Why Is Data Analytics Important in Healthcare?

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Click to learn more about author Sanjeev Agrawal.

Data Analytics is arguably the most significant revolution in healthcare in the last decade. There are several drivers for why the pace of Analytics adoption is accelerating in healthcare:

  • With the adoption of EHRs and other digital tools, much more structured and unstructured data is now available to be processed and analyzed.
  • Emergence of Machine Learning and Analytics tools that can make useful inferences and predictions based on available data.
  • The dramatically positive impact Analytics can have on the pressures health systems face to be more efficient and improve clinical outcomes.
  • Computing and Cloud infrastructure that allows for large quantities of data processing in real time, pushed to the right operational and clinical decision makers at the right time. A decade ago, ideas such as video calls with doctors, doctor-on-demand and Wi-Fi-enabled blood pressure monitors were a fantasy. Today, they are real — and mainstream. Both caregivers and consumers are adopting these tools at a rapid pace to increase efficiency, lower costs and improve patient satisfaction. And they are producing vast amounts of data — data with which traditional warehouses simply cannot keep up.
  • Leadership of health systems that is more technology-friendly, willing to adopt new tools and open to adopting new technologies. In fact, a lot of them recognize that new-world Data Analytics is no longer optional; it’s a must-have for healthcare institutions to survive.

What’s Different About Analytics in 2018?

For more than a decade, healthcare organizations invested millions of dollars building Data Warehouses and armies of data analysts – with the sole purpose of making better decisions with data and improving patient outcomes. The historical problem has been that these warehouses and Analytics alone aren’t enough because the Analytics / Reporting / Dashboard they provide are not actionable: they just tell you what’s happening, but they cannot explain why it’s happening and what one can do about making the right outcomes happen. Now instead of just understanding “what’s going on” the infrastructure and technology has come of age to figure out “why” and “what to do about it”.

Operational Efficiency Through Predictive Analytics: Let’s focus on one key area and dive a little bit deeper: enabling healthcare providers to “do more with less.” This is a critical need for hospitals to remain viable because of a number of forces:

  • An aging population is increasing the demand for expensive, critical care.
  • There is an ever-increasing shortage of qualified physicians and nurses and caregivers.
  • The move toward value-based care has increased the need for accountability and transparency.

These forces are turning out to be so strong and powerful that many healthcare organizations are being forced to consolidate, and this trend will continue. Efficiency is therefore at the top of agenda, and Data Analytics has an important seat at the table. In fact, hospitals today face the same cost and revenue pressure that retail, transportation and airlines have faced for years. As Southwest, Amazon, FedEx and UPS have demonstrated, to remain viable, industries that are asset-intensive and service-based must streamline operations and do more with less. Healthcare providers can’t keep spending their way out of trouble by investing in more and more infrastructure; instead, they must optimize their use of the assets currently in place. To do this, providers need to consistently make excellent operational decisions, as these other industries have, through Predictive Analytics that learn continually and use optimization algorithms and Artificial Intelligence to deliver prescriptive recommendations throughout the system to administrative and clinical end users.

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