How Data Contextualization Makes a Difference in Healthcare

The healthcare industry faces a multitude of challenges in handling the vast amounts of information it works with. From patient data to clinicians’ performance to health stats of different populations, healthcare is awash in information that is critical to the industry. So how do all the different stakeholders in the industry make sense of this information? That’s where data contextualization comes in. Understanding and identifying correlations, patterns, and trends in data is the link between data gathering/analysis and actionable insights.

While the opportunities for the healthcare industry to leverage the power of data contextualization are limitless, in this article, we’ll dive into three examples of how this approach can make a difference in healthcare – from solving problems to uncovering new trends and patterns.

What is data contextualization? 

Before we dive into examples of data contextualization, it’s important to understand what it is. Contextualization means adding related information to big data to make it more understandable and useful. When context is added to data, it can be easier to get more value from what might otherwise be incomprehensible or confusing information.

For example, you may have information on how many patients saw a particular doctor. That information is all well and good but what does it mean? Context such as the time period, the doctor’s speciality, and more, can make that information meaningful and show correlations. If we know that the doctor being seen is a cardiologist and they saw an increase in patients, then perhaps there’s a health crisis among a certain community that requires action. Without that context, it’s nearly impossible to take action and make well-informed decisions.

Navigating a worldwide crisis

Probably the best and most recent example of the healthcare industry leveraging the power of data contextualization is the COVID-19 pandemic. The crisis is unprecedented and took the world by surprise. With few previous situations to draw from, healthcare experts have relied on real-time data to educate themselves and guide the public.

Many governments around the world quickly deployed various ways of collecting community information on COVID exposure, transmission rates, symptom progression, and much more. From websites to apps to dashboards, the healthcare industry rapidly collected huge amounts of information. But, the industry also knew that it would need context to understand the data and act quickly.

For example, doctors were able to better understand why different spikes in COVID lasted longer than previous outbreaks. This has been possible by understanding how long a patient is infectious even after their symptoms are gone and they’re feeling better. By identifying and understanding the correlation between the infectious period and transmission rates, experts were able to warn the public and establish guidelines such as 14-day quarantines and social distancing to minimize the spread.

Preventing and treating chronic diseases

The healthcare industry has long used data analytics and contextualization to help identify, detect, and treat chronic diseases. These data models have spotlighted possible risks that could trigger someone’s chances of developing a chronic disease in the future. With so many potential causes that can contribute to the start of chronic illnesses, contextualization is critical to understand what might be more of a cause than another, and how to prevent it.

The healthcare industry has a lot of information that can tell them about spikes or changes in the development of disease but establishing trends can shine a light on how to treat or prevent it. If data is showing a spike in type 2 diabetes in a particular population, it’s important to understand how things like diet or lifestyle play a role. Identifying that relationship can help healthcare providers give patients better care by advising healthier eating and an exercise regimen.

Big data analytics companies are also creating AI and machine learning solutions that can provide comprehensive factors that could uncover diseases prevalent in the youth. For example, for substance abuse, a model was created to identify triggering factors, allowing policy planners and interventionists to create programs that could help reduce the risk of substance use disorders.

Using technology to see the whole picture of well-being

It used to be that healthcare providers had sporadic and inconsistent interactions with patients; a yearly exam here or intermittent episodes there were all they got. But this leaves providers with an incomplete picture of a patient’s total health over time. With the advent of technology such as fitness trackers and wearables, providers can benefit from a continuous stream of information over time, allowing for better monitoring, prevention, and interventions.

These platforms also offer the ability to put health data and care into the context of daily life, adding key insights for fuller and more personalized interpretation of well being and vital signs, and factors such as work/life balance, physical and social activity, and other elements of daily life. Through an understanding of what this continuous information is showing, providers can reward or discourage specific behavior or routines, stimulate behavior change, or intervene when a behavior is deteriorating in real-time.

The bottom line: data contextualization can help the healthcare industry navigate challenges

These examples are just the tip of the iceberg of what’s possible with data contextualization. And with continuous innovations in digital technology, those opportunities will only grow. Data is important, but without the ability – and tools – to understand the patterns, trends, and correlations in that data, it’s useless. The healthcare industry is just one space where contextualization is making a real difference.

If your business is looking to create actionable insights from your data, reach out to Gemini today. We can help you transform data and analytics and enable you to easily and intuitively interact with data using storytelling visualization. It’s all about simplifying and making it easier to see, understand, and communicate the complex – so people can learn faster and do their jobs better.

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