Improving healthcare decision-making with analytics

Photo courtesy of Mahesh Kambala

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Data is one of the most important assets an organization can have, and in industries such as healthcare, it can even save lives. But what happens when you have too much data? While it might sound like a good problem to have, healthcare organizations are struggling to keep up with the vast amounts of data they collect. To make matters worse, not all data is created equal. There is in fact such a thing as bad data, and in healthcare, bad data can lead to poor decisions and negative patient outcomes.

The answer to this deluge of data is analytics — the practice of sorting, interpreting, and presenting this information in easy-to-digest formats. Through data analytics, experts like Mahesh Kambala can transform a mountain of confusing data into actionable insights that leadership can use to make life-saving decisions.

Discover how Kambala and other industry leaders are using analytics to weed out bad data, make sense of good data, and solve healthcare problems on a global scale.

Big data: A blessing and a curse

Healthcare organizations can use data analytics to dramatically improve patient outcomes and internal workflows. A few key benefits of data analytics in healthcare include:

• Healthcare cost reductions
• Minimized medical errors
• Improved diagnostics and predictions
• Better health indicators
• Enhanced patient experiences

But if they want to achieve these goals, healthcare providers must make proper data collection and analytics a priority for their organization. Thankfully, there is no shortage of data in healthcare — with a staggering 137 terabytes of data produced by the average hospital every day, a total of 30% of the world’s data is generated by the healthcare industry alone.

However, this raises two questions: 

• What are you supposed to do with all this data?
• How do you identify the data that’s actually useful?

If these questions go unanswered, healthcare organizations can face significant and expensive consequences. For example, poor data quality can cost organizations an average of $15 million every year. 

This is where data analytics comes in.

How data analytics and AI turn data into insights

Data analysis is the process of applying statistics, logic, and inductive reasoning to raw data. The goal is to describe the data, illustrate trends, reveal useful information, and support more informed decision-making.

Data analysts accomplish this process with a few different methods, including:

• Data Cleaning: Correcting, deleting, or updating data so that it’s accurate
• Data Aggregation: Using techniques like SQL to query data and show specific information
• Statistical Analysis: Applying advanced mathematical functions to reveal patterns and trends
• Data Visualization: Transforming the data into meaningful dashboards that quickly tell a story
• Predictive Modeling: Devising an algorithm that can predict future situations with some accuracy

Perhaps the most important part of an analyst’s job is data visualization. After all, business leaders don’t usually have the skill set (and very rarely the time) to interpret raw data, but a clear visualization speaks volumes.

And thanks to advancements in technology like artificial intelligence, these processes are becoming faster and even more accurate. This is especially true when it comes to predictive analytics — computers can process terabytes of information and apply machine learning algorithms to vast datasets, generating predictions in a matter of minutes.

To further show how analytics can benefit healthcare, let’s take a look at one of the industry’s experts: Mahesh Kambala.

Leveraging expertise to transform healthcare decision-making

Mahesh Kambala is a Senior Lead Engineer for a major health insurance company, where he works to help millions of Americans access more affordable care. He’s also an award-winning data expert, lauded for his efforts in enabling better healthcare decisions during the COVID-19 pandemic.

Kambala is a shining example of how data can save lives. As the world descended into a public health crisis, he led a team in the development of data dashboards that provided real-time insights into the health of those infected with the coronavirus. It’s this work that earned him a 2024 Global Recognition Award and an IT Professional of the Year award. But he remains humble, saying that he is most inspired by “technology’s potential to improve human lives.”

Kambala uses analytics to solve real-world problems every day, and he is increasingly turning to emerging technologies like AI to make his work even more effective. For example, he cites automation as one of the best ways he’s found to overcome two of the biggest hurdles an analyst faces: resources and timelines. 

While AI tends to raise questions of ethics and legality, Kambala believes it’s a force to be used for good. His journey, he says, is to “transform the latest technological advancements into consumable use cases for society. I am particularly fascinated by recent advancements in AI and ML and want to contribute to AI applications and safety.”

As a final note to those who shy away from AI and disparage its place in society, he says, “Technology will serve but never replace humanity.”

The future of healthcare analytics

As analytics and technologies like AI continue to become more sophisticated, healthcare providers can expect to see even more significant benefits. With better data comes better decision-making, ideally translating to better patient outcomes and lower healthcare costs. With improved artificial intelligence comes the possibility to predict diseases such as cancer and vastly decrease mortality rates.

Of course, organizations must still deal with the rising cost and complexity of collecting, storing, and interpreting immense amounts of data — and it doesn’t look like these problems are going away anytime soon. But when lives are on the line, these challenges are well worth overcoming by any means necessary.