Machine-Learning-Driven Engagement Campaign Drives Cardio Appointments and Revenue

Discover how Virtua Health partnered with Actium Health to boost revenue 130% and improve more patients’ heart health.

Case-Study Highlights

  • $800,000 estimated revenue generated by AI-based campaign
  • 25% were more likely to book a cardio-related appointment
  • 30.3% higher revenues from email openers vs control group
  • Enhanced visibility with machine learning from local vs national training data

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Identifying Cardio Needs

Cardiovascular diseases are the number one cause of death, yet conventional segmentation misses people who need engagement for heart services. 

For instance, outreach using “age greater than 50” naturally excludes thousands of people below 50 who are also at high risk. Other filters, such as past encounters, biomarkers, or demographic lifestyle markers, compound the problem. 

Healthcare needs are too complex to be represented in simple slices and data filters. 

AI/Machine Learning Approach

With these challenges in play, the marketing leaders at Virtua Health — an award-winning comprehensive community healthcare system comprising five hospitals and 270+ locations in South New Jersey and Philadelphia — knew that driving access demanded a far more nuanced approach. 

cardio campaign

< Age 50

thousands of patients are at risk

The Solution

Virtua Health selected Actium for their machine learning approach because of the ability to use data science to identify patients in need and report on the results. Also, Virtua Health had previously seen impressive results when partnering with Actium to identify and engage patients at high risk for breast cancer. Virtua Health’s breast cancer screening campaign impacted the lives of more than 1,300 patients, and the health system was optimistic that Actium could help drive similar results for cardiology patients. 

Rather than manually filtering and segmenting, machine learning models observe characteristics across hundreds of thousands, or even millions of patients.

Using volumes of local health system data to train allowed the model to pick up on nuances specific to Virtua Health and the area. These nuances help identify people in need of services, and they won’t surface in with generic, national datasets or manual filters.

Results – Campaign Performance

Virtua Health used the model in an email campaign with approx. 133,000 recipients. 

To measure the campaign’s effectiveness, we used a Randomized Control Trial (RCT) approach. This means that of the 130,000+ people in the campaign, a random 7,000 were in a holdout group. The holdout group would not receive the campaign communication during the study. Using this method will allow us to understand, given the same audience, the percent of patients that would come in for an appointment without outreach vs those with outreach. The remaining people in the holdout group would receive the communication after it was proven effective. 

We developed the communications in tandem with Virtua Health and one of their design partners. This is often the case with AI driven campaigns, where a data-oriented partner can help inform the creative process based on demographics and other factors in the target audience.

cardio campaign


Impact on appointments



Click rate vs industry average 2.49%



Higher revenues from email openers vs control group

“The science behind Actium’s approach enabled us to increase the number of cardiology appointments booked and drive revenue. More importantly, we were able to positively impact the lives of patients with elevated health risks.”

VP of Marketing, Virtua Health

What Do These Results Mean?

We’ve shown how Virtua Health reached people in need using AI. Without Virtua’s AI-based outreach, over 4,000 of these people would not have scheduled appointments and been seen. 

This is important above and beyond calls for driving volume and revenue. Among these patients are diagnoses of major illnesses. There are early warnings for preventive measures that can save lives. 

Manually slicing and dicing segments of the population is no longer enough. This true personalization requires nuance AND scale that AI offers. We simply can’t do this by hand.


estimated revenue growth

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