Healthcare Data Experience: The new realm of data in healthcare

In the contemporary healthcare environment, data now drives the healthcare system. Data is just the raw material and the healthcare system need tools and a strategy to get the most out of it.

A healthcare data experience is one where the user needs and wants of data comes to them, rather than them having to find it or search for it on their own.

The healthcare system needs tools and platforms that offers relevant insights into what that data suggests and how it can be used to drive better healthcare and business processes. This experience should also be ‘’intelligent’’ and should be able “infuse” this intelligence into the healthcare and business processes.

The key characteristics of a quality data experience

The ability of data to find the user:

Healthcare professionals should be able to get the data that is most relevant to them as easily as possible. Additionally, this process has to be automated and intelligent in order to improve user experience and save time.

The ability to infuse data insights into healthcare processes:

Intelligent insights help guide the healthcare users and provide needed context to making better clinical decisions. Providing intelligent insights is also different from normal analytics.

The capacity to narrate data with integrated data reports in the form of data story:

An intelligent data experience goes beyond populating a dashboard or spreadsheet with data and actually helps to contextualize clinical insights in real medical language. The narrative element of an intelligent experience is crucial for ensuring a wide range of healthcare professionals can benefit from the data, and that the insights are fully understood.

The ability to deliver descriptive, diagnostic, predictive and prescriptive analytics:

The healthcare professional should be able to acquire the knowledge of what happened, why it happened, what will happen, and what they should do about it. This holistic experience is essential for the provision of an intelligent data experience.

Data experience categories

Modern BI and analytics:

This category serve up real-time relevant reports and dashboards that start as a starting point for more in-depth analysis. The value of these tools lies in their capacity to provide easy access to trustworthy data to allow healthcare professionals to make better, data-driven decisions.

Integrated insights:

This category provides contextual insights from data and infuse relevant information into the tools and products that are already in use. This enhances the user experience of these tools making them more effective. These tools enable everyone user in the healthcare system to make data-enabled decisions.

Data -driven workflows:

The tools in this category have the capacity to provide operational insights by super-charging operational workflows with complete and near-real time data. They add additional value by saving time and money by putting a healthcare organizations’ data to work in every point of the healthcare work flow.

Custom applications:

The tools in this category provide intentional and intelligent data experience. These tools are mostly purpose-built tool to deliver data in an experience customized to a particular condition or specialty. They build the exact experience healthcare professionals need, making them more effective and more efficient.

4- stages of Data experience model maturity

• Data operationalization
• Data relevancy
• Data and result personalization
• Intelligence and seamless experience

Conclusion

Data experience is way above conventional data analysis and learning. The current scenario of data experience is a combination of activity, learning, behavior, data performance and intelligence. It should also be understood that creating data experience is a process and involves understanding the users, context and the possibilities of available data.

About the author: Arunakiry Natarajan holds a Master’s in Medical Informatics from Technische Hochschule Deggendorf (THD) in Germany. Additionally, he holds a Master’s in Dentistry with specialization in Oral Medicine and Radiology and certifications in the field of Data Science. Following a career in academics and in clinical dentistry, he moved into healthcare data science. He followed this career with an immense belief that apt use of health data, analytics, automation, and responsible AI can spark a digital metamorphosis in health systems across the global healthcare ecosystem. Currently, he is working at management4health as a Project Manager and a Digital Health Data Specialist. He is also a webinar and workshop provider in clinical decision support systems for the students of THD.