using, and sharing private information. You need patients’ data for representation, but if the visualization tools you employ are weak, your patients’ sensitive medical records could be compromised. You may end up facing heavy fines and losing the trust of clients.
• Staff resistance to change: Although data visualization tools significantly streamline analytics, your staff still requires skills to leverage them effectively. And while you are ready to provide training, your team may still prefer to work with familiar tools like Excel, Google Sheets, and PowerPoint.

Concluding Lines

Health data visualization is instrumental in tackling the ongoing challenges that arise within the industry. By having critical info readily accessible, you can swiftly make data-driven decisions, promptly identify discrepancies and errors in healthcare data, and deliver exceptional patient care.

In the ideal world, dashboards are intended to simplify the lives of their users. Sometimes, though, they also make it more complicated. In the majority of cases, it happens because of incorrect visualization tools selection, poor and inconsistent design, and, of course, absence of balance between the number of desirable metrics and concise presentation manner.

Velvetech’s team is well-versed in data engineering and is proficient in healthcare software development. Drop us a line, and our seasoned engineers will be happy to assist you in every step of creating a top-notch visualization and providing expert data visualization consulting services for your healthcare facility, so you can use your data with maximum efficiency!