4 questions healthcare executives are asking about augmented analytics

Estimated reading time: 4 minutes

At our annual Impact Summit, I had the privilege to talk about augmented analytics and address questions from healthcare executives—many from payer organizations. Here are a few answers to some of the most common questions I’m asked. (You can also catch my session on-demand.)

1.    What is augmented analytics?

Augmented analytics is the use of enabling technologies—such as machine learning and AI—to support data preparation, insight generation and explanation. These capabilities help users understand and apply data more effectively in their day-to-day workflows.

Augmented analytics also enables data scientists to automate the development, management and deployment of data science, machine learning and AI models.

I like to say augmented analytics has four flavors:

1. Descriptive: What happened? [ex: patient has this issue]
2. Diagnostic: Why did it happen? [ex: these factors are influencing patient’s health status]
3. Predictive: What will happen? [ex: based on factors, behaviors and diagnosis, patient’s expected outcomes and trajectory is….]
4. Prescriptive: What should I do? [ex: these actions will most powerfully impact those outcomes and shift that trajectory]

2.    What trends are driving the adoption of augmented analytics?

There are three key trends influencing the budding interest in and adoption of augmented analytics.

First, data is steadily growing—both in volume and complexity—making manual processing insufficient. Organizations need intelligent tools to sift through data, identify patterns, and highlight critical insights.

Second, workforce shortages in the healthcare industry are driving an increased reliance on technology to improve staff retention and decrease burnout. The stress of understaffing also increases the risk of human error, which can negatively impact patient outcomes and satisfaction. Alternatively, augmented analytics can secure the quality and accuracy of data processing.

Third, health plans specifically are seeing the value in using automation to deliver higher value and superior outcomes to their members.

3.    How does augmented analytics solve key pain points for payers?

Augmented analytics has incredible potential to transform performance, but is it worth the investment for health plans? After decades of experience in the payer space, I say resoundingly yes—and here’s why.

It removes complexities. Health plans have more data than they know what to do with, and it’s typically coming from a variety of sources in a wide array of formats. Augmented analytics offers a simple way to sift through this data, organize it sensibly, and deliver actionable insights back to users.

It addresses workforce limitations. Challenges in hiring and retaining staff extend far beyond clinical settings; health plans have also been struggling to fill roles. Augmented analytics offers the tools necessary to reduce manual work for payer teams while enhancing the quality of outputs.

It looks forward. Data has long been used to look backward and understand mistakes that have already been made—but reactivity is no longer enough. To drive optimal outcomes for their members, health plans need predictive and prescriptive insights.

4.    How can you use augmented analytics to create impact?

The use cases for augmented analytics are powerful and extensive. At its core, its ease of use makes the complex simple. It can:

• Facilitate data mining and identify anomalies using algorithms
• Examine historical data and predict trends
• Automate routine processes or workflows
• Leverage rules engines to compare data and identify deviations
• Prescribe actions and recommendations
• Trigger alerts to expose areas of investigation
• Automatically annotate descriptive narratives

These applications all sound great conceptually, but how does augmented analytics actually make a difference in practice? Let’s consider an example from one of our own clients.

At ConcertoCare, predictive analytics enabled stronger population health management by stratifying the patient population based on risk factors and assessing cost and utilization patterns to match the right intervention to the most in-need patients. As a result, ConcertoCare was able to proactively fulfill care needs for the most complex 5% of its population and rising risk in up to 20% of its population, reducing ER visits by 16% and readmissions by 40%. You can read their full story here.

Overall, health plans who implement augmented analytics can anticipate decreasing ER visits by up to 30%, lowering readmissions by up to 40%, reducing unnecessary admissions by up to 45%, and shrinking PMPM costs by up to 56%.

Creating measurable impact by using augmented analytics is not only possible but necessary for organizations today. Let’s continue this conversation. Let me know what questions you have on this topic over on LinkedIn.