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Patient Retention Through Coronary Artery Calcification Scoring

Coronary Artery Calcification, Outcomes, Patient Management, ROI
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Artificial Intelligence (AI) plays a noteworthy role in healthcare. For those who are familiar with healthcare AI, they may see the technology primarily as an assistant to radiologists, flagging suspected positive findings for a host of pathologies, dependent on whatever point solutions are running at a given health system. Though such a function is far from trivial, an enterprise-wide approach to healthcare AI requires its impact to reach well beyond the radiologist’s workstation. 

Through the lens of enterprise-wide AI, we’re able to find downstream benefits in other specialties, including for cardiovascular departments. More specifically – AI is paying dividends via patient retention in coronary artery calcification (CAC) scoring. 

AI for CAC

Emergent cardiovascular diseases strain hospital resources, but one of the most useful, non-invasive tools in predicting the likelihood of coronary artery disease – CAC scoring delivered through chest CT – is frequently not leveraged effectively for proactive disease management. 

AI Can Maximize Patient Lifetime Value

When you apply the figures above to the number of chest CTs performed annually at your facility, it brings to light how many patients may be overlooked. Below we outline the difference of an emergency department with and without AI denoting incidental CAC findings:

Without AI

  • Chest CT is ordered for suspected rib fracture after an auto accident. While CAC is visible on the image, it’s not documented in the report because it is incidental.
  • The patient is discharged and told to follow-up with their primary care provider, but that provider only has access to what was documented in the report.

With AI

  • With an image-based solution, all available AI is applied to visible anatomy, assessing the severity of the CAC and automatically noting it in the radiology report.
  • An enterprise-wide AI solution has access to radiologist reports, scans, medication and scheduling history to flag patients that may have previously been treated for a coronary artery disease (CAD) or the hallmarks of suspected, untreated CAD. Based on the institutional workflow, this alert can go to primary care, a cardiology group and/or the patient to ensure these findings receive follow-up. 

What a New Model of AI CAC Care Could Mean at Your Facility

With AI, the personal vigilance of health care providers and patients becomes automated, and so does the potential for retaining that patient for clinically-appropriate interventions, whether advanced imaging, medication management or procedures.

1 Okwuosa, T. M., Greenland, P., Ning, H., Liu, K., Bild, D. E., Burke, G. L., Eng, J., & Lloyd-Jones, D. M. (2011). Distribution of Coronary
Artery Calcium Scores by Framingham 10-Year Risk Strata in the MESA (Multi-Ethnic Study of Atherosclerosis). Journal of the American
College of Cardiology, 57(18), 1838–1845. https://doi.org/10.1016/j.jacc.2010.11.053

2 Williams, K. A., Kim, J., & Holohan, K. M. (2013). Frequency of unrecognized, unreported, or underreported coronary artery and
cardiovascular calcification on noncardiac chest CT. Journal of Cardiovascular Computed Tomography, 7(3), 167–172. https://doi.
org/10.1016/j.jcct.2013.05.003

3 Aidoc customer data on file.

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